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Sanitary landfill is still considered as one of the most significant and least expensive methods of waste disposal. It is essential to consider environmental impacts while selecting a suitable landfill site. Thus, the site selection for sanitary landfill is a complex and time-consuming task needing an assessment of multiple criteria. In the present study, a decision support system (DSS) was prepared for selecting a landfill site in a growing urban region. This study involved two steps of analysis. The first step of analysis involved the application of spatial data to prepare the thematic maps and derive their weight. The second step employed a fuzzy multicriteria decision-making (FMCDM) technique for prioritizing the identified landfill sites. Thus, initially, the analytic hierarchy process (AHP) was used for weighting the selected criteria, while the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) was applied for addressing the uncertainty associated with decision-making and prioritizing the most suitable site. A case study was conducted in the city of Memari Municipality. The main goal of this study was the initial evaluation and acquisition of landfill candidate sites by utilizing GIS and the following decision criteria: (1) environmental criteria consisting of surface water, groundwater, land elevation, land use land cover, distance from urban residence and buildup, and distance from sensitive places; and (2) socioeconomic criteria including distance from the road, population density, and land value. For preparing the final suitability map, the integration of GIS layers and AHP was used. On output, 7 suitable landfill sites were identified which were further ranked using FTOPSIS based on expert's views. Finally, candidate site-7 and site-2 were selected as the most suitable for proposing new landfill sites in Memari Municipality. The results from this study showed that the integration of GIS with the MCDM technique can be highly applied for site suitability. The present study will be helpful to local planners and municipal authorities for proposing a planning protocol and suitable sites for sanitary landfill in the near future.

The indiscriminate use of groundwater and its overexploitation has led to a significant decline in groundwater resources in India, making it essential to identify potential recharge zones for aquifer recharge. A study was conducted to determine such potential recharge zones in the Nandhour-Kailash River watershed. The study area included 1481 streams divided into 12 sub-basins (SWS). The results show that the downstream Saraunj sub-basins (SWS-11) and Odra sub-basins (SWS-12) were high priority and required immediate soil and water conservation attention. Sub catchments Lobchla West (SWS-4), Deotar (SWS-5), Balot South (SWS-8), Nandhour (SWS-9), and Nakoliy (SWS-10) had medium priority and were designated for moderate soil erosion and degradation. In contrast, sub-catchments Aligad (SWS-1), Kundal (SWS-2), Lowarnala North (SWS-3), Bhalseni (SWS-6), and Uparla Gauniyarao (SWS-7) had low priority, indicating a low risk of soil erosion and degradation. Using the existing groundwater level data, the potential map of groundwater was validated to confirm its validity. According to the guidelines provided by the Integrated Mission for Sustainable Development (IMSD), the results of the groundwater potential zones for good to very good zones have been integrated at the slope and stream order. In a 120.94 km2 area with a slope of 0–5% in first-order streams, 36 ponds were proposed, and in a 218.03 km2 area with a slope of 15% in first- to fourth-order streams, 105 retention dams were proposed and recognized as possible sites for artificial groundwater recharge. The proposed water harvesting structure may aid in continuously recharging these zones and benefit water resource managers and planners. Thus, various governmental organizations can use the results to identify possible future recharge areas. [ABSTRACT FROM AUTHOR]

Vypin, Vallarpadam, and Bolgatty are significant tropical coastal islands situated in the humid tropical Kerala region of India, notable for their environmental sensitivity. This study conducted a comprehensive assessment of shoreline alterations on these islands by integrating Remote Sensing (RS) and Geographic Information Systems (GIS) techniques. Utilizing satellite imagery from the LANDSAT series with a spatial resolution of 30 m, the analysis spanned the years from 1973 to 2019. The Digital Shoreline Analysis System (DSAS) tool, integrated into the ArcGIS software, was employed to monitor and analyze shoreline shifts, encompassing erosion and accretion. Various statistical parameters, including Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR), were utilized to evaluate these changes. Additionally, the study aimed to discern the root causes of shoreline modifications in the study area, encompassing disturbances and the construction of new structures on these islands. The results conclusively demonstrated the substantial impact endured by these coastal islands, with accretion on both sides leading to the creation of new landmasses. This manuscript effectively illustrates that these islands have experienced marine transgression, notably evidenced by accretion. Anthropogenic activities were identified as the primary drivers behind the observed shoreline changes, underscoring the need for careful management and sustainable practices in these fragile coastal ecosystems. [ABSTRACT FROM AUTHOR]

The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also. [ABSTRACT FROM AUTHOR]

Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, the combination of data‐driven techniques with remote sensing (RS) and geographical information systems (GIS) has been widely used by researchers for flood susceptibility mapping. This study presents a novel hybrid model combining the multilayer perceptron (MLP) and autoencoder models to produce the susceptibility maps for two study areas located in Iran and India. For two cases, nine, and twelve factors were considered as the predictor variables for flood susceptibility mapping, respectively. The prediction capability of the proposed hybrid model was compared with that of the traditional MLP model through the area under the receiver operating characteristic (AUROC) criterion. The AUROC curve for the MLP and autoencoder‐MLP models were, respectively, 75 and 90, 74 and 93% in the training phase and 60 and 91, 81 and 97% in the testing phase, for Iran and India cases, respectively. The results suggested that the hybrid autoencoder‐MLP model outperformed the MLP model and, therefore, can be used as a powerful model in other studies for flood susceptibility mapping. [ABSTRACT FROM AUTHOR]