Macro and micro geo-spatial environment consideration for landfill site selection in Sharjah, United Arab Emirates

Sanitary municipal landfill site selection by integration of GIS and multi-criteria techniques for environmental sustainability in Safita area, Tartous governorate, Syria

Urban waste disposal is a problem that poses a major challenge to city planners as a result of rapid population growth and urbanization. Finding suitable sites for solid waste is one of the most important solutions developed globally to manage this problem. In this regard, a set of physical, socio-economic and technological criteria must be considered to tackle the problem. Safita area (Tartous governorate) witnessed a rapid population growth during the decade of the war in Syria due to the onrush of internal refugees, which resulted in several environmental problems, including random waste dumps. After perusing the previous literature and considering expert opinions, a map of the spatial suitability of sustainable waste sites in the Safita area was developed by integrating the multi-criteria decision- making methodology (analytic hierarchy process) with the geographic information system. Thirteen criteria, including elevation, slope, permeability, distance to faults, distance to settlement, land use/land cover, distance to drainage, distance to water supplies, distance to lakes, distance to road, distance from tourist centers, distance from archaeological centers, and distance from religious centers, were used to achieve the goal of this study. The layer maps for these criteria were developed based on various data sources, including conventional and remote sensing data. Potential landfill sites were identified and divided into five categories: unsuitable (83.28%), less suitable (8.49%), moderately suitable (4.49%), highly suitable (2.57%), and very highly suitable (0.72%). The results of this study provide reliable spatial outputs that will help in suggesting new landfill sites that maintain environmental and socio-economic sustainability in the post-war phase. Moreover, the application of the methodology of this study can be generalized to the rest of the regions in Syria within the framework of the integrated management of the problem of random landfills.

A GIS-based multi-criteria decision-making method for the selection of potential municipal solid waste disposal sites in Mersin, Turkey

Due to rapid urbanization and the resulting rapid population increases, an important problem for cities today is the elimination of solid waste or finding suitable places for waste storage. Municipal solid waste disposal (MSWD) site selection is one of the most important steps in urban waste management. Many criteria political, economic, social, and technological should be considered in this process. Geographic information systems (GIS) and multi-criteria decision-making (MCDM) are tools that are superior to traditional methods in the planning phase of site selection studies. In this study, suitable MSWD sites were determined in Mersin (a Turkish province) based on GIS and the analytic hierarchy process, an MCDM method. Unsuitable areas in the study were removed at the beginning of the analysis. Eleven evaluation criteria were selected: elevation, slope, permeability, distance from lineaments, groundwater level, distance from rivers and water surfaces, distance from roads, distance from settlements, distance from protected areas, and land cover. Considering the evaluation and exclusion criteria, 19.12% of the study area was deemed suitable, and 80.88% was determined unsuitable for an MSWD site. An MSWD suitability map was created as a result of the study. The outcomes indicate that 80,377 ha and 83,022 ha of the study area were classified as high and very high suitability, respectively. Based on these results, we evaluate whether the locations of existing solid waste landfills are appropriate and propose alternative solid waste landfills for each district.

Waste to energy spatial suitability analysis using hybrid multi-criteria machine learning approach

Municipal solid waste is typically managed in developing countries through various disposal methods, such as sanitary landfills or dumpsites. Alternatively, waste to energy (WTE) systems have been recently adopted to provide sustainable waste management and diversify the energy mix. The abundance of remotely sensed datasets and derivatives, along with the rapid development of artificial intelligence, can offer an effective solution for WTE site selection. In this study, an analytical hierarchy process (AHP)-based framework supported by multiple machine learning algorithms (gradient boosted tree (GBT), decision tree (DT), and support vector machines (SVMs)) was established to explore the optimum location for WTE facilities. Various social, legal, environmental, economic, morphological, and land cover parameters were considered under 11 thematic geospatial raster layers. The proposed framework was applied to the 1.5-million-capita city of Sharjah, United Arab Emirates. A novel approach was developed to incorporate Gaussian dispersion modeling for the expected air pollution emissions from a WTE facility. The results showed that the accuracy performance sequence of the algorithms was 94.6, 93.9, and 91.8% for GBT, DT, and SVM, respectively. It was found that the distance from existing landfills had the most critical impact on the optimum location of the WTE facility, followed by the distance from coastline and elevation. The AHP consistency check revealed an acceptable overall criteria consistency index and the ratio of 0.0344 and 0.019, respectively. The results showed that 16.6% of Sharjah was considered extremely highly suitable areas. This research supports decision-makers in developing local guidelines for siting WTE facilities and determining the most suitable locations for such projects.

Insights for Landfill Site Selection Using GIS: A Case Study in the Tanjero River Basin, Kurdistan Region, Iraq

The increasing world population and the growing quantity of solid waste have become a challenging problem facing governments and policy makers because of the scarcity of suitable sites for new landfills and the negative perception of these sites by the people. This study aims to evaluate the performance of different Multi-Criteria Decision-Analysis (MCDA) approaches using remote sensing and Geographic Information System (GIS) data for identifying suitable landfill sites (LFSs). We evaluated the methodologies used by various investigators and selected appropriate ones as suitable sites for Municipal Solid Waste (MSW) landfill in the Tanjero River Basin (TRB) in the Iraqi Kurdistan region. We applied Boolean Overlay (BO), Weighted Sum Method (WSM), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP), and Technique for Order Performance by Similarity to an Ideal Solution (TOPSIS) to allow combined use of 15 thematic layers as predictive factors (PFs). In this study, we applied the Topographic Position Index (TPI) for the first time to select MSW LFSs. Almost all methods showed reliable results and we identified eight suitable sites situated in the western part of the TRB having total area of ~18.35 km2. The best accuracy was achieved using the AHP approach. This paper emphasizes that the approach of the used method is useful for selecting LFSs in other areas, which are located in similar environments.

A worldwide meta-analysis review of restriction criteria for landfill siting using geographic information systems

One of the most crucial parts of solid waste management is determining landfill site location, since multiple factors must be considered and there is no universal formula. The main purpose of this study is to make a worldwide systematic review of restriction criteria used for landfill siting using geographic information systems (GIS). Literature from the last years was thoroughly assessed, and 45 restrictions found were classified as environmental, economic, or social criteria. Our findings show that although the number of articles published has increased recently, they use on average seven restrictions, focusing mainly on environmental over economic and social criteria. In our boxplot statistical analysis, the most frequently used environmental restrictions are the distance from surface water resources (used in 77% of articles), slope (52%), and distance from groundwater founts (40%), with a median of 300 m, 20%, and 250 m, respectively. The most frequently used economic restrictions are distances from roads (60%), airports (40%), and power lines (18%), with medians of 275 m, 3000 m, and 75 m, respectively. The most frequently used social restrictions are distances from urban areas (45%), settlements and residential areas (40%), and cultural heritage or archaeological areas (23%), with medians of 1000 m. This information might help, on the one hand, governments to develop new legislation about landfill siting and on the other hand, decision-makers and scientists to produce new studies with different restrictive scenarios.

Analyzing territory for the sustainable development of solar photovoltaic power using GIS databases

Solar energy generated by grid-connected photovoltaic (GCPV) systems is considered an important alternative electric energy source because of its clean energy production system, easy installation, and low operating and maintenance costs. This has led to it becoming more popular compared with other resources. However, finding optimal sites for the construction of solar farms is a complex task with many factors to be taken into account (environmental, social, legal and political, technical-economic, etc.), which classic site selection models do not address efficiently. There are few studies on the criteria that should be used when identifying sites for solar energy installations (large grid-connected photovoltaic systems which have more than 100 kWp of installed capacity). It is therefore essential to change the way site selection processes are approached and to seek new methodologies for location analysis. A geographic information system (GIS) is a tool which can provide an effective solution to this problem. Here, we combine legal, political, and environmental criteria, which include solar radiation intensity, local physical terrain, environment, and climate, as well as location criteria such as the distance from roads and the nearest power substations. Additionally, we use GIS data (time series of solar radiation, digital elevation models (DEM), land cover, and temperature) as further input parameters. Each individual site is assessed using a unique and cohesive approach to select the most appropriate locations for solar farm development in the Valencian Community, a Spanish region in the east of Spain.

GIS-based MCDM modeling for landfill site suitability analysis: A comprehensive review of the literature

One of the cheapest and proper methods for the ultimate disposal of Municipal Solid Waste (MSW) is landfilling. However, determining the location of landfill sites is a difficult and complex task due to depending on social, environmental, technical, economic, and legal factors. To solve the aforementioned challenges related to the landfill site suitability analysis, the combinations of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) have been studied by academia and applied by experts over the years. This notice is apparent by the large number of academic papers which have been announced in the near future. To provide a framework of the existing literature, and to guide colleagues, a state-of-the-art of recent papers is crucial. The goal of this study is to review all scientific papers in GIS-based MCDM modeling for landfill site suitability analysis in academic journals. A total of 106 studies published between 2005 and 2019 are recorded and surveyed. The studies are then investigated and classified by a generated taxonomy including following categories: GIS software, application area, uncertainty, MCDM techniques, cell sizes in GIS, and criteria. Based on the review conducted, it is observed that while Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) are the most widely used MCDM methods for weighting the criteria and ranking the alternatives, respectively. On the other hand, while environmental dimension is the most commonly preferred main criteria, surface water comes first in the sub-criteria pool. Criteria analysis shows that surface and ground water, geology, land use, distance to fault zone, distance to urban areas, and distance to road and slope are the most commonly used criteria groups among others. These classifications and observations are helpful for identifying research gaps in the current literature and provide insights for future modeling and research efforts in the field.

Dam Site Suitability Mapping and Analysis Using an Integrated GIS and Machine Learning Approach

: Meeting water demands is a critical pillar for sustaining normal human living standards, industry evolution and agricultural growth. The main obstacles for developing countries in arid regions include unplanned urbanisation and limited water resources. Locating and constructing dams is a strategic priority of countries to preserve and store water. Recent advances in remote sensing, geographic information system (GIS), and machine learning (ML) techniques provide valuable tools for producing a dam site suitability map (DSSM). In this research, a hybrid GIS decision-making technique supported by an ML algorithm was developed to identify the most appropriate location to construct a new dam for Sharjah, one of the major cities in the United Arab Emirates. Nine thematic layers have been considered to prepare the DSSM, including precipitation, drainage stream density, geomorphology, geology, curve number, total dissolved solid elevation, slope and major fracture. The weights of the thematic layers were determined through the analytical hierarchy process supported by several ML techniques, where the best attempted ML technique was the random forest method, with an accuracy of 76%. Precipitation and drainage stream density were the most influential factors affecting the DSSM. The developed DSSM was validated using existing dams across the study area, where the DSSM provides an accuracy of 83% for dams located in the high and moderate zones. Three major sites were identified as suitable locations for constructing new dams in Sharjah. The approach adopted in this study can be applied for any other location globally to identify potential dam construction sites.

Suitable map analysis for wind energy projects using remote sensing and GIS: a case study in Turkey

The aim of the study is to create a suitable map for wind energy projects in a rural area. The primary goal here is to show a methodology using automatic object extraction of the target classes of buildings, vegetation, and ground. The secondary goal is to identify the potential effects for wind turbine sites based on four criteria: Wind speed, Slope, Building, and Vegetation using the fuzzy analytical hierarchy process (FAHP). This paper discusses two important situations for wind energy projects. The first strategy is to just determine the best suitable site locations of wind turbines, while the second strategy determines the locations of wind turbines with minimal negative effects on the rural area. The proposed approach is tested using the data obtained from a multi-sensor system in Evrencik, Turkey. In preliminary phases of renewable energy projects, successful results are dependent on evaluating the potential site's suitability with criteria such as social, environmental, physical, and economic conditions. Furthermore, an accuracy analysis is performed on the automatically extracted target classes for the study area, yielding a value of 89% in the remote sensing section of the study. Moreover, for the GIS section of the study, suitable and unsuitable areas are identified, and the suitability levels of the remaining areas are determined for the two strategies. According to the results, 11% of the areas are found to have high, moderate, and low suitability levels, and 89% are unsuitable for the first strategy, whereas these rates are, respectively, 2% and 98% for the second strategy.