Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models

Applying the water quality indices, geographical information system, and advanced decision-making techniques to assess the suitability of surface water for drinking purposes in Brahmani River Basin (BRB), Odisha

Surface water is used for a variety of purposes, including agriculture, drinking water, and other services. Therefore, its quality is crucial for irrigation, human welfare, and health. Thus, the main objective is to improve surface water quality assessment and geochemical analysis to evaluate anthropogenic activities' impact on surface water quality in the Brahmani Watershed, Odisha. In the present paper, emerging techniques such as CRITIC (Criteria Importance Through Inter-criteria Correlation), Additive Ratio Assessment (ARAS), Weighted Aggregated Sum-Product Assessment (WASPAS), SHAP (Shapley Additive Explanation), and Geographical Information System (GIS) were used to locate the origins of pollution in the surface water. The 5-year (2018-2023) database was created by analysing samples that varied geographically over seven sampling locations. The dataset was categorized according to its intended usage. The study employed Inverse Distance Weighting (IDW) tool, to forecast quantities and their geographical arrangement. The water temperature detected at several locations along the river revealed minor variations. The pH variations indicate that the surface water in the studied area is alkaline. Notably, the water's lowest temperature ever recorded was 25.72 °C, at Q-(1). In addition, sufficient DO concentrations are monitored to ensure optimal water quality. The major parts of the study area were found to be majorly affected with high concentrations of PO43-, EC, Ca2+, Mg2+, and SO42-. To determine the degree of contamination, a basic standard reference is necessary to interpret the values, which range from the anthropogenic to the natural contribution. The statistical results reveal the dominant decreasing order amongst the cations, such as: Ca2+  > Mg2+  > Na+  > K+ and in anions, namely, SO42- > Cl- > NO3- > F- > PO43-, respectively. It displays seasonal variations in dissolved and specific phase metal fractions that are not statistically significant at any of the seven sites. Proceeding further, the water quality index showed that the four samples fall in the poor water quality class, whereas the rest, 3 samples, were of good water quality. The surface water is contaminated and negatively affected due to percolation of ions from landfill leachate as per the data of C-WQI. Based on ARAS and WASPAS, Q-(1) and Q-(2) were mainly not fit for consumption. Meanwhile, the SHAP-WQI showed an increase in the number of samples (71.43%) with unsuitable quality for drinking. This emphasizes on the importance of weathering, dissolution, terrigenous, leaching, ion exchange, lithological and evaporation as the primary processes. Human influences were the secondary factors. Overall, the findings indicate that the study area's surface water is safe to drink, with the exception of a few locations including, Q-(1), (2), (3), (4), and (7), in the river water. Integrating GIS using WQ methods gives a new knowledge on the spatial variation in surface water characteristics for designated use. When enforcing regulations and carrying out pollution control operations, this will help determine the precise sampling sites or the sections of the river that show significant degradation. Thus, the integrated model provides insightful data on surface watershed management for urban planners and decision-makers. In overall, these findings underscore the importance of coordinated efforts across administrative boundaries within the basin to reduce water governance costs, providing valuable insights for fostering the coordinated development of regional economies and environmental sustainability. As a result, future studies should be conducted in the area to precisely state the quality of water used for drinking and domestic purposes.

Monitoring land subsidence in the Peshawar District, Pakistan, with a multi-track PS-InSAR technique

Peshawar is one of the most densely populated cities of Pakistan with high urbanization rate. The city overexploits groundwater resources for household and commercial usage which has caused land subsidence. Land subsidence has long been an issue in Peshawar due to insufficient groundwater removal. In this research, we employ the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique with Sentinel-1 imaging data to observe the yearly land subsidence and generate accumulative time-series maps for the years (2018 to 2020) using the SAR PROcessing tool (SARPROZ). The PS-InSAR findings from two contiguous paths are combined by considering the variance over the overlapping area. The subsidence rates in the Peshawar are from -59 to 17 mm/yr. The results show that subsidence is -28.48 mm/yr in 2018, the subsidence reached -49.02 mm/yr in 2019, while in 2020, the subsidence reached -49.90 mm/yr. The findings indicate a notable rise in land subsidence between the years 2018 and 2020. Subsidence is predicted in the research region primarily due to excessive groundwater removal and soil consolidation induced by surficial loads. The correlation of land subsidence observations with groundwater levels and precipitation data revealed some relationships. Overall, the proposed method efficiently monitors, maps, and detects subsidence-prone areas. The utilization of land subsidence maps will enhance the efficiency of urban planning, construction of surface infrastructure, and the management of risks associated with subsidence.

Integrating stratified best-worst method and GIS for landslide susceptibility assessment: a case study in Erzurum province (Turkey)

Landslides are among the most destructive geological disasters that seriously damage human life and infrastructures. Landslides mainly occur in mountainous regions around the world. One of the key processes to reduce these damages is to uncover landslide-exposed areas through different data-driven methods such as Geographical Information System (GIS) and multi-criteria decision-making (MCDM). In the literature, there are many studies developed with these fundamental tools. In this study, unlike the literature, a new landslide susceptibility assessment model is proposed by integrating GIS with the stratified best-worst method (S-BWM). This model has four main dimensions and 16 sub-dimensions under topography, environment-land, location, and hydrological factors, weighted with the S-BWM. A network was created considering the different states that may arise in the importance weights of these dimensions in the future. The transition probabilities of these states were predicted and injected into the classical BWM. Then, maps were created for these dimensions and classifications for each sub-dimension according to the map characteristics. Finally, the most susceptive landslide locations were determined with GIS-based calculations. To demonstrate the model's applicability, a case study was conducted for the Erzurum region, one of Turkey's landslide-prone regions. In addition, besides the landslide map, an analysis and discussion about the spatial distribution of susceptibility classes was presented, contributing to the study's robustness. In the results of landslide susceptibility analysis, landslides are higher in the range of about 1600-2500 m. Approximately 42% (35.59 sq. km) of the study area has high landslide susceptibility, while 58% (64.41 sq. km) has medium and low landslide susceptibility.