Swarnim et al. (2025) Combining MCDM and geospatial techniques to identify groundwater potential zones and trend analysis of rainfall and well water level data: An investigation in the Prayagraj and Kaushambi districts

Identification

• Journal: Geosystems and Geoenvironment
• Year: 2025
• Date: 2025-09-02
• Authors: Swarnim, Jayant Nath Tripathi, Irjesh Sonker, Ritesh Kumar Singh
• DOI: 10.1016/j.geogeo.2025.100454

Research Groups

• Department of Earth & Planetary Sciences, University of Allahabad, Prayagraj, India

Short Summary

This study identifies groundwater potential zones in Prayagraj and Kaushambi districts, India, using remote sensing, GIS, and Multi-Criteria Decision Making (MCDM) techniques (AHP and MIF), revealing that while rainfall is increasing, groundwater levels are decreasing due to overexploitation.

Objective

• To identify groundwater potential zones in Prayagraj and Kaushambi districts, India, by integrating remote sensing and GIS with Analytical Hierarchy Process (AHP) and Multi-Influencing Factor (MIF) methods, utilizing ten thematic layers, and to analyze long-term rainfall and well water level trends.

Study Configuration

• Spatial Scale: Prayagraj and Kaushambi districts, Uttar Pradesh, India, covering an area of 7258.95 square kilometres.
• Temporal Scale: Rainfall data from 1990 to 2023 (34 years); well water level data from 1998 to 2023 (26 years).

Methodology and Data

• Models used: Multi-Criteria Decision Making (MCDM), Analytical Hierarchy Process (AHP), Multi-Influencing Factor (MIF), Receiver Operating Characteristic (ROC) curve analysis, Weighted Linear Combination (WLC), Geostatistical Kriging interpolation, Linear regression (for multicollinearity test), TOPMODEL (for TWI).
• Data sources: Remote Sensing (Landsat 8 OLI/TIRS C2 L2 at 30 m resolution, Sentinel 2 at 10 m resolution, SRTM DEM at 30 m resolution), Geographic Information System (GIS) software (ArcGIS), Geological Survey of India (GSI) (Geology 1:50,000, Geomorphology 1:250,000), Indian Meteorological Department (IMD) Pune (Gridded rainfall data at 0.25° resolution), National Bureau of Soil Survey and Land Use Planning (NBSS and LUP) (Soil map 1:50,000), Uttar Pradesh Groundwater Board (UPGWB) (Well water level data), India Water Resources Information System (India WRIS) (Aquifer system details), Survey of India toposheets (1:50,000). Thematic layers included geology, geomorphology, precipitation, soil texture, lineament frequency, slope, drainage density, topographic wetness index (TWI), land use and land cover (LULC), and normalized difference vegetation index (NDVI).

Main Results

• The Multi-Influencing Factor (MIF) method classified groundwater potential zones as: High (16.35%; 1173.79 km²), Moderate (76.28%; 5477.24 km²), and Low (7.38%; 529.64 km²).
• The Analytical Hierarchy Process (AHP) method classified groundwater potential zones as: High (10.01%; 717.87 km²), Moderate (80.92%; 5810.41 km²), and Low (9.09%; 652.40 km²).
• Model accuracy, validated using ROC curve analysis with well water level data, was 79.6% for AHP and 77.7% for MIF, indicating AHP as slightly more accurate.
• Trend analysis revealed an increasing annual rainfall trend of 1.55 mm per year, while the depth to groundwater level in wells showed a decreasing trend of 0.056 m per year.
• Low groundwater potential zones are predominantly found in the southern Prayagraj district, characterized by hard lithology (sandstone and quartzite), dissected plateaus, high drainage density, and low vegetation cover. High potential zones are typically located near rivers and in alluvial regions.

Contributions

• First application of combined Analytical Hierarchy Process (AHP) and Multi-Influencing Factor (MIF) methodologies with multicollinearity assessment for groundwater potential zone mapping in the Prayagraj and Kaushambi districts.
• Integration of a comprehensive set of ten geo-environmental thematic layers for robust groundwater potential assessment.
• Validation of both AHP and MIF models using Receiver Operating Characteristic (ROC) curve analysis with actual well water level data, providing a comparative accuracy assessment.
• Long-term trend analysis of rainfall (34 years) and groundwater levels (26 years) to contextualize the GWPZ findings and highlight the impact of overexploitation despite increasing rainfall.
• Provides a crucial framework for accelerating groundwater recharge analysis and guiding the installation of artificial recharge structures for sustainable water management in the region.

Funding

• University Grants Commission (U.G.C.), New Delhi, for a Senior Research Fellowship awarded to Swarnim.

Citation

@article{Swarnim2025Combining,
  author = {Swarnim and Tripathi, Jayant Nath and Sonker, Irjesh and Singh, Ritesh Kumar},
  title = {Combining MCDM and geospatial techniques to identify groundwater potential zones and trend analysis of rainfall and well water level data: An investigation in the Prayagraj and Kaushambi districts},
  journal = {Geosystems and Geoenvironment},
  year = {2025},
  doi = {10.1016/j.geogeo.2025.100454},
  url = {https://doi.org/10.1016/j.geogeo.2025.100454}
}