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The present study aims to enrich the specialized literature by proposing and calculating a new flash-flood propagation susceptibility index (FFPSI). Thus, firstly the Flash-Flood Potential Index (F...

This article is intended to assess the flood-induced landslide susceptibility in the Indian state of Assam. This study area has high frequency and severity of landslides that are triggered by heavy...

The present study aimed to assess the impacts of land cover on groundwater quality by integrating physico-chemical data and satellite imageries. Initially, fourteen groundwater parameters of both pre-monsoon and post-monsoon were collected from nineteen sampled stations and water quality index (WQI) was calculated. Consequently, Google earth, Landsat-8, and Sentinel-2A imageries were considered for land cover mapping including the concentration of settlement and urban built-up, greenery coverage, and micro-level land use. Two machine learning models such as artificial neural network (ANN) and random forest (RF) were used for pixel-based classification and establish spatial relation between water quality and land cover. This study trained and tested the models for the whole study area as well as 500m buffers from each sampled station. The result of model’s validation including mean absolute error (MAE), root mean squared error (RMSE), Kappa statistic (K), Overall accuracy of model (OAC), and receiver operating characteristic (ROC), indicated that random forest classifier has better performance than the artificial neural network. The results show that the testing dataset of pre-monsoon season has higher accuracy with MAE 0.343, RMSE 0.397, K-value 0.55, and ROC value 0.838 to envisage the impact of land cover on groundwater quality in comparison to post-monsoon season. The results also reveal that the classification accuracy is greater within 500m buffer areas in comparison to the whole study area with a close to 0 value of MAE and RMSE, and absolutely 1 value of K and ROC. Based on the above findings, the present study suggested to consider a large scale for determining the controlling factors of groundwater degradation.

The water quality of rivers is deteriorating due to human interference. It is essential to understand the relationship between human activities and land use types to assess the water quality of a region. GIS is the latest tool for analyzing this spatial correlation. Land use land cover, and change detection are the best illustration for showing the human interactions with land features. This study assessed water quality index of the upper Ganges River near Haridwar, Uttarakhand, and spatially correlated it with changing land use to reach a logical conclusion. In the upper course of Ganges, along a 78-km stretch from Kaudiyala to Bhogpur, water samples were collected from five stations. For water quality index, physicochemical parameters like pH, EC, DO, TDS, CaCO3−, CaCO3, Cl−, Ca2+, Mg2+, Na+, K+, F−, Fe2+ were considered. The results of the spatial analysis were evaluated through error estimation and spatial correlation. The root mean square error between spatial land use and water quality index at the selected sampling sites was estimated to be 0.1443. The spatial correlation between land use change and site-wise differences in water quality index also showed a high positive correlation, with R2 = 0.8455. The degree of positive correlation and root mean square error strongly indicated that the water quality of the river in the upper course of the Ganges is highly impacted by human activities.

One of the most important challenges in solid waste management throughout the world is selection of suitable landfill sites. It is essential for solid waste management, because unscientific dumping and landfilling of waste could negatively impact the environment and human health. This research presents a GIS-based multi-criteria decision support approach for assessing the suitability analysis for landfill site selection in Kolkata Municipal Corporation, India. In highly urbanized areas of Kolkata, there is stress regarding the location of potential landfill sites, because existing waste dumping ground, i.e., Dhapa have no longer space to hold waste after 2 or 3 years. The availability of land is also a major tension than financial assistance in the study area. Therefore, the present study developed a methodological framework for locating suitable landfill candidate sites and selecting the best alternatives. Thus, 20 relevant alternatives were selected and relative weight calculated using the fuzzy analytic hierarchy process. The geographic information system was considered to arrange the spatial and aspatial data. The alternatives of all criteria were rated from 1–5 scale indicating lowest to highest suitability for landfill. The weighted linear combination was performed between different criteria for modeling suitable candidate sites. The results revealed that throughout the study area, initially, 16 candidate sites were identified based on geospatial analysis, but out of them, 13 sites cannot be accepted for a landfill site due to current land use, public acceptance, transportation, and local environmental issues. Only three suitable sites can be considered for landfilling, although these are also not completely satisfying the environmental concern. However, landfill site is also essential; otherwise, it will appear as more adverse impact. Therefore, with little compromising with the environmental component, the engineered plan can be designed for the sanitary landfill on specific sites that recommended in this suitability analysis.