Groundwater potential mapping in Trans Yamuna Region, Prayagraj, using combination of geospatial technologies and AHP method

In this study, the combination of Remote Sensing and Geographic Information System (GIS) was utilized to identify the Groundwater Potential Zones (GPZs) of the Trans-Yamuna region. The Groundwater Potential Zones (GPZ) were mapped by integrating drainage density, slope, geology, geomorphology, NDVI,...

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Bibliographic Details
Published in:Environmental monitoring and assessment 2023-11, Vol.195 (11), p.1375-1375, Article 1375
Main Authors: Swarnim, Tripathi, Jayant Nath, Sonker, Irjesh, Tiwari, Surya Prakash
Format: Article
Language:English
Subjects:
Accuracy
Analytic hierarchy process
Atmospheric Protection/Air Quality Control/Air Pollution
Drainage
Drainage density
Earth and Environmental Science
Ecology
Ecotoxicology
Environment
Environmental Management
Environmental monitoring
Geographic information systems
Geographical information systems
Geology
Geomorphology
Groundwater
Groundwater management
Groundwater potential
Groundwater recharge
Groundwater resources
Hierarchies
Information processing
Information systems
Land cover
Land use
Mathematical models
Monitoring/Environmental Analysis
Parameters
Prediction models
Rainfall
Remote sensing
Resource management
Sensitivity analysis
Soil types
Water resources
Wetness index
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Summary:In this study, the combination of Remote Sensing and Geographic Information System (GIS) was utilized to identify the Groundwater Potential Zones (GPZs) of the Trans-Yamuna region. The Groundwater Potential Zones (GPZ) were mapped by integrating drainage density, slope, geology, geomorphology, NDVI, lineament density, rainfall, soil types, land use & land cover, and topographic wetness index maps. For the prediction output to have a non-trivial degree of accuracy, multicollinearity tests were run before integrating the layers. Using the Analytical Hierarchy Process (AHP), groundwater recharge-affecting parameters and classes of each parameter were scored. All thematic layers were integrated using weighted linear combination on a GIS platform to create a groundwater potential zone map. The outcomes of the model indicate that the research region exhibits three distinct groundwater potential zones, namely low (11.928%; 354.884 km 2 ), moderate (76.44%; 2274.4 km 2 ), and high (11.267%; 345.943 km 2 ), in sequential sequence. These categories define the model’s output in descending order of how closely it matches the actual conditions. After that, a map removal sensitivity analysis was also executed and found that geology, geomorphology, lineament density and drainage density strongly influence the prediction model for groundwater potential zone identification. The reliability of the results is established by employing a Receiver Operating Characteristic (ROC) curve for evaluation, which demonstrates a prediction accuracy of 81.3%. Authorities responsible for groundwater resource management can use this study’s findings to better inform future regulatory initiatives.
ISSN:0167-6369
1573-2959
DOI:10.1007/s10661-023-11934-y