Exploration
BALBIR NAGAL; Ajay Krishna Prabhakar; Mahesh Pal
Abstract
This study delineates groundwater potential (GWP) zones across Haryana, India, for the year 2023 using geospatial techniques integrated with the analytical hierarchy process (AHP). Multiple thematic layers, including slope, land use/land cover (LULC), soil, geology, drainage density (DD), lineament density ...
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This study delineates groundwater potential (GWP) zones across Haryana, India, for the year 2023 using geospatial techniques integrated with the analytical hierarchy process (AHP). Multiple thematic layers, including slope, land use/land cover (LULC), soil, geology, drainage density (DD), lineament density (LD), elevation, rainfall, and topographic wetness index (TWI), were generated using datasets from SRTM, Sentinel-2, food and agriculture organization (FAO), and the India meteorological department (IMD) and weighted through the AHP. These layers were integrated using weighted overlay analysis (WOA) to generate the final GWP map. The GWP map was validated against field groundwater level (GWL) data from 646 wells recorded in 2018 by the central ground water board (CGWB), resulting in an accuracy of 77.55 percent. This confirmed the reliability of the geographic information system (GIS) and AHP technique. The study reveals that moderate GWP zones dominate (43.71%) the region, followed by high (33.24%) and very high (11.96%) zones, whereas low and very low GWP zones cover 7.59% and 3.51% of the area, respectively. The findings indicate that Haryana’s groundwater distribution is largely stable, with minor variation observed between 2018 and 2023. This shows stable aquifer behaviour and relatively unchanged recharge and extraction patterns over the five-year period. The outcomes of this study are valuable for strategic groundwater management, especially in arid and semiarid regions of Haryana state.
Mine Economic and Management
Sarina Akbari; Reza Ghezelbash; Hamidreza Ramazi; Abbas Maghsoudi
Abstract
Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies ...
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Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies have been proposed for generating landslide hazard maps, which can potentially aid in predicting future landslide-prone areas. This study employed an integrated approach that combines statistical and multi-criteria decision-making (MCDM) methodologies. The Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) were utilized as knowledge-driven approaches, while the Support Vector Machine (SVM) using an RBF kernel, a widely recognized machine learning algorithm, was applied as a data-driven method. Ten factors influencing landslides were considered, including slope angle, aspect, altitude, geology, land use, climate, erosion, and distances from rivers, faults, and roads. The results revealed that landslides are more predictable in the southern, southwestern, and central regions of the studied area. A quantitative assessment of the different methods using prediction-rate curves indicated that the SVM method outperformed the FR and AHP-FR approaches in identifying susceptible areas. The findings of this work could be effectively employed to mitigate potential future hazards and associated damages.
Akhilesh Kumar; Ravi Kumar Sharma; Vijay Kumar Bansal
Abstract
The GIS-multi-criteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for predicting the future hazards, land use planning, and hazard preparedness. Identification of landslide susceptible regions helps in making a strategic plan for future developmental ...
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The GIS-multi-criteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for predicting the future hazards, land use planning, and hazard preparedness. Identification of landslide susceptible regions helps in making a strategic plan for future developmental activities in the landslide-prone areas. It enables the integration of different data layers with varying levels of uncertainty. In this work, GIS-MCDA is applied to landslide hazard zonation for the Kullu district in Himachal Pradesh, India. The current work aims to evaluate the performance of the analytical hierarchy process (AHP) for the development of a landslide hazard map. The geographical information system is used for the preparation of the database, analysis, modelling, and results. The ArcGIS 10.0 software is used to integrate the input layers by assigning appropriate weights. Six landslide causal factors are used, whereby the parameters are extracted from an associated spatial database. These factors are evaluated, and then the respective factor weight and class weight are assigned to each one of the associated factors. The developed landslide hazard map is categorized into three risk zones. The current work may be of great assistance to regional planners and decision-makers in deciding on the most suitable risk mitigation measures at the local level to prevent the potential losses and damages from landslides in the region.
