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Machine Learning-Driven Groundwater Potential Zoning Using Geospatial Analytics and Random Forest in the Pandameru River Basin, South India

Author

Listed:
  • Ravi Kumar Pappaka

    (Department of Geology, Yogi Vemana University, Kadapa 516005, Andhra Pradesh, India)

  • Anusha Boya Nakkala

    (Department of Geology, Yogi Vemana University, Kadapa 516005, Andhra Pradesh, India)

  • Pradeep Kumar Badapalli

    (CSIR-National Geophysical Research Institute, Hyderabad 500007, Telangana, India)

  • Sakram Gugulothu

    (CSIR-National Geophysical Research Institute, Hyderabad 500007, Telangana, India)

  • Ramesh Anguluri

    (Ministry of Environment Forest and Climate Change, New Delhi 110003, India)

  • Fahdah Falah Ben Hasher

    (Department of Geography and Environmental Sustainability, College of Humanities and Social Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Mohamed Zhran

    (Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

Abstract

The Pandameru River Basin, South India, is affected by high levels of contamination from human activities and the over-exploitation of groundwater for agriculture, both of which pose significant threats to water quality and its availability for drinking and irrigation. To explore sustainable groundwater management, this study presents a machine learning-driven approach to basin-scale groundwater potential zone (GWPZ) mapping by integrating remote sensing (RS), a geographic information system (GIS), and the random forest (RF) algorithm. The research leverages ten thematic layers—including lithology, geomorphology, soil type, lineament density, slope, drainage density, land use/land cover (LULC), NDVI, SAVI, and rainfall—to assess groundwater availability. The RF model, trained with well-distributed groundwater data, provides an optimized classification of GWPZs into five categories: very good (5.84%), good (15.21%), moderate (27.25%), poor (27.22%), and very poor (24.47%). The results indicate that excellent groundwater zones are predominantly located along highly permeable alluvial deposits, whereas low-potential zones coincide with impermeable geological formations and steep terrains. Field validation using piezometric readings and well data confirmed significant variations in water table depths, ranging from 5 m to over 150 m. The groundwater potential map achieved an accuracy of 86%, underscoring the effectiveness of the RF model in predicting groundwater availability. This high-precision mapping technique enhances decision-making for sustainable groundwater management, supporting long-term water conservation, equitable resource allocation, and climate-resilient water strategies. By providing reliable insights into groundwater distribution, this study contributes to the sustainable utilization of groundwater resources in semiarid regions, aiding policymakers and planners in mitigating water scarcity challenges and ensuring water security for future generations.

Suggested Citation

  • Ravi Kumar Pappaka & Anusha Boya Nakkala & Pradeep Kumar Badapalli & Sakram Gugulothu & Ramesh Anguluri & Fahdah Falah Ben Hasher & Mohamed Zhran, 2025. "Machine Learning-Driven Groundwater Potential Zoning Using Geospatial Analytics and Random Forest in the Pandameru River Basin, South India," Sustainability, MDPI, vol. 17(9), pages 1-27, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:3851-:d:1641835
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    References listed on IDEAS

    as
    1. Ahmed Madani & Burhan Niyazi, 2023. "Groundwater Potential Mapping Using Remote Sensing and Random Forest Machine Learning Model: A Case Study from Lower Part of Wadi Yalamlam, Western Saudi Arabia," Sustainability, MDPI, vol. 15(3), pages 1-15, February.
    2. Shaghayegh Miraki & Sasan Hedayati Zanganeh & Kamran Chapi & Vijay P. Singh & Ataollah Shirzadi & Himan Shahabi & Binh Thai Pham, 2019. "Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 281-302, January.
    3. Phong Tung Nguyen & Duong Hai Ha & Abolfazl Jaafari & Huu Duy Nguyen & Tran Van Phong & Nadhir Al-Ansari & Indra Prakash & Hiep Van Le & Binh Thai Pham, 2020. "Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam," IJERPH, MDPI, vol. 17(7), pages 1-20, April.
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    1. Surajit Banerjee & Tuhina Nandi & Vishwambhar Prasad Sati & Wiem Mezlini & Wafa Saleh Alkhuraiji & Djamil Al-Halbouni & Mohamed Zhran, 2025. "Integrating Remote Sensing, Landscape Metrics, and Random Forest Algorithm to Analyze Crop Patterns, Factors, Diversity, and Fragmentation in a Kharif Agricultural Landscape," Land, MDPI, vol. 14(6), pages 1-23, June.

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