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Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information System-based Multi-Criteria Decision Analysis methods—the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89.80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations.

Improper disposal of solid waste obstructs drainage systems and pollutes surface water. Additionally, the dumping of unsorted garbage generates emissions and leachate, which harm local ecosystems and contribute to climate change. With Rewari City’s growing population, effective municipal solid waste management, including landfill site selection, is crucial. This study employs Geographic Information System (GIS), Analytical Hierarchical Process (AHP), and Weighted Linear Combination (WLC) methodologies to determine appropriate sites for landfills. The FAO, ALOS PALSAR DEM, Sentinel 2B images, Google Earth Pro, and interviews were employed to gather data. The results of the Analytic Hierarchy Process (AHP) indicate that 35.4% of the parameters under consideration are associated with Land Use Land Cover (LULC), whereas roads rank as the second most significant criterion, accounting for 24.0%. The WLC technique determined that 4.65 square kilometers were inappropriate for dump sites, while 0.11 square kilometers were extremely favorable. These findings can assist decision-makers in determining the order of importance for variables when selecting a landfill location.

Landfill has been taken to the bottom of the hierarchy of options for waste disposal but has been the most used method for urban solid waste disposal. However, landfill has become more difficult to implement because of its increasing cost, community opposition, and more restrictive regulations regarding the siting and operation of landfills. Land is a finite and scarce resource that needs to be used wisely. Appropriate allocation of landfills involves the selection of areas that are suitable for waste disposal. The present work describes a type of multi-criteria evaluation (MCE) method called weighted linear combination (WLC) in a GIS environment to evaluate the suitability of the study region for landfill. The WLC procedure is characterized by full tradeoff among all factors, average risk and offers much flexibility than the Boolean approaches in the decision making process. The relative importance weights of factors are estimated using the analytical hierarchy process (AHP). In the final aggregated suitability image, zones smaller than 20 hectares are eliminated from the allocation process. Afterwards, the land suitability of a zone is determined by calculating the average of the suitability of the cells belonging to that zone, a process called zonal land suitability. The application of the presented method to the Gorgan city (Iran) indicated that there are 18 zones for landfill with their zonal land suitability varying from 155.426117 to 64.149024. The zones were ranked in descending order by the value of their zonal land suitability. The results showed the use of GIS as a decision support system (DSS) available to policy makers and decision makers in municipal solid waste (MSW) management issues.

Landslides have a profound impact on landscape geology, resulting in extensive devastation and loss of human lives. Mapping landslide susceptibility is crucial for effective land use planning in mountainous country like Ethiopia. This study was conducted in the upper Didessa sub-basin, southwestern parts of Ethiopia using Geographic Information System (GIS) and multi criteria evaluation (MCE) technique. This study employed a blend of primary data, encompassing field surveys and interviews with experts, as well as secondary data derived from diverse source, such as remote sensing data, digital soil maps, and geological maps. A total of eleven critical factors were employed to assess the triggers of landslides. These factors include slope, aspect, drainage density, topographic wetness index (TWI), stream power index (SPI), topographic ruggedness index (TRI), hypsometric integral, lithology, land use land cover (LULC), soil texture, and distance from roads. The analytical hierarchy process (AHP) method was used to determine the significance of each indicator through pairwise comparison matrix. The study area was categorized into different zones based on the susceptibility to landslides, namely very high, high, moderate, low, and very low. Results revealed that cultivated land had the highest likelihood of experiencing landslides, with a total of nine incidents out of 25, followed by built-up areas with seven landslides. Conversely, dense forests, sparse forests, and grazing land experienced a lower likelihood of landslides. Out of the 11 factors contributing to landslides, 24% of the surveyed region was deemed to have a moderate susceptibility, with 12% and 6% falling into the categories of high and very high susceptibility to landslides, respectively. The findings of this research provide important information for policymakers to develop efficient measures for preventing and reducing the risks of landslides.

The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of entropy (IOE), weighted linear combination (WLC)] and machine learning [support vector machine (SVM)] models for estimating landslide susceptibility at the Wuning area, China. A total of 445 landslides were randomly classified into 70% (311 landslides) and 30% (134 landslides) to train and validate landslide models, respectively. Fourteen landslide conditioning factors including slope angle, slope aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, NDVI, land use, rainfall, distance to road, distance to river and distance to fault were then studied for landslide susceptibility assessment. Performances of five studied models were evaluated using area under the ROC curve (AUROC) for training (success rate curve) and validation (prediction rate curve) datasets, statistical-based measures and tests. Results indicated that the area under the success rate curve for the FR, IOE, WLC, SVM and SMCE models was 88.32%, 82.58%, 78.91%, 85.47% and 89.96%, respectively, demonstrating that SMCE could provide the higher accuracy. The prediction capability findings revealed that the SMCE model (AUC = 86.81%) was also the highest approach among the five studied models, followed by the FR (AUC = 84.53%), the SVM (AUC = 81.24%), the IOE (AUC = 79.67%) and WLC (73.92%) methods. The landslide susceptibility maps derived from the above five models are reasonably accurate and could be used to perform elementary land use planning for hazard extenuation.

Solid waste management is a serious problem in most cities of the world due to rapid urban expansion and it causes increasing solid waste generation. The practice of solid waste management in Robe town was very poor and it is one of the chronic problems of the town. Therefore, the town needs a suitable landfill site to properly manage solid wastes and mitigate its impacts on public health and environment. The purpose of the study is to identify suitable landfill sites in Robe town, Ethiopia, that is socially and environmentally acceptable, and economically feasible by applying geographic information system and multi-criteria decision analysis and evaluation techniques. This study was based on factor criteria thematic layers of land-use and land-cover types, groundwater depth, lineament, soil permeability, river, water pipelines, slope, main and secondary roads, and constraints thematic maps of boreholes, built-up areas, and green areas. The analytical hierarchy process pair-wise comparison model was used to compute the weight of criteria. The weighted linear combination model was also used to combine different criteria weight and produce a suitable landfill site map. Landfill site suitability map was prepared by overlaying different criteria and suitability ranks were assigned as unsuitable, low suitable, moderately suitable, highly suitable and very highly suitable. The result of the study shows that 41.02 km2 (651.12%) of the area was unsuitable, 16.27 km2 (20.28%) was low suitable, 10.53 km2 (13.12%) was moderately suitable, 7.54 km2 (9.40%) was highly suitable and 4.88 km2 (6.08) was very highly suitable. From highly and very highly suitable sites, 7 candidate landfill sites were selected and evaluated in terms of area of the site, distance to nearby boreholes, built-up areas, green areas and distance from the center of the town to choose the most suitable site. According to the result of the study, landfill site 6 was the most suitable followed by landfill site 5 while landfill site 2 was the least suitable. The result also shows that selected suitable sites are expected to be friendly to the environment and the societies. Therefore, to have a sound environment and improve public safety, the town should need a landfill site and implement integrated solid waste management.

In the present study, we achieved the susceptibility mapping to slope instability processes by the implementation of Analytic Hierarchy Process and Weighted Linear Combination methods, in the North Branch of Argentino Lake, Southern Patagonian Icefield. The strong retraction of the glaciers in the area has triggered paraglacial readjustments, producing instability processes that favor the generation of mass removal processes. The results obtained from optical satellite images show that the highest degrees of susceptibility (4 and 5) are located on the western slopes of the Upsala Channel, Bertacchi and Cono Tributary Glaciers, and the Moyano and Norte Valleys, respectively. These slopes coincide with the geographic location of previous events surveyed by the inventory of unstable areas of the zone. Low degrees of susceptibility are found on the downhill valleys, outcrops rock and glaciers. The Consistency Ratio was 0.069, indicating that being less than 0.1 the study is reliable. The study sheds light on the knowledge of slopes and valleys that are more susceptible to processes of instability in mountainous areas, which would make it possible to prevent possible hazards associated with these events.

Rapid population growth integrated with poor governance and urban planning is highly challenging resulting key for the selection of unsuitable landfill sites, particularly in developing counties. Therefore, the aim of this study is to investigate the suitable solid waste landfill sites in the capital of the country as a case study, by the integration of Geographical Information System (GIS) with fuzzy logic, analytical hierarchy process (AHP), and weighted linear combination (WLC) method based on multi-criteria decision-making (MCDM). We chose thirteen (13) criteria (9 factors and 4 constraints) and grouped them into two main categories (environmental and socioeconomic) to achieve the objectives. The AHP was employed to evaluate the relative importance of the factors followed by standardization of criteria factors based on fuzzy set theory. Subsequently, all criteria factors were combined based on AHP and fuzzy logic-WLC method in order to obtain land suitability map. Finally, the sites were identified by the intersection of two combined suitability index layers. The obtained results depicted that the integration of fuzzy logic, AHP, and WLC technique with GIS can produce satisfactory results for the suitable locations of solid waste landfill sites over complex topographic regions. Overall, the land suitability obtained based on fuzzy-WLC is more refined and smooth because of its better segregation and its potential to consider full tradeoff between factors and average risk. The AHP was identified (47 km 2 ) as high suitable while fuzzy-WLC generated 36 km 2 as suitable area. Finally, the intersection of both suitability index map shows numerous suitable landfill sites available in Islamabad city; however, the surface areas of the sites are small at individual level (less than 15 ha).

The Northeast part of India is experiencing an increase in infrastructure projects as well as landslides. This study aims to prepare the landslide susceptibility map of Tamenglong and Senapati districts, Manipur, India, and evaluates the state of landslide susceptibility along the Imphal-Jiribam railway corridor. Efficient statistical methods such as frequency ratio (FR), information value (IoV), weight of evidence (WoE), and weighted linear combination (WLC) were used in model preparation. A total of 322 landslide points were randomly divided into training (70%) and testing (30%) datasets. Nine causative factors were utilized for landslide susceptibility mapping (LSM). The importance of which was obtained using the information gain (IG) method. FR, IoV, WoE, and WLC were used to prepare the LSM using the training datasets and nine causative factors. Moreover, the accuracy and consistency were evaluated using AUC-ROC, precision, recall, overall accuracy (OA), balanced accuracy (BA), and F -score. The validation results showed that all methods performed well with the highest AUC and precision values of 0.913 and 0.95, respectively, for the IoV method, while the WLC method had the highest OA, BA, and F -score values of 0.808, 0.81, and 0.812, respectively. Finally, the results from LSM were used to evaluate the state of landslide susceptibility along the Imphal-Jiribam railway corridor. The results showed that 34% of the areas had high and very high susceptibility, while 40% were under less and significantly less susceptibility. The Tupul landslide area lay in medium susceptibility where the disastrous landslide occurred on 30 June 2022. Susceptibility values around the Noney and Khongsag railway station ranged from high to very high susceptibility. Thus, the study manifests the need for LSM preparation in rapidly constructing areas, which in turn will help the policymakers and planners for adopting strategies to minimize losses caused due to landslides.

The increasing trend of population growth along with the rapid groundwater-based agricultural expansion and decreasing trend of mean annual rainfall in the Northwest region of Bangladesh has been exacerbating the declination of groundwater for further expansion. Therefore, the present study attempts to demarcate the potential groundwater abstraction zones from the assessment of potential recharge and available recharge. Potential recharge was obtained with commonly used geospatial-based weighted linear combination (WLC) technique. Here, WLC analysis was based on eight factors related to physiographic (e.g. drainage density, lineament density, slope), geomorphologic (e.g. geomorphology, lithology, soil), land use and land cover (LULC) and hydrology (i.e. rainfall). Available net recharge was assessed for the period 1993–2017 by employing the water table fluctuation method. Finally, the resultant map on potential abstraction was characterized into five different classes, viz. ‘very low’, ‘low’, ‘moderate’, ‘high’ and ‘very high’. The derived map reveals that ‘very high’ potential zone is distributed along the Teesta river floodplain, especially the northeastern part. In contrast, the Barind Tract (i.e. the southwestern and the southcentral parts) area shows ‘very low’ groundwater prospect. Such fused interpretations are expected to contribute to the planning of integrated management of water resources.