Landfill site selection by integrating fuzzy logic, AHP, and WLC method based on multi-criteria decision analysis

Literature analysis of the location selection studies related to the waste facilities within MCDM approaches

The increase in waste and related environmental problems is one of the major problems compromising health and environmental quality in urban and rural areas. There are a number of policies that can be implemented to reduce waste, but since it cannot be completely eliminated, recycling and disposal facilities for waste will always be required. Researchers and professionals are currently grappling with the issue of where to locate waste facilities. In the light of all this information, a literature review is presented so that researchers can easily access and systematically review previous studies on the waste facility location selection problem. At this point, in order to reduce the reviewed studies to a reasonable level and to conduct a more organized research, this literature research has conducted within the framework of multi-criteria decision-making (MCDM) approaches, which is one of the most applied methods in location selection problems. The subsequent strengths, weaknesses, opportunities, and threats (SWOT) analysis delves into the strengths, weaknesses, opportunities, and threats in the field, offering a concise guide for future research in waste facility location selection problem. The SWOT analysis highlights the strengths of global environmental awareness and versatile MCDM approaches, while addressing weaknesses in emerging technology integration and potential biases. Opportunities for interdisciplinary collaboration and integration of sustainability metrics provide strategic pathways, but threats such as regulatory changes and limited funding underscore challenges. This analysis serves as a concise guide for future research in waste facility location selection.

A comprehensive review on the integration of geographic information systems and artificial intelligence for landfill site selection: A systematic mapping perspective

Properly selecting landfill sites for waste disposal is crucial for mitigating environmental and public health risks. Geographic Information Systems (GISs) and Artificial Intelligence (AI) techniques have emerged as valuable tools for identifying suitable landfill locations. This study presents a systematic mapping study (SMS) that investigates the usage of GIS and AI in landfill site selection. We searched six databases (IEEE Xplore, ACM Digital Library, Science Direct, Emerald Insight, Taylor & Francis Online and Web of Science) using predefined keywords related to landfills, GIS and AI. From 858 initially retrieved articles, we selected 48 relevant articles for in-depth analysis. Our research aimed to answer various questions, such as publication trends, the geographic distribution of case studies, criteria for assessing landfill suitability, tools and techniques employed, preliminary site screening methods, decision-making processes, limitations and future research directions. We used bubble charts, bar charts and tables to visualize the results. The findings of our study highlight the growing interest in using GIS and AI for landfill site selection and emphasize the importance of incorporating multi-criteria decision-making techniques. Furthermore, the results reveal the need for developing more advanced AI models, addressing the limitations of current approaches and exploring novel visualization techniques for enhancing landfill site selection processes. This study provides valuable insights for researchers and practitioners in waste management, environmental science and geoinformatics. It sets the groundwork for future research on improving GIS- and AI-based landfill site selection methodologies.

Evaluating the effect of the COVID-19 pandemic on medical waste disposal using preference selection index with CRADIS in a fuzzy environment

The COVID-19 pandemic has caused a surge in essential medical supplies usage, leading to a notable increase in medical waste generation. Consequently, extensive research has focused on sustainable disposal methods to handle used medical equipment safely. Given the necessity to evaluate these methods considering qualitative and quantitative criteria, this falls within the realm of multi-criteria decision-making (MCDM). This study introduces a framework for selecting the most suitable medical waste treatment methods, taking into account economic, technological, environmental, and social aspects. Sixteen criteria were assessed using the Fuzzy Preference Selection Index (F-PSI) to determine the optimal waste disposal approach. Additionally, the Fuzzy Compromise Ranking of Alternatives from Distance to Ideal Solution (F-CRADIS) method was employed to evaluate nine technologies for medical waste disposal. Notably, disinfection efficiency emerged as the most crucial criterion, with autoclaving identified as the preferred method for medical waste treatment. A practical case study conducted in Sivas, Turkey, validates the feasibility of these strategies. Multiple sensitivity analyses were performed to ensure the stability and reliability of the proposed approach.

Landfill site selection using MCDM methods and GIS in the central part of the Nile Delta, Egypt

One of the most prevalent and serious issues afflicting developing countries is the lack of adequate space for waste disposal. Al-Gharbia Governorate, located in the middle of the Nile Delta in Egypt, suffers from random selection of sites for solid waste disposal, resulting in significant environmental challenges. The aim of this study is to determine optimal landfill locations within Al-Gharbia Governorate and validate the existing landfill sites. Four techniques of multi-criteria decision-making (MCDM) were applied to generate suitability maps for the Governorate: the analytical hierarchy procedure (AHP), ratio scale weighting (RSW), straight rank sum (SRS), and Boolean method. Eleven effective criteria were considered: groundwater, surface water, elevation, slope, soils, land use, roads, railways, urban areas, villages, and power lines. The suitability maps were categorized into four different classes: suitable, moderately suitable, low suitable, and unsuitable. The latest suitability map was determined by combining the results from the different methods, providing decision-makers with the means to select the optimal landfill site. The suitable zone encompasses a small area (3%), predominantly located in the northeast region (Al-Mahalla), central region (Tanta), and northern region (Kotour). Conversely, the unsuitable area covers a substantial portion (72.7%) due to the agricultural nature of the governorate, high population density, and elevated groundwater levels. Furthermore, all existing landfill sites fall within unsuitable or low suitable areas, inflicting severe impacts on the nearby environment, public health, and groundwater integrity.

Integration of LCSA and GIS-based MCDM for sustainable landfill site selection: a case study

The paper aims at developing a framework for decision-support to select a sustainable landfill site in Bardaskan City (Iran) by combining life cycle sustainability assessment (LCSA) concepts and geographic information system (GIS)-based multi-criteria decision-making (MCDM). Overall, 13 criteria were chosen (three constraints and 10 factors) and classified into three main aspects of sustainability (i.e., environmental, social, and economic) to achieve the research goals. Boolean and fuzzy logic were employed to standardize the classified constraints and factors, respectively. Analytic hierarchy process (AHP) was used to calculate the factors' weights and then suitability maps were produced using the GIS analysis. The layers were combined using simple additive weighting (SAW). Next, the most sustainable sites were obtained. The results indicated that distance from city backline, groundwater depth, and distance from rural areas were the most significant factors with the weight of 0.338, 0.141, and 0.129, respectively. The final map of suitable sites was created by classifying the SAW layer according to 75, 80, and 85% of suitability to show the high, medium, and low priority areas for landfill site selection, respectively. Therefore, integration of LCSA and GIS-based MCDM to select the sustainable landfill site for municipal solid waste (MSW) is highly important, which can be effectively employed in regional and urban planning to select the location of appropriate and sustainable landfills.

Sanitary landfill site selection using GIS-based on a fuzzy multi-criteria evaluation technique: a case study of the City of Kraljevo, Serbia

Adequate disposal of municipal solid waste (MSW) is one of Serbia's most complex environmental challenges. The problem is more serious in urban areas, since large amounts of waste are disposed of in locations that do not comply with environmental, technical, and socio-economic standards. Such is the case for the city of Kraljevo, where about 116,000 inhabitants do not have a sanitary landfill facility. This research includes a multi-criteria analysis, conducted with the help of geographic information systems, to find a suitable landfill site location. After data collection, the first step was to process 15 environmental and socio-economic factors utilizing the fuzzy analytic-hierarchy process method. The second step comprised the visual analysis and selection of the ten most suitable locations from the synthetic convenience map. The third step involved the final ranking of sites by means of the fuzzy multi-objective analysis by ratio, plus the full multiplicative form method, based on four additional beneficial and non-beneficial criteria. The results show that sanitary landfill candidate site A4 is the most suitable location for constructing a sanitary landfill site due to its large area (569 ha) and relatively short distance from the urban zone (8 km). This study is the first to integrate geographic information systems and the fuzzy analytic-hierarchy process, multi-objective analysis by ratio, and the full multiplicative form algorithm for sanitary landfill selection. The results of the research can be used as a reference for safe waste disposal in the city of Kraljevo.

Concentration of heavy metals in leachate, soil, and plants in Tehran's landfill: Investigation of the effect of landfill age on the intensity of pollution

Heavy metals are one of the important pollutants in landfill leachate. Plants and soil near the landfill may be contaminated by leachate. In this study, by evaluating the heavy metals in the leachate of two landfills and the soil and plants near them, the amount of pollution caused by the leachate in the environment around the landfills in Tehran was investigated. This study was conducted in three stations, soil and plant samples were prepared according to a specific protocol. Also, three indexes including PI, PI_Nemerow, and BF were used to interpret the results. The results showed that the concentration of total metals in the old landfill leachate and new landfill leachate was only 12% different and was 24.13 mg/L on average. In the new landfill leachate, iron had the highest concentration among metals, which was 22.94 and 17.01 mg/L in two samples. In the old landfill leachate, the concentration of manganese was 15.71 mg/L, which was the highest among the studied metals. The concentration of heavy metals in the soil of the old landfill was 24.6% lower than the concentration of metals in the soil of the new landfill. In all samples, the highest metal concentration in the soil was related to manganese, which was 33.65-34.14 mg/L. Cadmium had the lowest concentration in soil compared to other metals. The concentration of total metals in the studied plants was 29-60 ppm. The PInemerow for studied stations was 0.1711, 0.1708, and 0.1463. The highest PI in the case of lead was observed at the second station equal to 0.54. The highest BF in case of Atriplex Undulata was more than 6 and related to cadmium, while the highest BF in case of Atriplex Cinearea was more than 3.5 related to cadmium. This study showed that the soil and plants of the landfill were contaminated with heavy metals under the influence of leachate, and the ability of plants to uptake and accumulate metals can be used to manage soil pollution near the landfill.

Estimating solid waste generation and suitability analysis of landfill sites using regression, geospatial, and remote sensing techniques in Rangpur, Bangladesh

Municipal solid waste (MSW) management has been a growing problem in fast-developing cities. A considerable amount of solid waste is generated daily and disposed anywhere, which creates an unhealthy environment. This study aims to develop a model to determine household solid waste (HSW) generation using multiple linear regression and identify suitable landfill sites to ensure proper MSW disposal in Rangpur City, Bangladesh. Socioeconomic variables data like average monthly income, educational level, family size, age of family head, and average HSW generation per day were collected from 381 respondents through stratified random sampling with a 95% confidence level. Multi-criteria decision analysis (MCDA) was performed using variables like surface water, slope, road network, and land use through GIS and remote sensing to find suitable landfill sites. Results of the model show no multicollinearity as the variance inflation factor was estimated to be less than 2 for each independent variable. Furthermore, the model provides a moderate overall fit because of the coefficient of determination (R² = 0.661), which denotes the independent variables' predictive capability. The results also demonstrate that family size and education are the most critical variables in predicting waste generation because of the values of coefficients 122.39 and - 184.72, respectively. This study also illustrated suitable landfill sites through MCDA, which can be a useful resource for the city authority to ensure environmental sustainability by implementing effective strategies for proper MSW management.

Using of geographic information systems (GIS) to determine the suitable site for collecting agricultural residues

The main aim of this study is to use the Geographic Information Systems (GIS) techniques to determine the optimum site to collect the residues in order to reduce cost and increase the benefits. To achieve these three scenarios were studied to reach the best collection sites for recycling rice straw in Sinbilawin center. The results indicate that the first scenario: The result was forty (40) collection sites in this status the cost of transfer will be very high because the collecting starts from inside village to the 40 sites and transfer to main sites to recycle operation. The second scenario: The total lengths of roads are not much then the cost of transfer is low and save time and efforts. The third scenario: The result was five collecting sites. It was shortest length and lowest cost. Transportation costs in the first scenario were difficult to calculate because of the difficulty to access a network of documented roads from satellite maps to use it with the GIS program. The total internal transport costs were 987,308.86 and 826,966.43 L.E (Egyptian pound, $ = 19.15 L.E) for second and third scenarios, respectively. The average transport costs per ton were 17 and 14 L.E/ton for the second and third scenarios, respectively. Also, the total lengths of roads were 817.62 and 615.65 km for the second and third scenarios, respectively.

Landscape Planning Integrated Approaches to Support Post-Wildfire Restoration in Natural Protected Areas: The Vesuvius National Park Case Study

In recent decades in the Mediterranean basin there has been a considerable increase in both the number of wildfires and the extent of fire-damaged areas, resulting in ecological and socio-economic impacts. Protected areas are particularly vulnerable and many characteristics underpinning their legal protection are threatened. Several studies have been devoted to mitigating wildfire risks inside the protected areas, however often only in regard to natural heritage losses. Based on the adaptive wildfire resilience approaches, this work proposes a framework of actions that integrates natural, social and economic components. Starting from the Vesuvius National Park case study, affected by wildfires in 2017, the paper proposes a framework of action, envisaging two main phases: (i) the identification of priority intervention areas, by means of spatial multicriteria decision analysis, and (ii) damage assessment by using a monetary approach to value ecosystem services (ESs). The results identified priority areas where to concentrate economic and material resources, and estimated ecosystems damage, demonstrated ESs losses in areas adjacent to the burnt ones. This work, by integrating the relation between environmental sciences and policy, underpins a medium-long term development planning process. The aim of this work is to support landscape management and planning that includes socio-economic components such as sustainable development measures.

GIS-based multicriteria decision analysis for settlement areas: a case study in Canik

In addition to global population growth due to migration from rural areas to urban areas, population density is constantly increasing in certain regions, thereby necessitating the introduction of new settlements in these regions. However, in the selection of settlement areas, no sufficient preliminary examinations have been conducted; consequently, various natural disasters may cause significant life and property losses. Herein, the most suitable settlement areas were determined using GIS (geographic information systems) in Canik District, where the population is continuously increasing. Therefore, this study aimed to incorporate a new perspective into studies on this subject. Within the scope of the study, landslide and flood risks, which are among the most important natural disasters in the region, were primarily evaluated, and high-risk areas were determined. Elevation, slope, aspect, curvature, lithology, topographic humidity index (TWI), and proximity to river parameters were used to produce flood susceptibility maps. A digital elevation model (DEM) of the study area was produced using contours on the 1/25,000 scaled topographic map. The elevation, slope, aspect, curvature, and TWI parameters were produced from the DEM using the relevant analysis routines of ArcGIS software. The raster map of each parameter was divided into 5 subclasses using the natural breaks classification method. In the reclassified raster maps, the most flood-sensitive or flood-prone subclasses were assigned a value of 5, and the least sensitive subclasses were assigned a value of 1. Then, the reclassified maps of the 7 parameters were collected using the "map algebra" function of ArcGIS 10.5 software, and the flood susceptibility index (FSI) map of the study area was obtained. The flood susceptibility map of the study area was obtained by dividing the FSI into 5 subclasses (very low, low, moderate, high, and very high) according to the natural breaks classification method. Thereafter, suitable and unsuitable areas in terms of biocomfort, which affects people's health, peace, comfort, and psychology and is significant in terms of energy efficiency, were determined. At the last stage of the study, the most suitable settlement areas that were suitable in terms of both biocomfort and low levels of landslide and flood risks were determined. The calculated proportion of such areas to the total study area was only 2.1%. Therefore, because these areas were insufficient for the establishment of new settlements, areas that had low landslide and flood risks but were unsuitable for biocomfort were secondarily determined; the ratio of these areas was calculated as 56.8%. The remaining areas were inconvenient for the establishment of settlements due to the risk of landslides and floods; the ratio of these areas was calculated as 41.1%. This study is exemplary in that the priority for the selection of settlement areas was specified, and this method can be applied for selecting new settlements for each region considering different criteria. Due to the risk of landslides or flooding in the study area, the areas unsuitable for establishing a settlement covered approximately 41.1% of the total study area. The areas that had low flood and landslide risks but were suitable for biocomfort constituted only 2.1% of the study area. In approximately 56.8% of the study area, the risk of landslides or floods was low, and these areas were unsuitable in terms of biocomfort. Therefore, these areas were secondarily preferred as settlement areas. The most suitable areas for settlements constituted only 0.19% of the total study area, and these areas will not be able to meet the increasing demand for settlement area. Therefore, it is recommended to select areas that do not have the risk of landslides and floods but are unsuitable for biocomfort. This study reveals that grading should be performed in the selection of settlement areas. When choosing a settlement area in any region, possible natural disasters in the region should be identified first, and these disasters should be ordered in terms of their threat potential. Moreover, biocomfort areas suitable for settlements should be considered. In the next stages of settlement area selection, the criteria that affect the peace and comfort of people, such as distance to pollution sources, distance to noise sources, and proximity to natural areas, should also be evaluated. Thus, a priority order should be created for the selection of settlement areas using various other criteria.

A GIS-based novel approach for suitable sanitary landfill site selection using integrated fuzzy analytic hierarchy process and machine learning algorithms

Disposal of waste without treatment is the least preferable way of sustainable solid waste management (SWM). But most cities in developing nations still use open dumps, causing negative impacts on the environment and human health. This study offered a novel approach for selecting landfill sites and sustainable SWM in Aligarh city, India. This was done through data collection, selecting models for criterion weighting, and validation. In order to prepare a landfill site suitability map, a geographic information system (GIS)-based ensemble fuzzy analytic hierarchy process-support vector machine (FAHP-SVM) and fuzzy analytic hierarchy process-random forest (FAHP-RF) models were implemented. Considering the previous studies and the study area characteristics, eighteen thematic layers were selected. The result revealed that land value; distance from residential roads, hospitals and clinics, and waste bins; and normalized difference built-up index (NDBI) have a fuzzy weight greater than 0.10, indicating significant factors. In contrast, land elevation, land slope, surface temperature, soil moisture index, normalized difference vegetation index (NDVI), and urban classification have a zero fuzzy weight, indicating these criteria have no importance. The result further revealed that FAHP-RF with an area under curve (AUC) value of 0.91 is the more accurate model than FAHP-SVM. According to the final weight-based overlay result, seven potential landfill sites were identified, out of which three were determined as most suitable by considering current land cover, public opinions, and environmental and economic concerns. This research proposed a zonal division model based on landfill sites location for sustainable SWM in Aligarh city. However, the findings may provide a guideline to the decision-makers and planners for optimal landfill site selection in other cities of developing countries.

A Cluster-based Stratified Hybrid Decision Support Model under Uncertainty: Sustainable Healthcare Landfill Location Selection

Nowadays, healthcare waste management has become one of the significant environmental, health, and social problems. Due to population and urbanization growth and an increase in healthcare waste disposals according to the growing number of diseases and pandemics like COVID-19, disposal of healthcare waste has become a critical issue. Authorities in big cities require reliable decision support systems to empower them to make strategic decisions to provide safe disposal methods with a prospective vision. Since inappropriate healthcare waste management systems would definitely bring up dangerous environmental, social, health, and economic issues for every city. Therefore, this paper attempts to address the landfill location selection problem for healthcare waste using a novel decision support system. Novel decision support model integrates K-means algorithms with Stratified Best-Worst Method (SBWM) and a novel hybrid MARCOS-CoCoSo under grey interval numbers. The proposed decision support system considers waste generate rate in medical centers, future unforeseen but potential events, and uncertainty in experts’ opinion to optimally locate required landfills for safe and economical disposal of dangerous healthcare waste. To investigate the feasibility and applicability of the proposed methodology, a real case study is performed for Mazandaran province in Iran. Our proposed methodology could efficiently deal with 79 medical centers within 4 clusters addressing 9 criteria to prioritize candidate locations. Moreover, the sensitivity analysis of weight coefficients is carried out to evaluate the results. Finally, the efficiency of the methodology is compared with several well-known methods and its high efficiency is demonstrated. Results recommend adherence to local rules and regulations, and future expansion potential as the top two criteria with importance values of 0.173 and 0.164, respectively. Later, best location alternatives are determined for each cluster of medical centers.

Evaluation of Resilience in Historic Urban Areas by Combining Multi-Criteria Decision-Making System and GIS, with Sustainability and Regeneration Approach: The Case Study of Tehran (IRAN)

Historic urban areas are the beating heart of the city, but neglecting them can lead to low resilience. Therefore, paying attention to their regeneration can create a sustainable city. The purpose of this study was to determine the resilience of neighborhoods in Tehran and evaluate effective criteria for the resilience increase. In this study, to evaluate the resilience of Tehran, initially, 18 criteria were considered. Then, using the Delphi technique, 14 criteria among them were selected for final analysis. Using the AHP multi-criteria decision-making method, the importance of each criterion was determined. Using GIS capabilities, the parameters map was prepared, and by combining the prepared maps with AHP weights, a resilience map was created. Finally, 20 neighborhoods with the lowest resilience were identified as priorities for stabilization and regeneration measures, and the criteria status used in them was examined. Results showed that deteriorated urban areas (19.53%) and construction materials (18.51%) were the most important criteria. Non-resilience areas were generally in the southern half of the city. 78% of 20 selected neighborhoods had deteriorated urban areas, while only 14% of the city deteriorated. Finally, by examining the criteria in neighborhoods with the lowest resilience, suggestions were made to regeneration, sustainability, and increase the resilience of these neighborhoods.

Odor-aided analysis for landfill site selection: study of DOKAP Region, Turkey

In our world, with the increase of factors such as the rapid and irresponsible consumption of natural resources, man-made environmental disasters, global warming, and pollution of water resources in our world, the need for more efficient storage and disposal of solid waste has arisen. The presentation of the data required to solve spatial problems such as storage, management, and location selection can be carried out extensively and effectively using geographic information systems (GIS). On the other hand, the unsatisfactory results obtained with GIS recently have made it mandatory to use spatial multiple criteria decision-making (S-MCDM) methods that include the decision-makers in the process. In this study, landfill site selection was carried out in eight provinces in the region under the responsibility of the Eastern Black Sea Project Regional Development Administration (DOKAP). GIS and S-MCDM were used together in this site selection process. A total of eight spatial data layers were used in the site selection application. Afterwards, storage areas determined as suitable via GIS analysis underwent additional evaluation, taking into account geological, seismic, and environmental factors as well as transportation costs. In addition to these multi-component evaluations, odor analyses were carried out on the proposed storage areas using the prevailing wind direction.

Assessment of foot-and-mouth disease risk areas in mainland China based spatial multi-criteria decision analysis

Background

Foot-and-mouth disease (FMD) is a highly contagious viral disease of cloven-hoofed animals. As a transboundary animal disease, the prevention and control of FMD are important. This study was based on spatial multi-criteria decision analysis (MCDA) to assess FMD risk areas in mainland China. Ten risk factors were identified for constructing risk maps by scoring, and the analytic hierarchy process (AHP) was used to calculate the criteria weights of all factors. Different risk factors had different units and attributes, and fuzzy membership was used to standardize the risk factors. The weighted linear combination (WLC) and one-at-a-time (OAT) were used to obtain risk and uncertainty maps as well as to perform sensitivity analysis.

Results

Four major risk areas were identified in mainland China, including western (parts of Xinjiang and Tibet), southern (parts of Yunnan, Guizhou, Guangxi, Sichuan and Guangdong), northern (parts of Gansu, Ningxia and Inner Mongolia), and eastern (parts of Hebei, Henan, Anhui, Jiangsu and Shandong). Spring is the main season for FMD outbreaks. Risk areas were associated with the distance to previous outbreak points, grazing areas and cattle density. Receiver operating characteristic (ROC) analysis indicated that the risk map had good predictive power (AUC=0.8634).

Conclusions

These results can be used to delineate FMD risk areas in mainland China, and veterinary services can adopt the targeted preventive measures and control strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-021-03084-5.

A novel MADM algorithm for landfill site selection based on q-rung orthopair probabilistic hesitant fuzzy power Muirhead mean operator

With the rapid development of economy and the acceleration of urbanization, the garbage produced by urban residents also increases with the increase of population. In many big cities, the phenomenon of "garbage siege" has seriously affected the development of cities and the lives of residents. Sanitary landfill is an important way of municipal solid waste disposal. However, due to the restriction of social, environmental and economic conditions, landfill site selection has become a very challenging task. In addition, landfill site selection is full of uncertainty and complexity due to the lack of cognitive ability of decision-makers and the existence of uncertain information in the decision-making process. Therefore, a novel multi-attribute decision making method based on q-rung orthopair probabilistic hesitant fuzzy power weight Muirhead mean operator is proposed in this paper, which can solve the problem of landfill site selection well. This method uses probability to represent the hesitance of decision maker and retains decision information more comprehensively. The negative effect of abnormal data on the decision result is eliminated by using the power average operator. Muirhead mean operator is used to describe the correlation between attributes. Then, an example of landfill site selection is given to verify the effectiveness of the proposed method, and the advantages of the proposed method are illustrated by parameter analysis and comparative analysis. The results show that this method has a wider space for information expression, gives the decision maker a great degree of freedom in decision-making, and has robustness.

Coupling Fuzzy Multi-Criteria Decision-Making and Clustering Algorithm for MSW Landfill Site Selection (Case Study: Lanzhou, China)

The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results. In this study, a coupled fuzzy Multi-Criteria Decision-Making (MCDM) approach was employed to site landfills in Lanzhou, a semi-arid valley basin city in China, to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30 m resolution, then criteria weights were obtained by DEMATEL and ANP, and the optimal fuzzy membership function was determined for each evaluation criterion. Combined with GIS spatial analysis and the clustering algorithm, candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics were analyzed. These sites were subsequently ranked utilizing the MOORA, WASPAS, COPRAS, and TOPSIS methods to verify the reliability of the results by conducting sensitivity analysis. This study is different from the previous research that applied the MCDM approach in that fuzzy MCDM for weighting criteria is more reliable compared to the other common methods.