• Editorial Board +
• For Contributors +
• Journal Search +
Journal Search Engine
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.21 No.1 pp.20-42
DOI : https://doi.org/10.7232/iems.2022.21.1.020

# Sustainable Assessment for Biomass Power Plant Location during the COVID-19 Pandemic

Athakorn Kengpol*, Piya Rontlaong, Kalle Elfvengren
Advanced Industrial Engineering Management Systems Research Center, Department of Industrial Engineering, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, Thailand
Department of Industrial Technology, Bansomdejchaopraya Rajabhat University, Thailand
School of Engineering Science, LUT University, Finland
*Corresponding Author, E-mail: athakorn@kmutnb.ac.th
June 4, 2020 October 19, 2020 January 10, 2022

## ABSTRACT

Renewable energy plays an important role in energy sustainability and environmental friendliness, for example in terms of biomass power, and is essential for generating electric power, which has a significant impact on sustainable energy for the future. The objective of this research, therefore, is to propose a sustainable method of assessment for biomass power plant site selection for electric power supply for field hospitals during the COVID-19 pandemic in Thailand. In this research, based upon overlay map layers as an empirical study, the pandemic, as well as environmental friendliness, are considered in Thailand. The Fuzzy Analytic Hierarchy Process (FAHP) has been applied in order to determine the weighting of the criteria. Furthermore, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) has also been applied in order to rank alternative locations. The contribution of this research lies in the development of an approach that is flexible in terms of sustainable assessment, in terms of both subjective and objective evaluation measures, in order to reduce bias in the decision-making process, as well as to include natural disaster assessment in providing guidelines for the location selection of a biomass power plant from the perspective of environment friendliness.

## 1. INTRODUCTION

Electrical power is vital to the economic and industrial expansion of the world. Hence, there is growing demand for electricity. According to the current disaster situation, including natural disasters and pandemic hazards have occurred in the present, especially in 2020. The COVID-19 pandemic has resulted in an infection of a large number of people around the world. According to a recent report, there are approximately 228 million people infected with COVID-19 (World Health Organization Coronavirus Disease (COVID-19) Dashboard, 2021). As a result of the COVID-19 pandemic, there have been a number of infected people. Consequently, hospitals have been unable to service patients sufficiently. For this reason, field hospitals should be constructed in order to support the increasing number of patients. However, if the field hospital is located far away from the source of a power supply, electric power would be required in order to operate the hospital’s activities and functions, such as medical equipment, lighting systems, and all utility systems. Therefore, the present study discusses appropriate locations for biomass power plants to generate and distribute electric power to remote field hospitals for their operations.

Finding a location to produce electric power to be used to support a field hospital in the incidence of a disaster during the COVID-19 pandemic should consider the suitability of the area and the environmental impact caused by electricity production. Currently, the production of electricity from fossil fuels, such as oil, and natural gas and coal, causes environmental impacts due to the release of greenhouse gases (CO2) into the atmosphere. Thus, this also results in climate change, which is a major global problem that is related to energy, the economy, and the environment and technology (Zheng et al., 2019).

According to the International Energy Agency (IEA, 2018), global energy-related CO2 emissions has increased by 1.7% and reached 33.1 Gt CO2, the highest level ever recorded. In addition, this rate of increase was the highest since 2013, as shown in Figure 1; it is 70% higher than the average rise since the year 2010. Numerous obvious climate changes have been caused by these high CO2 emissions throughout the world, resulting in a large number of natural disasters, which impact the daily life of millions of people (Girard et al., 2019).

The industry that produces the most greenhouse gas (GHG) emissions in the world is the electricity generation industry (Khan, 2019). Reducing GHG emissions by using renewable energy to produce electricity instead of fossil fuels is an important guideline for sustainable electricity generation and environmentally friendly production. For this reason, the use of renewable energy instead of fossil fuel for electricity production can result in a reduction of GHG emissions. At present, renewable energy is crucial for generating electricity for many activities around the globe, because one of the advantages of electricity obtained using renewable energy is that it can reduce greenhouse gas emissions into the atmosphere, which has been asserted to be a cause of global warming.

A survey of the renewable energy used for global electricity generation showed that in 2018, the use of various types of sources for renewable power generation installed throughout the world continued to change. Hydropower dropped to less than 48% by the end of the year and thus now accounts for below half of the total capacity for renewable power production. Furthermore, the use of wind power has increased and now accounts for approximately 25% of the capacity to generate renewable energy that has been installed, and for the first time, solar PV surpassed 20%. In total, the use of renewable energy has increased to more than 33% of the total capacity for power generation that has been installed in the world, as shown in Figure 1 (Renewables Global Status Report, 2019).

Especially in Thailand, renewable energy is encouraged by the government following the Power Development Plan 2018 (PDP, 2018) for producing electrical energy from renewable energy, one of which is biomass energy. Based upon the Alternative Energy Development Plan for 2015-2036, the target of electricity production from renewable energy for the type of biomass in 2036 according to the AEDP 2015 plan is 5,570 megawatts (Alternative Energy Development Plan, 2015). Biomass is an abundant resource throughout Thailand, such as sugar cane, cassava, corn, etc., because Thailand is an agricultural country. The selection of the most appropriate location is a very important task for biomass power plants because an improper location can negatively affect the cost, environment, and productivity in generating electricity systems. Previous research has discussed various methods for site selection, for example, GIS (Shi et al., 2008;Sánchez-García et al., 2017;Brahma et al., 2016;Sharma et al., 2017;Perpiña et al., 2012;Höhn et al., 2014;Janke, 2010;Brewer et al., 2015;Fernandez-Jimenez et al., 2015;Vlachopoulou et al., 2001;Venier and Yabar, 2017, the AHP (Ozdemir and Sahin, 2018;Aras et al., 2003;Korpela and Tuominen, 1996), the FAHP (Chou et al., 2007), integrated GIS and AHP (Perpiña et al., 2012;Vlachopoulou et al., 2001;Al Garni and Awasthi, 2017;Uyan, 2013;Aly et al., 2017;Wang et al., 2009;Neissi et al., 2020;Majid and Mir, 2021), integrated GIS and Fuzzy AHP (Demir et al., 2021), integrated GIS, AHP, and TOPSIS (Sánchez-Lozano et al., 2013;Ramya and Devadas, 2019;Gil-García et al., 2022), integrated GIS, AHP, and VIKOR (Eren and Katanalp, 2022), integrated FAHP and TOPSIS (Choudhary and Shankar, 2012), integrated GIS, FAHP and TOPSIS (Erbaş et al., 2018), and the Genetic Algorithm (Shi et al., 2019).

The contribution of this research lies in the development of an approach that is flexible in terms of sustainably assessing both subjective and objective evaluation measures. Subjective assessment is an evaluation made based upon the individual’s personal opinion and work experience. In this research, subjective assessment was adopted based upon experts’ experience related to the electricity agencies of Thailand in terms of rating the weights of the decision criteria. However, subjective assessment may result in unreliable scores. Therefore, objective assessment should be used in conjunction with subjective assessment. Objective assessment is based upon principles, logic, and reasoning rather than personal feelings. The experts’ opinions, comprised of qualitative data, were translated into quantitative data as the same benchmark in order that it could be assessed and measured according to the same standard and criteria. Consequently, in order to minimize bias in decision-making, both subjective and objective assessments should be applied (Kilincci and Onal, 2011) in order to strengthen the reliability of the assessment, so that decision-making can be made with the same standards.

The objective of this research is to propose a sustainable assessment for biomass power plant site selection for electric power supply to the field hospital in the incidence of a disaster during the COVID-19 pandemic. The layer maps affecting the locations of the biomass power plants, such as forests, buildings, slopes, aquifers, earthquakes, geology, roads, on-ground water sources, and grid connections, are assessed throughout the empirical study. Moreover, the FAHP has been applied to determine the weighting of the criteria. Furthermore, the FTOPSIS has also been applied to rank the alternative locations based upon overall performance.

This research is organized into 6 sections as follows. Section 2 illustrates the background of the methodology used in this research and some of the relevant literature. Section 3 describes the process of assessing the maps involved in location selection. Section 4 provides an illustrative empirical study area in the northeastern part of Thailand. Section 5 discusses the implementation. Finally, section 6 presents the conclusions and recommendations of this research.

## 2. LITERATURE REVIEW

The studies of Alzahrani et al. (2020), Fanelli and Piazza (2020), Hernandez-Matamoros et al. (2020), Sahai et al. (2020), and Sarkar et al. (2020) predicted that the number of infected people would increase, so the hospitals would be unable to support the patients. Thus, the field hospital is set up in the affected area to support the increasing number of patients. Electric power is also needed for the field hospital to conduct its activities. Nevertheless, since the field hospital in the case study is situated in a remote area that is far from the source of electric power distribution, it is necessary to find a location for generating electric power to be utilized in the field hospital. Therefore, the selection of an appropriate location for a power plant is significant. The related research for the selection of the location that would be appropriate is shown in Table 1. In previous research on location selection, some of the literature is revealed in which GIS and multi-criteria decision-making tools, such as the AHP, the FAHP, and TOPSIS, are used to solve the location selection problem as applied for renewable energy.

Based upon detailed surveys, site selection using GIS-AHP has existed for some years; however, there is a dearth of research that can sustainably assess both the subjective and objective evaluation measures, including the implications of disaster during the COVID-19 pandemic for transitioning to sustainability remain to be seen (Sarkis et al., 2020) in order to reduce bias in the decision-making process, as well as choosing the most appropriate site from a large number of biomass power plant alternative sites.

### 2.1.1 Geographic Information System

GIS has been created by combining the varied disciplines of mathematics, geography, computer science, surveying, mapping, statistics, and management (Bhatta, 2008). Its overlay operations are its advantage. For geographic data processing, the ability to overlay multiple data layers in a vertical manner is the most needed and normal procedure. Actually, the necessity for overlaying vector data layers is why a topological data structure is used nowadays. Polygon overlay has become the most prevalent geo-processing tool as well as the source of any functional GIS software package because of the establishment of the concepts of mathematical topology (Jensen, 1996). A spatial operation that overlays one polygon on another to create new polygon coverage is a vector polygon overlay. In order to obtain new data connections in the output coverage, the spatial positions of each polygon set are combined (ESRI, 1992a;ESRI, 1992b).

### 2.1.2 Fuzzy Analytical Hierarchy Process

For solving multi-criteria decision-making issues, the systematic AHP procedure is used due to its quantitative and qualitative capabilities, providing a suitable hierarchical process to manage the reliability of the assessment measures and options favored by decision-makers in order to minimize bias in the decision-making process, and using both subjective and objective assessment measures (Kilincci and Onal, 2011). There are some drawbacks to the AHP despite its accuracy. For example, it fails to fully show the stances and thinking models of people. Further, the decision-maker may face inconsistency during meditation, which could cause miscalculations in the numbers (Kahraman et al., 2003). Therefore, the fuzzy theory has been used to aid in the decision-making steps in order to manage any issues occurring during the analysis of the criteria. The fuzzy set theory, along with the AHP in an abstruse context, is used by the FAHP, concentrating on the pairwise comparison approach. Instead of just the scoring method for assessment, a fuzzy data set is employed (Celik et al., 2009). The fuzzy theory can be used to help in the decision-making process as well as to settle issues arising during the criteria analysis process when a decision-maker is faced with any conflict occurring while thinking about prioritization of the fixed number. In present studies, the FAHP method is applied to use determine the weights of the criteria, as seen for example in the work of Ram and Chandna (2018).

The steps for calculating the simple fuzzy, which can be given using by Chang (1996) extent analysis, is as follows:

• Step 1: The value of the fuzzy synthetic extent concerning the ith object is expressed as

$S i = ∑ j = 1 m M g i j ⊗ [ ∑ i = 1 n ∑ j = 1 m M g i j ] − 1$
(1)

where all of the $M g i j ( j = 1 , 2 , ... , m )$ are triangular fuzzy numbers (TFN), and $⊗$ represents the expanded multiplication of two fuzzy numbers.

In order to define a fuzzy event signified as (l, m, u), we take into account a triangular fuzzy number. The parameters l, m, and u represent the minimum potential value, the value with the most potential, and the biggest potential value for a fuzzy event, respectively. In order to find $∑ j = 1 m M g i j$, execute the fuzzy addition operation of m extent evaluation values for a specific matrix so that:

$∑ j = 1 m M g i j = ( ∑ j = 1 m l j , ∑ j = 1 m m j , ∑ l = 1 m u j )$
(2)

To find $[ ∑ i = 1 n ∑ j = 1 m M g i j ] − 1$, execute the fuzzy addition operation of $M g i j ( j = 1 , 2 , … , m )$ values so that:

$∑ i = 1 n ∑ j = 1 m M g i j = ( ∑ i = 1 n l i , ∑ i = 1 n m i , ∑ i = 1 n u i )$
(3)

and then calculate the inverse of the vector in Equation (2), so that:

$[ ∑ i = 1 n ∑ j = 1 m M g i j ] − 1 = ( 1 ∑ i = 1 n u i , 1 ∑ i = 1 n m i , 1 ∑ i = 1 n l i )$
(4)

Step 2: As $M 1 = ( l 1 , m 1 , u 1 ) and M 2 = ( l 2 , m 2 , u 2 )$ are two triangular fuzzy numbers, the degree of possibility of $M 2 = ( l 2 , m 2 , u 2 )$M1 = (ll, m1, u1) is expressed as:

$V ( M 2 ≥ M 1 ) = sup y ≥ x ⌊ min ( μ M 1 ( x ) , μ M 2 ( y ) ) ⌋$
(5)

where $μ M 1 ( x )$ and $μ M 2 ( y )$ are the membership function of M1, and M2, and x and y are the values on the axis of membership function for each criterion, which can be expressed equally as follows:

$V ( M 2 ≥ M 1 ) = h g t ( M 1 ∩ M 2 ) = μ M 2 ( d ) = { 1 , i f m 2 ≥ m 1 , 0 , i f l 1 ≥ u 2 , l 1 − u 2 ( m 2 − u 2 ) − ( m 1 − l 1 ) , o t h e r w i s e }$
(6)

where d is the ordinate of the maximum intersection point D between $μ M 1$ and $μ M 2$ shown in Figure 2. In order to equate M1 and M2, both the values of $V ( M 1 ≥ M 2 )$ and $V ( M 2 ≥ M 1 )$ are needed.

• Step 3: The degree of probability for a rounded fuzzy number to be higher than a k curved fuzzy number $M i ( i = 1 , 2 , ... , k )$ can be expressed as:

$V ( M ≥ M 1 , M 2 , … , M k ) = V ( M ≥ M 1 ) a n d ( M ≥ M 2 ) a n d … a n d ( M ≥ M k ) = min V ( M ≥ M i ) , i = 1 , 2 , … , k .$
(7)

The least degree of possibility $d / ( A i )$ can be presumed to be determined by:

$d ′ ( A i ) = min V ( S i ≥ S j )$
(8)

for $k = 1 , 2 , … , n ; k ≠ i$. Then the weight vector $( W / )$ is specified as:

$W ′ = ( d ′ ( A 1 ) , d ′ ( A 2 ) , … , d ′ ( A n ) ) T$
(9)

where $A i ( i = 1 , 2 , ... , n )$ are n elements.

• Step 4: Through standardization, the regulated weight vector (W) is

$W = ( d ( A 1 ) , d ( A 2 ) , … , d ( A n ) ) T$
(10)

where W is a non-fuzzy number offering precedence to the weight of an aspect or one option over another.

### 2.1.3 The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution

For various reasons, TOPSIS was used to select the most suitable method. TOPSIS is a Multi-Criteria Decision Analysis (MCDA) approach typically employed for ordering and/or choosing options. Since it is used to choose the best functioning model overall with the longest distance from the worst values of the peer group and the one with the shortest distance from the best executors of each measure, its logic is the easiest to understand (Sureeyatanapas et al., 2018). TOPSIS was developed by Hwang and Yoon (1981) and is a multiple criteria method to identify solutions from a finite set of alternatives (Gumus, 2008). The principle of TOPSIS is to choose the solution that is nearest to the best and farthest with the worst solution (Jahanshahloo et al., 2006). The report of TOPSIS shown in the findings of the assessment displays the index of each option. The best solution for decision-making in the particular situation comes from the maximum value. TOPSIS supposes that we have m options and n criteria while we have the value for each option concerning every criterion. Individual decisions are signified using crisp values in the conventional construction of TOPSIS. In previous studies, the TOPSIS method is used to determine the ranking of alternative, as seen for example in the work of Koohathongsumrit and Meethom (2021). Still, the human preference model is undefined in various real-world cases. Decision-makers could be afraid of or unable to appoint crisp values to comparison decisions. Triangular fuzzy numbers are employed in this research for FTOPSIS. A triangular fuzzy number is innately easy for decision-makers to use and estimate, which is why it is so commonly used (Dağdeviren et al., 2009).

The following steps can be used to explain the FTOPSIS process (Dağdeviren et al., 2009;Önüt and Soner, 2008):

• Step 1: Select the linguistic values ($x ˜ i j , i = 1 , 2 , ... , n , J = 1 , 2 , ... , J$) for the options concerning the criteria. The fuzzy linguistic score ($x ˜ i j$) maintains the property that the ranges of regulated triangular fuzzy numbers are affiliated with [0,1], so there is no need for standardization.

• Step 2: Determine the subjective standardized fuzzy decision matrix.

• Step 3: Find the positive-ideal (A*) and negative-ideal (A-) solutions. The fuzzy positive-ideal solution (FPIS, A*) and the fuzzy negative-ideal solution (FNIS, A-) can be expressed as follows:

$A * { v ˜ 1 * , v ˜ 2 * , … , v ˜ i * } = { ( max j y i j | i ∈ I ′ ) , × ( min j v i j | i ∈ I ″ ) } , i = 1 , 2 , … , n j = 1 , 2 , … , J$
(11)

$A − { v ˜ 1 − , v ˜ 2 − , … , v ˜ i − } = { ( min j v i j | i ∈ I ′ ) , × ( max j v i j | i ∈ I ″ ) } , i = 1 , 2 , … , n j = 1 , 2 , … , J$
(12)

where $I '$ is linked to the benefit criteria and $I ' '$ is linked to the cost criteria.

• Step 4: Compute the distance of each option from A* and A- using the following:

$D j * = ∑ j = 1 n d ( v ˜ i j , v ˜ i * ) j = 1 , 2 , … , J$
(13)

$D j − = ∑ j = 1 n d ( v ˜ i j , v ˜ i − ) j = 1 , 2 , … , J$
(14)

• Step 6: Sort the options using the CCj index value in order to establish the most suitable solution. After that, select the shortest distance for the fuzzy positive ideal solution and longest distance for the fuzzy negative ideal solution, where the CCj index value is between 0 and 1. Better performance of the alternatives results from a larger CCj index value.

• Step 5: Analyze parallels to the ideal solution.

$C C j = D j − D j * + D j −$
(15)

## 3. RESEARCH METHODOLOGY

In previous studies, the investigation of site selection considered geographic features: that is, a selected site should be appropriate geographically, as seen for example in the work of the following: Brewer et al. (2015), Vlachopoulou et al. (2001), Uyan (2013), Sánchez-Lozano et al. (2013), Cheng et al. (2007), Zamorano et al.. 2008), Mendas and Delali (2012), Al Garni and Awasthi (2017), Neissi et al. (2020), Demir et al. (2021), Majid and Mir (2021). This research uses not only the GIS application and FAHP, but also the FTOPSIS multi-criteria method. However, very few studies have deal with natural disasters associated with the desired site, particularly during the COVID-19 pandemic. Therefore, this research aims at including disaster assessment to contribute to the site selection process of biomass power plants. This research has evaluated the suitability of the area to find an area for a biomass power plant by using vector polygon overlay, which not only considers the infrastructure but also the natural disasters that may occur in the area. The selection of the analysis and evaluation of layer maps concern nine factors of a biomass power plant location: forests, buildings, slopes, aquifers, earthquakes, geology, roads, bodies of water, and power grids. The most appropriate location is assessed using all six factors: forests, buildings, slopes, aquifers, earthquakes, and geology, which have to be classified as “appropriate,” and the summation of the distance of the last three factors roads, bodies of water, and power grids has to be minimal. Figure 3 illustrates the methodology employed in this research.

### 3.1 Theoretical Research Methodology

#### 3.1.1 Part I: Preliminary Screening Site

The data formulation for each site for the decision-making support system is explained in this section, which comprises practical information for biomass power plant locations. GIS is used to find the best location for a biomass power plant during the initial development of spatial examination. Potential locations are screened using GIS analysis. Based upon certain geographic features, incompatible sites are disqualified. Slope, geographic direction, and the height of construction are deemed to be significant options. Rough mountainous land, for example, with low valleys is difficult and incompatible for constructing biomass power plants. Primarily, GIS checks a possible area for the best location for an effective biomass power plant as well as a potential area with the least amount of slope, assuming it is a long distance from any community or river, where there is the risk of flooding. Forestry regions and anywhere near historical buildings are other unpopular sites.

#### 3.1.2 Part II: Overlay Data Layer

One of the main research processes is map overlay, since it can be applied in numerous fields including surveying, urban planning, remote sensing, and GIS (Chiu and Wang, 2003). The map overlay technique offers a number of benefits, so it was integrated with GIS to provide an in-depth analysis of various locations in order to support the selection of the ideal location for biomass power plants in Thailand. In this research, the assessment process of the GIS-based map overlay method considers the nine data layers to be assessed in an overlay map to provide an overview of alternative locations for a biomass power plant. The data layers consist of forests, buildings, slopes, aquifers, earthquakes, geology, roads, bodies of water, and power grids.

#### 3.1.3 Part III: Weight Calculated using FAHP

The pertinent factors for the choice of construction sites were weighted through FAHP in order to find the real implication for every factor, after the locations were screened geographically, as detailed in the first stage. Different weight vectors can be described by decision-makers with dissimilar experiences. In the decision-making steps, usually result in vague estimations. For this reason, we proposed a group decision based upon FAHP in order to improve the pairwise comparison. First, each decision-maker carries out a pairwise comparison individually by using Satty’s 1-9 scale. Then, a comprehensive pairwise comparison was built by integrating three decision-makers’ pairwise comparisons. In this manner, the decision-makers’ pairwise evaluation values can be changed into triangular fuzzy numbers (TFN). The weights of all criteria were resolved by using the FAHP after making a fuzzy pairwise comparison matrix.

#### 3.1.4 Part IV: FTOPSIS Ranking

FTOPSIS employs FAHP weighted results as the input weight in part IV. This allows for reliable and organized standards for selecting the best option possessing the most direct route from the fuzzy positive ideal solution (FPIS, A*) and the remotest distance from the negative ideal solution (FNIS, A*) (Dağdeviren et al., 2009). The decision-maker can assess the ranking and choose the most appropriate option when having both an ideal and a non-ideal solution.

## 4. AN EMPIRICAL STUDY

Empirical study for biomass power plant location was conducted during the COVID-19 pandemic hazard in 2020 based upon the avoided area according to the flood of 2019 in Thailand, as shown in Figure 4.

### 4.1 Development of GIS for Biomass Power Plant Site Selection

Geographical features were first extracted using computer software. After that, the 9 overlay map data layers were analyzed.

Layer 1, Forests: Biomass power plants occupy large areas of land, and the land for biomass power plants should not conflict with other land usage, such as protected natural reserves (forests). The Forest Polygon Attribute Table in Figure 5 indicates the habitat suitability of the case study.

Layer 2, Buildings: The biomass power plant should be located in an area with no public housing, because it may have an adverse effect on residential areas. The Building Polygon Attribute Table in Figure 6 shows the habitat suitability of the case study.

Layer 3, Slopes: The Slope Polygon Attribute Table in Figure 7 illustrates the suitability of the slope of the area of the case study.

Layer 4, Aquifers: An underground layer of water-supporting permeable rock or aggregate materials (gravel, sand, or silt) used to extract groundwater from a water well is called an aquifer. The biomass power plant needs water to be used in the power generation process and for the utilities. Thus, underground water (aquifer) is an alternative water source to replace the earth water, which is insufficient for a biomass power plant. The Aquifer Polygon Attribute Table in Figure 8 shows the suitability of the underground water of the area of the case study.

Layer 5, Earthquakes: The areas affected by earthquakes should be excluded from the biomass power plant site. The Earthquake Polygon Attribute Table in Figure 9 demonstrates the intensity of earthquakes in the case study area.

Layer 6, Geology: Soil types have an important role in determining the kind of work that can be carried out at a particular location, with foundation work critically affected. Where soils are loose or sandy, the potential of a site is highly limited. The Geology Polygon Attribute Table in Figure 10 shows the suitability of the geology area of the case study.

Layer 7, Roads: One of the fundamental factors is the road distribution in the area. This is because the proximity of roads allows the movement of construction equipment and the convenient performance of activities, which means lower transportation costs and time-savings in transportation. Thus, the ideal location for constructing a biomass power plant should be close to a road (in meters) that facilitates the operations and activities, as shown in Figure 11.

Layer 8, Bodies of water: A water source is essential to the activities carried out in the biomass power plant and utilized for basic activities such as daily consumption, so the ideal location should be close to a water source (in meters), as shown in Figure 12.

Layer 9, Power grids: The power produced by a biomass power plant needs to be conveyed to an electrical transmission line. A location adjacent to the transmission line is therefore ideal. Because of the proximity of the transmission line, the distance of the transmission line (in meters) from the given area should be shortened, resulting in less reduction of the electricity protection system and in the reduction of the cost of the transmission line, as shown in Figure 13.

### 4.2 Assessing the site for biomass power plants by using GIS

This stage takes all 9 data layers to be assessed in an overlay map to provide an overview of an appropriate area for a biomass power plant site. The data layers, as mentioned earlier, consist of forests, buildings, slopes, aquifers, earthquakes, geology, roads, bodies of water, and power grids. In the analysis of the overlay of the map data layers for layers 1–6, in which the map data layers include forests, buildings, slopes, aquifers, earthquakes, and geology, as shown in Table 2, there is a number representing the suitability of the area, which is assigned two values: 0 and 1, where “0” means that the area is unsuitable, and “1” means the opposite. The alternative area suitable for a biomass power plant must be an area suitable for all map data layers, which is represented by “1” for layers 1-6. However, if the alternative area is assigned a “0” for even one layer, the data will be excluded from the analysis. Thus, as shown in Table 2, the suitable area derived from the overlay of the map data layers is alternative area C-1, C-2, C-3, and C-4, as shown in Figure 14.

### 4.3 Fuzzy AHP for the Site Selection

A three-level hierarchical representation for the biomass power plant site challenge in northeastern Thailand is illustrated in Figure 15, which is founded on a hierarchical structure created according to all potential criteria and alternatives. Identifying the optimal site for a biomass power plant for electric power supply to the field hospital during the COVID-19 pandemic among possible candidates is the first level, while the goal of the model has three criteria as shown in the second level; namely, distance from power grids, distance from bodies of water, and distance from roads. The four potential locations are shown in Figure 15 at the lowest level of the hierarchical model.

Different decision-makers can prioritize different weighting vectors, and therefore the evaluations made are often quite inaccurate and the process can be contentious. For this reason, a sample of decision-makers who are expert and experienced in the electricity generation field was chosen from the primary electricity authorities of Thailand, including the Electricity Generation Authority of Thailand (EGAT), the Provincial Electricity Authority (PEA), and the Metropolitan Electricity Authority (MEA), to measure the importance of decision criteria based upon individual perspectives using the AHP. The advantage is that it brings consistency to the weight in selection problems whose decision criteria are expressed in subjective measures based upon user needs (Kengpol et al., 2012). Thus, three decision-makers were chosen to rate the importance of the decision criteria and to reduce bias from the decision made by a single decision-maker, based upon the FAHP, to improve the pairwise comparison (Ertuğrul and Karakaşoğlu, 2009). First, each decision-maker (Dp) carried out a pairwise comparison individually by using Satty’s 1–9 scale (Satty, 1980), as shown in Tables 3-5.

By mixing the three decision-makers’ pairwise evaluation using Equation (16), a broad pairwise assessment can be formed (Chen et al., 2006). Using this approach, the decision-makers’ pairwise assessment values change into triangular fuzzy numbers (TFN), as shown in Table 6.

$( x ) = ( a i j , b i j , c i j ) l i j = min k { a i j k } , m i j = 1 K ∑ k = 1 K b i j k , u i j = max k { d i j k }$
(16)

After constructing the fuzzy pairwise comparison matrix, the criteria weightings were established using the FAHP. Table 6 presents the fuzzy evaluation matrix with respect to the objective using triangular fuzzy numbers. In order to determine the priority weightings of the principal attributes, the fuzzy synthetic extent values for each attribute were first of all calculated by applying Equation (1), in which Sg, Sw, and Sr indicate the various different values for the fuzzy synthetic extent of the three different principal attributes. The degree of possibility of Si over Sj ($i ≠ j$) is determined by using Equation (5) and Equation (6). The priority weights are calculated by using Equation (7).

Accordingly, the weight vector is indicated as $W '$ = (1, 0.702, 0.138). Following the process of normalization, the principal attribute weight vectors namely the distances from power grids, bodies of water, and main roads can be calculated as (0.543, 0.382, 0.075). It can thus be inferred that the most significant attribute when selecting the location of a biomass power plant is the distance from the power grid since this is the attribute that attracts the highest weighting. Distance from bodies of water and distance from roads are the second and third most preferred attributes, respectively.

### 4.4 Application of FTOPSIS in Ranking the Biomass Power Plant Location Alternatives

In the final step, the FTOPSIS technique is used to rank various alternative locations. The construction of the fuzzy evaluation matrix depends upon the evaluation of the alternative locations using the linguistic values shown in Table 7, providing the resulting outcome indicated in Table 8. Having determined the fuzzy evaluation matrix, a fuzzy weight must be found through the use of the criteria weights set out in the FAHP. The Weighted Evaluation Matrix is then calculated using Equation (10). Table 9 shows that the elements given by $v ˜ i j , ∀ i j$ are normalized positive triangular fuzzy numbers with ranges in the closed interval [0,1]. It is therefore possible to define the fuzzy positive ideal solution (FPIS, $A *$) as well as the fuzzy negative ideal solution (FNIS, $A −$) as $v ˜ i * = ( 1 , 1 , 1 )$ and $v ˜ i − = ( 0 , 0 , 0 )$ to represent the benefit criterion, and $v ˜ i * = ( 0 , 0 , 0 ) v ˜ i − = ( 1 , 1 , 1 )$ serving as the cost criterion. For the purposes of this research, the benefit criteria are C1, C2, and C3.

The FTOPSIS technique can then be applied to calculate the rankings for the various locations. It is then necessary to calculate the separation measure for the fuzzy positive and fuzzy negative optimal alternatives. Through the application of Equation (14) and Equation (15), it is possible to determine the values for $D i * and D i −$, which can be observed in Table 10.

Finally, the calculated $D i * and D i −$ values are used to find the relative closeness (Ci). Table 10 shows the calculated relative closeness values.

According to the results in Table 10, the order of priority of the alternative locations is determined as C-1 > C-2 > C-3 > C-4. As a conclusion, the alternative location C-1 (CCj = 0.240) should be selected for the biomass power plant.

### 4.5 Sensitivity Analysis

Sensitivity analysis (as shown in Table 11 and Figure 16) is performed by varying the weighting of distances in the 3 criteria, which are distance from power grids, distance from bodies of water, and distance from roads. This can benefit the local communities in terms of assessing the alternative choice in depth.

Sensitivity analysis ought to be conducted by essential decision-makers in order to check whether the evaluation and ranking processes are suitably robust. This can be done by applying different priority weights to the attributes used in decision-making. The methodology of the FAHP and FTOPSIS can also be assessed through sensitivity analysis by altering the weights derived through the FAHP to use two different values while the remainders are unchanged. At the same time, the weighting for the first criterion C1 is adjusted to the weighting of C2, and then in sequence C3. FTOPSIS can then be operated in order to see the new results. It is then possible to examine and consider how the behavior of the methodology responds to the weighting changes. When conducting sensitivity analysis, it is normal to carry out three mutual changes of weighting, although more can be applied in order to expand the analysis further. From this process it is possible to observe the changes that occur in the results of the methodology, thereby assisting the system user in setting the right priorities to ease the process of evaluation (Gumus, 2008).

The results of the sensitivity analysis are shown in Table 11 and graphically in Figure 16. While the weights are interchanged, the index values $( C C j )$ and the rankings of alternative locations are changed when the priority weights of the criteria are interchanged. If the priority weights C1 and C2 are changed, then the index value $( C C j )$ of location C-1 is decreased from 0.240 to 0.230, and the ranking of location C-1 is changed. Apart from this, location C-2 has the maximum index value $( C C j )$ of 0.235 by interchanging the weights C1 and C2. Therefore, location C-2 has the first ranking and locations C-1, C-3, and C-4 have second, third, and final rankings, respectively.

According to the results from the sensitivity analysis, location C-1 is robust enough to be the most appropriate alternative location. That is because it had the highest index value (CCj) after the weight interchanges were carried out. The sensitivity analysis can be expanded by interchanging the weights in a different manner.

### 4.6 Results Summary

The research methodology begins with the first step, which is the preliminary screening of sites that show the selection of the case study area by avoiding flooding areas. The case study area is obtained (Figure 4). Then, the acquired areas are used to process step 2 by considering the six aspects of the geographical characteristics comprising forests, buildings, slopes, aquifers, earthquakes, and geology with the overlaying map data layers. Sixteen areas are selected (C-1 - C16) (Figure 14). Then, all 16 areas are analyzed to find the most appropriate area with the six geographical characteristics (Table 2). It was found that areas C-1, C-2, C-3, and C-4 are appropriate in terms of geographical characteristics (Figure 17). Then, in step 3, the weight is calculated using the FAHP to find the weight of the factors compared to the selected areas using the FAHP. The findings indicated that the weighting factor “distances from power grids” has the highest value (0.543) followed by “distances from the bodies of water” (0.382) and “distances from roads” (0.075) respectively. Lastly, the weighting factors for using FTOPSIS are analyzed. The results show that C-1 is the first ranking with the CCj being 0.240, followed by areas C-2, C-3, and C-4, where the CCj is 0.225, 0.166, and 0.106, respectively (Table 10). In summary, C-1 is the most appropriate area for the biomass power plant to supply electricity to the field hospital during the COVID-19 pandemic. This is also consistent with the sensitivity analysis (Table 11) when adjusting the weighting factor; the CCj of area C-1 has the highest value as well.

The identified alternative locations (C-1, C-2, C-3, and C-4) are shown in Figure 17 and the data for each alternative location demonstrates a detailed map of the amount of space, Universal Transverse Mercator: UTM, latitude and longitude, the distance from the location to the transmission line, water sources, roads, and the total distance, as shown in Table 12.

Table 12 presents the results for the alternative locations of the biomass power plants using the GIS application. The overlay of 9 data layers includes, as indicated earlier, forests, buildings, slopes, aquifers, earthquakes, geology, roads, bodies of water, and power grids. The alternative locations for the biomass power plant based upon the overlay of the 9 data layers are C-1 (70 meters from the power grid, 506 meters from bodies of water, and 507 meters from roads, which makes a total distance of 1,083 m); C-2 (324 meters from the power grid, 570 meters from bodies of water sources, and 863 meters from roads, which makes a total distance of 1,757 m); C-3 (190 meters from the power grid, 945 meters from bodies of water, and 649 meters from roads, which makes a total distance of 1,784 m); and C-4 (542 meters from the power grid, 912 meters from bodies of water, and 30 meters from roads, which makes a total distance of 1,484 m).

## 5. IMPLEMENTATION

In this section, the implementation of the overlay data layer, the hierarchical FAHP, and FTOPSIS are presented.

### 5.1 Overlay Data Layer Results

The cartographic analysis in this research delineates nine geographic attributes, including forests, buildings, slopes, aquifers, earthquakes, geology, roads, bodies of water, and power grids, as shown in Figures 5 to 13, and to identify alternative locations as shown in Figure 14. All alternative locations are then analyzed to determine the optimal locations, as shown in Table 2. As a result, four optimal locations for the power plant are obtained, as shown in Figure 17.

### 5.2 Hierarchical FAHP Results

In this research, the experts from the Electricity Generation Authority of Thailand (EGAT), the Provincial Electricity Authority (PEA), and the Metropolitan Electricity Authority (MEA) were selected, as mentioned above, for their opinions concering the site selection for a biomass power plant. The weight rating for three decision criteria was processed by the experts, as shown in Tables 3 to 5, and the results were translated into a triangular fuzzy number (TFN). Equation (16) implies the fuzzy pairwise comparison matrix with respect to the goal with TFN, as shown in Table 6, and the weights of the decision criteria were then calculated. Equation (7) implies the weights of all three decision criteria.

### 5.3 FTOPSIS Results and Implementation

The FTOPSIS method is used generally to figure out the ranking of the various alternative locations. The fabrication of the fuzzy evaluation matrix depends upon alternative location assessment using the linguistic values as shown in Table 7, and the results shown in Table 8. In order to determine the fuzzy evaluation matrix, a fuzzy weight is identified by using the criteria weights outlined in the FAHP. Table 9 indicates that the components are normalized positive triangular fuzzy numbers. Thus, it is feasible to determine the fuzzy positive ideal solution as well as the fuzzy negative ideal solution. The FTOPSIS is applied to calculate the rankings for the alternative locations. It is necessary to calculate the separation measure for the fuzzy positive and fuzzy negative optimal alternatives. With Equation (13) and Equation (14) applied, it is possible to determine the values for and which is observed in Table 10, sorting the options by using the CCj index value to establish the most appropriate solution by Equation (15). As a result, C-1 is recommended as the appropriate site for the biomass power plant wherein the geographic coordinate is as follows: Universal Transverse Mercator (UTM) 184824,1758584, latitude 15.886374730621409, and longitude 102.05696416296801, where the distance from power grids is 70 meters, the distance from bodies of water is 506 meters, and the distance from roads is 507 meters for electricity generation and supply to a field hospital during an exacerbation of the COVID-19 pandemic in Thailand.

### 5.4 Theoretical Implication

Site selection theories were primarily founded in the field of geography for the valuation of agricultural land with an emphasis on minimizing the production cost in agriculture. Later, industry development causes a shift in site selection theories, the models have increasingly supported the industrial site selection to reduce costs and profitability. Typically, there are two theories of site selections, including; increased profitability and quantifying methods and models.

According to the researches (Neissi et al., 2020;Demir et al., 2021;Majid and Mir, 2021;Gil-Garćia et al., 2022;Eren and Katanalp, 2022), the findings show the site selection principles initiate with the consideration of geographic maps to screen potential alternative areas from the scope of the study area, coupled with the criteria for consideration that are important to the activities to be carried out at the site and as well as the screened area. The site locations are then performed with multi-criteria decision-making technique comparison by score ranking order and by each selected location to identify the optimal location. In conclusion, site selection for different types of activities is common in the principles, however, it may vary from activities to activities carried out individually. Thus, to consider the suitable location for each type of activity, it’s needed to examine and adjust the criteria appropriately with the respective activities. This means that the appropriate facility location is vital to the activities carried out at the chosen location. The success or failure of the activities depends on the site selection through a methodical analysis.

In this research, the aforesaid procedures are applied and the criteria are adjusted appropriately with the activities and focusing on disasters that occur as well as environmental friendliness. For Thailand’s today, the location of electricity generating power distributed to the field hospitals is regarded urgently important because the reported COVID-19 cases and deaths have risen continuously, and the field hospitals are necessarily set up to accommodate the increasing number of infected COVID-19 patients. Therefore, the site selection for biomass power plants for electric power supply to the field hospitals applies the same site selection principles above mentioned and generally it is subject to natural disaster criteria, infrastructure, and environmental friendliness for the sustainability assessment that is realistic and reliable. In addition, the decision-making process from this research can also be used to examine the location of other alternative energy power plants. However, the factors must be modified to suit each type of energy and the area to study. The results provide guidelines for the location selected from the perspective of environmental friendliness and sustainability assessment.

### 5.5 Methodology Implication

In previous studies, the principal of GIS, FAHP, and FTOPSIS have discussed for site selection, for example, GIS (Shi et al., 2008;Sánchez-García et al., 2017;Brahma et al., 2016;Sharma et al., 2017;Perpiña et al., 2012;Höhn et al., 2014;Janke, 2010;Brewer et al., 2015;Fernandez-Jimenez et al., 2015;Vlachopoulou et al., 2001;Venier and Yabar, 2017), the FAHP (Kahraman et al., 2004;Chou et al., 2007), integrated GIS and AHP (Perpiña et al., 2012;Vlachopoulou et al., 2001;Al Garni and Awasthi, 2017;Uyan, 2013;Aly et al., 2017;Wang et al., 2009), integrated GIS, AHP, and TOPSIS (Sánchez-Lozano et al., 2013;Ramya and Devadas, 2019), integrated FAHP and TOPSIS (Choudhary and Shankar, 2012), integrated GIS, FAHP and TOPSIS (Erbaş et al., 2018), and FTOPSIS (Guo and Zhao, 2015). In this research, the principle of GIS is applied to mapping analysis where different geographic mapping data and natural disaster maps are overlapped to determine the preliminary suitable location for biomass power plant. Subsequently, the criteria affecting the site selection for biomass power plant sites are taken into consideration to determine the criteria weight with the expert validation through hierarchy process, using the Fuzzy AHP method to calculate the weight of each decision criterion. The calculated weight of the decision criterion is then analyzed to determine the optimal outcomes by using the Fuzzy TOPSIS method, which is based upon choosing the best alternative having the shortest distance from the positive ideal solution and the farthest from the negative ideal solution.

Based upon existing literature, there are a number of methods that have been applied for site selection, such as the Genetic Algorithm (Shi et al., 2019), integrated Artificial Neural Network and Genetic Algorithm (Lee et al., 2010), integrated Fuzzy Analytic Network Process and Fuzzy Goal Programming (Lee et al., 2017), Particle Swarm Optimization (Luo et al., 2020), integrated Analytic Network Process and PROMETHEE (Wu et al., 2020), which methods apply complicated analysis that takes a long time to obtain results. Therefore, in this research, the GIS (overlay data layer), FAHP, and FTOPSIS are used in the location selection of a biomass power plant, because the FAHP and FTOPSIS methods are quite simple in conception and application when compared to other methods of multi-criteria analysis.

Recently, the principal of GIS, FAHP, and FTOPSIS have been applied in several fields, such as flood risk assessment (Zhang et al., 2020), risk assessing in hazardous materials transportation (Ak et al., 2020), groundwater suitability analysis for drinking (Mallik et al., 2021), urban densification suitability analysis (Shen et al., 2021), assessment of flood-prone zones (Osei et al., 2021), evaluate the sensitivity of the hydroelectric system (Ram and Chandna, 2018), shipyard selection (Sahin et al., 2021), risk ranking of maintenance activities (Ünver et al., 2021), mining technology selection with spherical (Dogan, 2021), performance evaluation of reverse logistics (Han and Trimi, 2018), and sustainability models in the development of electric vehicles (Samaie et al., 2020). Especially at present, it has been applied in the COVID-19 pandemic, such as using GIS with big data providing geospatial information to fight COVID-19 (Zhou et al., 2020), choosing the most suitable occupational healthcare amid the COVID-19 pandemic (Chen, 2021), and evaluating government strategies against the COVID-19 pandemic (Alkan and Kahraman, 2021).

### 5.6 Practical Implication

The publication of research for site selection, including the criteria for disasters and environmental friendliness associated with the desired site, has not been analysed these elements together. In the present study, a survey for appropriate site selection gives attention to natural disaster criteria, such as flooding and earthquakes, into the analysis of the site selection as in the study of Sennaroglu and Celebi (2018), Pambudi and Nananukul (2019), Esmaeilpour-Poodeh et al. (2019), Kaya et al. (2020a), Kaya et al. (2020b), Rezaeisabzevar et al. (2020), and Waewsak et al. (2020). Therefore, this research focuses on the natural disaster criteria in the case study site, infrastructure, and environmental friendliness for the sustainability assessment leading to the procedures for selecting the biomass power plant sites with the use of vector overlay, that is; not only taking into accounts of the infrastructure but also natural disasters that may occur in the area. Thus, this study includes designing a systematic decision-making method that is an instrument to the application of decision-making in selecting the suitable sites for building biomass power plants, considering the geographical features in conjunction with natural disasters to supply electric power to field hospitals in the situation of COVID-19 pandemic and finding of research in providing guidelines for the location selection of the biomass power plant from the perspective of disasters and environment friendliness. The advantage of this research is that map assessment includes an analysis of the spacial operation map, infrastructure map, and natural disaster map, because a single infrastructure map or a map of natural disasters can be misleading and inadequate.

The proposed model can be applied to the study of other energy sources as a guideline to examine the determination of potential locations that can produce clean electricity for energy sustainability. That is, it can be applied to academic studies of other energy production sources, such as solar energy (consideration of the intensity of the solar map for each location that can be used to generate electricity) or wind power (consideration of the wind speed map criteria for each location that can be used to produce electricity), in conjunction with the layer maps affecting the locations of biomass power plants, such as forests, buildings, slopes, aquifers, earthquakes, geology, roads, on-groundwater sources, and grid connections, in order to obtain suitable locations for the electricity generating sources studied. The expected outcomes include appropriate locations for generating alternative electricity from clean and eco-friendly energy sources. Currently, the COVID-19 disaster has resulted in the need to allocate electric power to field hospitals in order to accommodate the growth of the number of patients. As a result, alternative electricity production sites become energy sources that are supplementary to the demand for electric power in combination with the main power of the electricity authority in the disaster area.

## 6. CONCLUSIONS AND RECOMMENDATIONS

Because of economic, social, and industrial growth, the demand for energy has increased. Especially nowadays, the higher incidence of natural disasters and the COVID-19 pandemic suggest the possibility of having to build field hospitals to help patients and infected people. In the case presented in this paper, the field hospital is located far from a main power supply. As a result, it cannot use electric power to perform various activities. Therefore, a biomass power plant site was set up to supply electricity to the field hospital in an area far from the main power supply. Hence, current electricity production to minimize CO2 emissions that uses renewable energy, especially biomass energy, is likely to continue to increase. In this research, energy sustainability has been integrated with map data layer analysis and data on natural disasters in order to select biomass power plant locations based upon the 2016-2036 electricity production plans, which will result in sustainable development in terms of energy generation and environmental friendliness.

The objective of this research is to propose sustainable assessment for biomass power plant site selection for electric power supply to a field hospital in the event of a disaster during the COVID-19 pandemic. The scientific value of this research is the acquisition of an assessment method for suitable sites for producing and supplying electricity to areas in need in the event of a COVID-19 disaster, where the analysis is performed systematically with the spatial and numerical data, and where site selection criteria were focused on in order to ensure accurate decision-making through mathematical models and suitable outcomes. The application of the assessment model requires criteria adjustment in determining the geographic limitations of each area because each area considered may have different site selection criteria and this may necessitate different decisions as a result. In this research, the overlay map data layers (GIS), FAHP, and FTOPSIS methods are used together. Still, input data stated in linguistic terms must depend upon the views and experience of decision-makers, and this leads to subjectivity and is one of the drawbacks of the FAHP approach. Specified information, evidence, and know-how are typically needed in the evaluation of criteria, sub-criteria, and for the choice of alternate locations. Nonetheless, subjectivity bias may be shown by experts in their decisions when favoring one criterion over others. For the ordering of options, the FTOPSIS is an organized approach (Choudhary and Shankar, 2012). As the first step, the research defines the alternative locations with the help of overlay map data layers. In this step, the overlay map data layers are assessed in order to provide an overview of appropriate alternative locations for a biomass power plant. The overlay map data layers are used to combine natural disasters and infrastructure such as forests, buildings, slopes, aquifers, earthquakes, geology, roads, bodies of water, and power grids. Additionally, FAHP is integrated in order to determine the weights of the criteria, and the FTOPSIS method is used for determining the ranking of the alternative locations. The advantage of this research is that the sustainable map assessment includes an analysis of the spacial operation map, the infrastructure map, and the natural disaster map, because a single infrastructure map or a map of natural disasters can be misleading and inadequate. It can be concluded that the FAHP and FTOPSIS techniques are simple concepts that can be easily applied, especially when compared to other multi-criteria methods of analysis. This research, therefore, makes a contribution to creating an approach that allows subjective and objective sustainable assessment measures to be readily assessed, thereby lowering the potential for biased decision-making. This is important, especially when considering factors such as natural disasters, which can affect biomass power plants and must be considered in choosing the appropriate location from the perspective of sustainability. Furthermore, the methodology from this research allows the decision-maker to vary the weights in the sensitivity analysis as needed. The benefit of this research in sustainable assessment and understanding of the needs of the local target community site will lead to the prevention of inappropriate locations and will increase the efficiency in the use of biomass power.

According to the results of the research, C-1 is recommended as the appropriate site for a biomass power plant for electricity generation and supply to a field hospital during the COVID-19 pandemic in Thailand. The proposed model can also be applied to the evaluation of locations in the regions of other countries. One of the limitations of this research is that it does not take into consideration the distance criteria between the biomass fuel source and the biomass power plant site. As a result, the prospective power plant site is disqualified from some decision criteria. Thus, future research should consider the distance criteria between the biomass fuel source and biomass power plant in order to ensure the appropriate outcomes for all decision criteria and make a comparison of GIS and other models, such as the Analytic Network Process, the Data Envelopment Analysis and the Discrete imperialist competitive algorithm (see in Kengpol and Tuominen, 2006;Kengpol and Boonkanit, 2011;Kengpol et al., 2012;Koohathongsumrit and Meethom, 2021;Tao et al., 2020).

## ACKNOWLEDGEMENTS

Appreciation is extended to the King Mongkut’s University of Technology North Bangkok (KMUTNB) for the funding of this research. The authors also would like to acknowledge all of the anonymous reviewers for their valuable comments. Contract no. KMUTNB-NRU-58-05.

## Figure

Renewable energy used for global electricity generation.

The intersection between M1 and M2 (Zhu et al., 1999).

The research methodology.

Screening phase: (a) candidate sites – macro; (b) candidate sites – micro.

Forest map for study area.

Building map for study area.

Slope map for study area.

Aquifer map for study area.

Earthquake map for study area.

Geology map for study area.

Bodies of water for study area.

Power grids for study area.

Map of suitable locations for biomass power plants.

Hierarchical structure of the model.

Effect on ranking of locations due to sensitivity analysis.

Alternative locations for biomass power plants.

## Table

References for the topic of location selection

Overlay data layers

Pairwise comparison of the first decision-maker (D1) at the main level

Pairwise comparison of the second decision-maker (D2) at the main level

Pairwise comparison of the third decision-maker (D3) at the main level

Fuzzy pairwise comparison matrix with respect to the goal with TFN

Linguistic values and fuzzy numbers

Fuzzy evaluation matrix for alternative locations

Weighted evaluation for alternative locations

The ranking of alternative location results

Results from the sensitivity analysis

Results of alternative locations for the biomass power plant

## REFERENCES

1. Ak, R. , Bahrami, M. , and Bozkaya, B. (2020), A time-based model and GIS framework for assessing hazardous materials transportation risk in urban areas, Journal of Transport & Health, 19, 1-15.
2. Al Garni, H. Z. and Awasthi, A. (2017), Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia, Applied Energy, 206, 1225-1240.
3. Alkan, N. and Kahraman, C. (2021), Evaluation of government strategies against COVID-19 pandemic using q-rung orthopair fuzzy TOPSIS method, Applied Soft Computing, 110, 1-19.
4. Alternative Energy Development Plan: Ministry of Energy of Thailand, Bangkok (2015), Available from: http://www.eppo.go.th/index.php/th/plan-policy/tieb/aedp.
5. Aly, A. , Jensen, S. S. , and Pedersen, A. B. (2017), Solar power potential of Tanzania: Identifying CSP and PV hot spots through a GIS multicriteria decision making analysis, Renewable Energy, 113, 159-175.
6. Alzahrani, S. I. , Aljamaan, I. A. , and Al-Fakih, E. A. (2020), Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions, Journal of Infection and Public Health, 13(7), 914-919.
7. Aras, H. , Erdoğmuş Ş. , and Koç, E. (2003), Multi-criteria selection for a wind observation station location using analytic hierarchy process, Renewable Energy, 29(8), 1383-1392.
8. Bhatta, B. (2008), Remote sensing and GIS, Oxford, Oxford University Press.
9. Brahma, A. , Saikia, K. , Hiloidhari, M. , and Baruah, DC. (2016), GIS based planning of a biomethanation power plant in Assam, India, Renewable and Sustainable Energy Reviews, 62, 596-608.
10. Brewer, J. , Ames, D. P. , Solan, D. , Lee, R. , and Carlisle, J. (2015), Using GIS analytics and social preference data to evaluate utility-scale solar power site suitability, Renewable Energy, 81, 825-836.
11. Celik, M. , Er, I. D. , and Ozok, A. F. (2009), Application of fuzzy extended AHP methodology on shipping registry selection: The case of Turkish maritime industry, Expert Systems with Applications, 36, 190-198.
12. Chang, D. Y. (1996), Applications of the extent analysis method on Fuzzy AHP, European Journal of Operational Research, 95, 649-655.
13. Chen, C. T. , Lin, C. T. , and Huang, S. F. (2006), A fuzzy approach for supplier evaluation and selection in supply chain management, International Journal of Production Economics, 102(2), 289-301.
14. Chen, T. (2021), A FAHP-FTOPSIS approach for choosing mid-term occupational healthcare measures amid the COVID-19 pandemic, Health Policy and Technology, 10(2), 100517.
15. Cheng, E. W. L. , Li, H. , and Yu, L. (2007), A GIS approach to shopping mall location selection, Building and Environment, 42(2), 884-892.
16. Chiu, C. S. and Wang, D. L. (2003), Weighted coordinates transformation method for map overlay with non-homogeneous space partition, Computer & Geosciences, 29(7), 877-883.
17. Chou, T. Y. , Hsu, C. L. , and Chen, M. C. (2007), A fuzzy multi-criteria decision model for international tourist hotels location selection, International Journal of Hospitality Management, 27(2), 293-301.
18. Choudhary, D. and Shankar, R. (2012), An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India, Energy, 42(1), 510-521.
19. Dağdeviren, M. , Yavuz, S. , and Kilinç, N. (2009), Weapon selection using the AHP and TOPSIS methods under fuzzy environment, Expert Systems with Applications, 36(4), 8143-8151.
20. Demir, S. , Basaraner, M. , and Gumus, A. T. (2021), Selection of suitable parking lot sites in megacities: A case study for four districts of Istanbul, Land Use Policy, 111, 105731.
21. Dogan, O. (2021), Process mining technology selection with spherical fuzzy AHP and sensitivity analysis, Expert Systems with Applications, 178, 1-9.
22. Erbaş, M. , Kabak, M. , Özceylan, E. , and Ҫetinkaya, C. (2018), Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis, Energy, 163, 1017-1031.
23. Eren, E. and Katanalp, B. Y. (2022), Fuzzy-based GIS approach with new MCDM method for bike-sharing station site selection according to land-use types, Sustainable Cities and Society, 76, 103434.
24. Ertuğrul, I. and Karakaşoğlu, N. (2009), Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods, Expert Systems with Applications, 36(1), 702-715.
25. Esmaeilpour-Poodeh, S. , Ghorbani, R. , Hosseini, S. B. , Salmanmahiny, A. , Rezaei, H. , and Kamyab, H. (2019), A multi-criteria method for sturgeon farming site selection in the southern coasts of the Caspian Sea, Aquaculture, 513, 734416.
26. ESRI (1992a), ARC/INFO: GIS Today and Tomorrow, Environment Systems Research Institute, Redlands, CA.
27. ESRI (1992b), Understanding GIS, Environment Systems Research Institute, Redlands, CA.
28. Fanelli, D. and Piazza, F. (2020), Analysis and forecast of COVID-19 spreading in China, Italy and France, Chaos, Solitons and Fractals, 134, 109761.
29. Fernandez-Jimenez, L. A. , Mendoza-Villena, M. , Zorzano-Santamaria, P. , Garcia-Garrido, E. , Lara-Santillan, P. , Zorzano-Alba, E. , and Falces, A. (2015), Site selection for new PV power plants based on their observability, Renewable Energy, 78, 7-15.
30. Gil-García, I. C. , Ramos-Escudero, A. , García-Cascales, M. S. , Dagher, H. , and Molina- García, A. (2022), Fuzzy GIS-based MCDM solution for the optimal offshore wind site selection: The gulf of marine case, Renewable Energy, 183, 130-147.
31. Girard, A. , Roberts, C. , Simon, F. , and Ordoñez, J. (2019), Solar electricity production and taxi electrical vehicle conversion in Chile, Journal of Cleaner Production, 210, 1261-1269.
32. Guo, S. and Zhao, H. (2015), Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective, Applied Energy, 158, 390-402.
33. Gumus, A. T. (2008), Evaluation of hazardous waste transportation firms by using a two-step fuzzy AHP and TOPSIS methodology, Expert Systems with Applications, 36(2), 4067-4074.
34. Han, H. and Trimi, S. (2018), A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms, Expert Systems with Applications, 103, 133-145.
35. Hernandez-Matamoros, A. , Fujita, H. , Hayashi, T. , and Perez-Meana, H. (2020), Forecasting of COVID 19 per regions using ARIMA models and polynomial functions, Applied Soft Computing, 96, 106610.
36. Höhn, J. , Lehtonen, E. , Rasi, S. , and Rintala, J. (2014), A Geographical Information System (GIS) based methodology for determination of potential biomasses and sites for biogas plants in southern Finland, Applied Energy, 113, 1-10.
37. Hwang, C. L. and Yoon, K. (1981), Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, Heidelberg.
38. International Energy Agency (IEA), The latest trends in energy and emissions in 2018, Available from: http://www.iea.org/geco/emissions/.
39. Jahanshahloo, G. R. , Lotfi, F. H. , and Izadikhah, M. (2006), Extension of the TOPSIS method for decision-making problems with fuzzy data, Applied Mathematics and Computation, 181(2), 1544-1551.
40. Janke, J. R. (2010), Multicriteria GIS modeling of wind and solar farms in Colorado, Renewable Energy, 35(10), 2228-2234.
41. Jensen, J. R. (1996), Introductory Digital Image Processing: A Remote Sensing Perspective, Upper Saddle River, New Jersey, Prentice Hall.
42. Kahraman, C. , Cebeci, U. , and Ulukan, Z. (2003), Multi-criteria supplier selection using fuzzy AHP, Logistic International Journal Management, 16(6), 382-394.
43. Kahraman, C. , Cebeci, U. , and Ruan, D. (2004), Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey, International Journal of Production Economics, 87(2), 171-184.
44. Kaya, Ö., Alemdar, K. D. , and Ҫodur, M. Y. (2020a), A novel two stage approach for electric taxis charging station site selection, Sustainable Cities and Society, 62,102396.
45. Kaya, Ö., Tortum, A. , Alemdar, K. D. , and Ҫodur, M. Y. (2020b), Site selection for EVES in Istanbul by GIS and multi-criteria decision-making, Transportion Research Part D, 80, 102271.
46. Kengpol, A. and Boonkanit, P. (2011), The decision support framework for developing Ecodesign at conceptual phase based upon ISO/TR 14062, International Journal of Production Economics, 131(1), 4-14.
47. Kengpol, A. and Tuominen, M. (2006), A framework for group decision support systems: An application in the evaluation of information technology for logistics firms, International Journal of Production Economics, 101(1), 159-171.
48. Kengpol, A. , Meethom, W. , and Tuominen, M. (2012), The development of a decision support system in multimodal transportation routing within Greater Mekong sub-region countries, International Journal of Production Economics, 140(2), 691-701.
49. Khan, I. (2019), Greenhouse gas emission accounting approaches in electricity generation systems: A review, Atmospheric Environment, 200, 131-141.
50. Kilincci, O. and Onal, S. A. (2011), Fuzzy AHP approach for supplier selection in a washing machine company, Expert System with Applications, 38(8), 9656-9664.
51. Koohathongsumrit, N. and Meethom, W. (2021), Route selection in multimodal transportation networks: A hybrid multiple criteria decision-making approach, Journal of Industrial and Production Engineering, 38(1), 171-185.
52. Korpela, J. and Tuominen, M. (1996), A decision aid in warehouse site selection, International Journal of Production Economics, 45(1-3), 169-180.
53. Lee, K. H. , Jun, S. O. , Pak, K. H. , Lee, D. H. , Lee, K. W. , and Park, J. P. (2010), Numerial optimization of site selection for offshore wind turbine installation using genetic algorithm, Current Applied Physics, 10(2), S302-S306.
54. Lee, A. H. I. , Chen, H. H. , and Kang, H. Y. (2017), A conceptual model for prioritizing dam sites for tidal energy sources, Ocean Engineering, 137, 38-47.
55. Luo, S. , Liang, W. , and Zhao, G. (2020), Hybrid PSO-WDBA method for the site selection of tailing pond, Computers & Industrial Engineering, 143, 106429.
56. Majid, M. and Mir, B. A. (2021), Landfill site selection using GIS based multi criteria evaluation technique. A case study of Srinagar city, India, Environmental Challenges, 3, 100031.
57. Mallik, S. , Mishra, U. , and Paul, N. (2021), Groundwater suitability analysis for drinking using GIS based fuzzy logic, Ecological Indicators, 121, 107179.
58. Mendas, A. and Delali, A. (2012), Integration of Multi Criteria Decision Analysis in GIS to develop land suitability for agriculture: Application to durum wheat cultivation in the region of Mleta in Algeria, Computers and Electronics in Agriculture, 83, 117-126.
59. Neissi, L. , Albaji, M. , and Nasab, S. B. (2020), Combination of GIS and AHP for site selection of pressurized irrigation systems in the Izeh plain, Iran, Agricultural Water Management, 231, 106004.
60. Önüt, S. and Soner, S. (2008), Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment, Waste Management, 28(9), 1552-1559.
61. Osei, B. K. , Ahenkorah, I. , Ewusi, A. , and Fiadonu, E. B. (2021), Assessment of flood prone zones in the Tarkwa mining area of Ghana using a GIS-based approach, Environment Challenges, 3, 100028.
62. Ozdemir, S. and Sahin, G. (2018), Multi-criteria decision-making in the location selection for a solar PV power plant using AHP, Measurement, 129, 218-226.
63. Pambudi, G. and Nananukul, N. (2019), A hierarchical fuzzy data envelopment analysis for wind turbine site selection in Indonesia, Energy Reports, 5, 1041-1047.
64. Perpiña, C. , Martínez-Llario, J. C. , and Pérez-Navarro, A. (2012), Multicriteria assessment in GIS environments for siting biomass plants, Land Use Policy,31, 326-335.
65. Power Development Plan2018 (PDP, 2018): Ministry of Energy of Thailand, Bangkok, Available from: http://www.eppo.go.th/index.php/en/policy-and-plan/en-tieb/tieb-pdp.
66. Ram, M. and Chandna, R. (2018), Sensitivity analysis of a hydroelectric production power plant under reworking scheme using fuzzy AHP approach, Journal of Industrial and Production Engineering, 35(8), 481-485.
67. Ramya, S. and Devadas, V. (2019), Integration of GIS, AHP and TOPSIS in evaluating suitable locations for industrial development: A case of Tehri Garhwal district, Uttarakhand, India, Journal of Cleaner Production, 238, 117872.
68. Renewables Global Status Report (2019), Available from:http://www.ren21.net/gsr-2019/chapters/chapter_01/chapter_01/#sub_4.
69. Rezaeisabzevar, Y. , Bazzargan, A. , and Zohourian, B. (2020), Landfill site selection using multi criteria decision making: Influential factors for comparing locations, Journal of Environmental Science,93, 170-184.
70. Sahai, A. K. , Rath, N. , Sood, V. , and Singh, M. P. (2020), ARIMA modeling & forecasting of COVID-19 in top five affected countries, Diabetes & Metabolic Syndrome: Clinical Research & Reviews,14(5), 1419-1427.
71. Sahin, B. , Yazir, D. , Soylu, A. , and Yip, T. L. (2021), Improveed fuzzy AHP based game-Theoretic model for shipyard selection, Ocean Engineering,233, 1-15.
72. Samaie, F. , Meyer-Naimi, H. , Javadi, S. , and Feshki-Farahani, H. (2020), Comparison of sustainability models in development of electric vehicles in Tehran using fuzzy TOPSIS method, Sustainable Cities and Society, 53, 1-13.
73. Sánchez-García, S. , Attanassiadis, D. , Martínez-Alonso, C. , Toosana, E. , Majada, J. , and Canga, E. (2017), A GIS methodology for optimal location of a wood-fired power plant: Qualtification of available wood fuel, supply chain costs and GHG emissions, Journal of Cleaner Production, 157, 201-212.
74. Sánchez-Lozano, J. M. , Teruel-Solano, J. , Soto-Elvira, P. L. , and García-Cascales, M. S. (2013), Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain, Renewable and Sustainable Energy Reviews, 24, 544-556.
75. Sarkar, K. , Khajanchi, S. , and Nieto, J. J. (2020), Modeling and forecasting the COVID-19 pandemic in India, Chaos, Solitons and Fractals, 139, 110049.
76. Sarkis, J. , Cohen, M. J. , Dewick, P. , and Schröder, P. (2020), A brave new world: Lessons from the COVID-19 pandemic for transitioning to sustainable supply and production, Resources, Conservation & Recycling,159, 1-4.
77. Satty, T. L. (1980), The Analytic Hierarchy Process, McGraw Hill, New York.
78. Sennaroglu, B. and Celebi, G. V. (2018), A military airport location selection by AHP integrated PROMETHEE and VIKOR methods, Transportation Research Part D, 59, 160-173.
79. Sharma, B. , Birrell, S. , and Miguez, F. E. (2017), Spatial modeling framework for biomethanol plant siting and biofuel production potential in the U.S., Applied Energy, 191, 75-86.
80. Shen, S. , Dragićević, S. , and Dujmović, J. (2021), GIS-based logic scoring of preference method for urban densification suitability analysis, Computers, Environment and Urban Systems, 89, 101654.
81. Shi, Q. , Ren, H. , Ma, X. , and Xiao, Y. (2019), Site selection of construction waste recycling plant, Journal of Cleaner Production, 227, 532-542.
82. Shi, X. , Elmore, A. , Li, X. , Gorence, N. Y. , Jin, H. , Zhang, X. , and Wang, F. (2008), Using spatial information technologies to select site for biomass power plants: A case study in Guang Province, China, Biomass and Bioenergy, 32(1), 35-43.
83. Sureeyatanapas, P. , Sriwattananusart, K. , Niyamosoth, T. , Sessomboon, W. , and Arunyanart, S. (2018), Supplier selection towards uncertain and unavailable information: An extension of TOPSIS method, Operation Research Perspectives, 5, 69-79.
84. Tao, X. R. , Li, J. Q. , Han, Y. Y. , Duan, P. , and Gao, K. Z. (2020), Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem, Journal of Industrial and Production Engineering, 37(7), 345-359.
85. Uyan, M. (2013), GIS-based solar farms site selection using Analytic Hierarchy Process (AHP) in Karapinar region, Konya/Turkey, Renewable and Sustainable Energy Reviews, 28, 11-17.
86. Ünver, B. , Altin, İ. , and Gürgen, S. (2021), Risk ranking of maintenance activities in a two-stroke marine diesel engine via fuzzy AHP method, Applied Ocean Research, 111, 1102648.
87. Venier, F. and Yabar, H. (2017), Renewable energy recovery potential towards sustainable cattle manure management in Buenos Aires Province: Site selection based on GIS spatial analysis and statistics, Journal of Cleaner Production, 162, 1317-1333.
88. Vlachopoulou, M. , Silleos, G. , and Manthou, V. (2001), Geographic information systems in warehouse site selection decisions, International Journal of Production Economics, 71(1-3), 205-12.
89. Waewsak, J. , Ali, S. , and Gagnon, Y. (2020), Site suitability assessment of para rubberwood-based power plant in the southernmost provinces of Thailand based on a multi-criteria decision-making analysis, Biomass and Bioenergy, 137, 105545.
90. Wang, G. , Qin, L. , Li, G. , and Chen, L. (2009), Landfill site selection using spatial information technologies and AHP: A case study in Beijing, China, Journal of Environmental Management, 90(8), 2414-2421.
91. World Health Organization Coronavirus Disease (COVID-19) Dashboard data last updated: 2021/9/21 (2021), Available from:https://covid19.who.int/.
92. Wu, Y. , Tao, Y. , Zhang, B. , Wang, S. , Xu, C. , and Zhou, J. (2020), A decision framework of offshore wind power station selection using a PROMETHEE method under intuitionistic fuzzy environment: A case in China, Ocean and Coastal Management, 184, 105016.
93. Yi, C. S. , Lee, J. H. , and Shim, M. P. (2010), Site location analysis for small hydropower using geo-spatial information system, Renewable Energy, 35(4), 852-861.
94. Zamorano, M. , Molero, E. , Hurtado, A. , Grindlay, A. , and Ramos, A. (2008), Evaluation of a municipal landfill site in Southern Spain with GIS-aided methodology, Journal of Hazardous Materials, 160(2-3), 473-481.
95. Zhang, D. , Shi, X. , Xu, H. , Jing, Q. , Pan, X. , Liu, T. , Wang, H. , and Hou, H. (2020), A GIS-based spatial multi-index model for flood risk assessment in the Yangtze River Basin, China, Environmental Impact Assessment Review, 83, 106397.
96. Zheng, X. , Streimikiene, D. , Balezentis, T. , Mardani, A. , Cavallaro, F. , and Liao, H. (2019), A review of greenhouse gas emission profiles, dynamics and climate change mitigation efforts across the key climate change players, Journal of Cleaner Production, 234, 1113-1133.
97. Zhou, C. , Su, F. , Pei, T. , Zhang, A. , Du, Y. , Luo, B. , Cao, Z. , Wang, J. , Yuan, W. , Zhu, Y. , Song, C. , Chen, J. , Xu, J. , Li, F. , Ma, T. , Jiang, L. , Yan, F. , Yi, J. , Hu, Y. , Liao, Y. , and Xiao, H. (2020), COVID-19: Challenges to GIS with big data, Geography and Sustainability, 1(1), 77-87.
98. Zhu, K. J. , Jing, Y. , and Chang, D. Y. (1999), A discussion on extent analysis method and applications of fuzzy AHP, European Journal of Operational Research, 116(2), 450-456.
 Do not open for a day Close