Year 2026,
Volume: 11 Issue: 2, 301 - 320
Suraj Dule
,
Arabinda Sharma
References
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Saravanan, S., Saranya, T., & Abijith, D. (2022). Application of frequency ratio, analytical hierarchy process, and multi-influencing factor methods for delineating groundwater potential zones. International Journal of Environmental Science and Technology, 19(12), 12211-12234.
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Thanh, N. N., Chotpantarat, S., Trung, N. H., & Ngu, N. H. (2022). Mapping groundwater potential zones in Kanchanaburi Province, Thailand by integrating of analytic hierarchy process, frequency ratio, and random forest. Ecological Indicators, 145, 109591.
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Masroor, M., Rehman, S., Sajjad, H., Rahaman, M. H., Sahana, M., Ahmed, R., & Singh, R. (2021). Assessing the impact of drought conditions on groundwater potential in Godavari Middle Sub-Basin, India using analytical hierarchy process and random forest machine learning algorithm. Groundwater for sustainable development, 13, 100554.
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Roy, P., Pal, S. C., Chakrabortty, R., Chowdhuri, I., Saha, A., & Shit, M. (2022). Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation. Science of the total environment, 849, 157850.
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Das, S. (2019). Comparison among influencing factor, frequency ratio, and analytical hierarchy process techniques for groundwater potential zonation in Vaitarna basin, Maharashtra, India. Groundwater for Sustainable Development, 8, 617-629.
-
Khosravi, K., Sartaj, M., Tsai, F. T. C., Singh, V. P., Kazakis, N., Melesse, A. M., & Pham, B. T. (2018). A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Science of the total environment, 642, 1032-1049.
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-
Rahmati, O., Pourghasemi, H. R., & Melesse, A. M. (2016). Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena, 137, 360-372.
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Nag, S. K., Chowdhury, P., Das, S., & Mukherjee, A. (2021). Deciphering prospective groundwater zones in Bankura district, West Bengal: a study using GIS platform and MIF techniques. International Journal of Energy and Water Resources, 5, 323-341.
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Sahu, B. B., & Kumar, A. (2022). Aquifer Mapping and Management of Ground Water Resources Bankura District West Bengal. https://www.cgwb.gov.in/cgwbpnm/public/uploads/documents/16905335181706431115file.pdf
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Abate, S. G., Amare, G. Z., & Adal, A. M. (2022). Geospatial analysis for the identification and mapping of groundwater potential zones using RS and GIS at Eastern Gojjam, Ethiopia. Groundwater for Sustainable Development, 19, 100824.
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Saranya, T., & Saravanan, S. (2020). Groundwater potential zone mapping using analytical hierarchy process (AHP) and GIS for Kancheepuram District, Tamilnadu, India. Modeling Earth Systems and Environment, 6(2), 1105-1122.
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Çitfçi, H., & KUŞAK, L. (2021). Determination of unsuitability points on the route of Van Gölü-Kapıköy railway line by using GIS and AHP method. Advanced GIS, 1(1), 27–37. Retrieved from https://publish.mersin.edu.tr/index.php/agis/article/view/69
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Deciphering groundwater potential zones of Sarenga block, West Bengal, India using Geographic Information System and Multi-Criteria Decision-Analysis methods
Year 2026,
Volume: 11 Issue: 2, 301 - 320
Suraj Dule
,
Arabinda Sharma
Abstract
The increasing demand for water in recent decades has led to continuous exploitation and mismanagement of groundwater resources worldwide. This has often resulted in the reduction of the water table and deterioration of water quality due to non-sustainable consumption and excessive extraction practices. To address these issues, it is very crucial to analyse Groundwater Potential (GWP) zones periodically. In this study, Geographic Information System (GIS) and Remote Sensing (RS) techniques coupled with Analytical Hierarchy Process (AHP), Multi Influencing Factor (MIF), and Random Forest (RF) algorithm have been used to define GWP zones. These methods helped to identify, weigh, and rank eleven major hydrogeological factors influencing groundwater potential (GWP). A novel application of the RF algorithm utilized to generate high-resolution GWP maps outperformed AHP (0.875) and MIF (0.828) with a Receiver Operating Characteristic (ROC) of 0.982 in GWP delineation, as assessed by the Area Under the Curve (AUC) analysis. The outcome from AHP, MIF, and RF methods revealed that around 60-70% of the study area showed poor to fair GWP while only 30- 40% of the area exhibited good to excellent GWP. The results revealed that a significant portion of the study area exhibits poor to fair GWP, highlighting the urgent need for sustainable GW management strategies. These findings provide valuable insights for policymakers and local farmers to make informed decisions on sustainable GW management plans tailored to the specific needs of the study area.
Ethical Statement
1. The authors declare that the manuscript represents original work and has not been copied or plagiarized, in whole or in part, from other published or unpublished work. All contributors who have made substantial contributions to the work reported in this manuscript are acknowledged.
2. The authors declare that the manuscript has not been previously published, nor is it under consideration for publication elsewhere.
3. The authors declare no conflicts of interest to declare.
4. The authors declare that all data sources used in the preparation of this manuscript, including text, figures, and tables, have been properly cited and acknowledged in the text.
Thanks
The authors would like to thank the Editors and anonymous reviewers for their valuable suggestion to make this article of publishing quality.
References
-
Hussain, S., & Mahmood, S. (2023). Analyzing Domestic Water Consumption in Wana, South Waziristan, Khyber Pakhtunkhwa Province- Pakistan. Advanced Geomatics, 3(1), 16-22.
-
Ünel, F. B., Kuşak, L., Yakar, M., & Doğan, H. (2023). Coğrafi bilgi sistemleri ve analitik hiyerarşi prosesi kullanarak Mersin ilinde otomatik meteoroloji gözlem istasyonu yer seçimi. Geomatik, 8(2), 107-123.
-
Kinzelbach, W., Bauer, P., Siegfried, T., & Brunner, P. (2003). Sustainable groundwater management—problems and scientific tools. Episodes Journal of International Geoscience, 26(4), 279-284.
-
Berhanu, K. G., & Hatiye, S. D. (2020). Identification of groundwater potential zones using proxy data: case study of Megech Watershed, Ethiopia. Journal of Hydrology: Regional Studies, 28, 100676.
-
Das, S. (2017). Delineation of groundwater potential zone in hard rock terrain in Gangajalghati block, Bankura district, India using remote sensing and GIS techniques. Modeling Earth Systems and Environment, 3(4), 1589-1599.
-
Waikar, M. L., & Nilawar, A. P. (2014). Identification of groundwater potential zone using remote sensing and GIS technique. International Journal of Innovative Research in Science, Engineering and Technology, 3(5), 12163-12174.
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Patil, R., & Datta, M. (2022). Spatio-Temporal Analysis of Climate Change in India: a Theoretical Perspective. Advanced Geomatics, 2(1), 07-13.
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Jamil, M., Mahmood, S., Husssain, S., & Saad, M. (2024). Assessing the impact of drought on groundwater resources using geospatial techniques in Balochistan Province, Pakistan. Advanced Remote Sensing, 4(1), 11-27.
-
Ahirwar, R., Malik, M. S., Ahirwar, S., & Shukla, J. P. (2021). Groundwater potential zone mapping of Hoshangabad and Budhni industrial area, Madhya Pradesh, India. Groundwater for Sustainable Development, 14, 100631.
-
Makhmudov, R., & Teymurov, M. (2024). Importance of using GIS software in the process of application of Analogue terrains and Counter-approach technologies in water resources assessment. Advanced Remote Sensing, 4(1), 36-45.
-
Avtar, R., Singh, C. K., Shashtri, S., Singh, A., & Mukherjee, S. (2010). Identification and analysis of groundwater potential zones in Ken–Betwa river linking area using remote sensing and geographic information system. Geocarto International, 25(5), 379-396.
-
Memduhoglu, A. (2023). Identifying impervious surfaces for rainwater harvesting feasibility using unmanned aerial vehicle imagery and machine learning classification. Advanced GIS, 3(1), 01-06.
-
Najatishendi, E., Ergene, E. M., Uzar, M., & Şanlı, F. B. (2022). Production of flood risk maps: Ayancık Stream Example. Mersin Photogrammetry Journal, 4(1), 24-31.
-
Pande, C. B., Moharir, K. N., Panneerselvam, B., Singh, S. K., Elbeltagi, A., Pham, Q. B., & Rajesh, J. (2021). Delineation of groundwater potential zones for sustainable development and planning using analytical hierarchy process (AHP), and MIF techniques. Applied Water Science, 11(12), 186.
-
Patra, S., Mishra, P., & Mahapatra, S. C. (2018). Delineation of groundwater potential zone for sustainable development: A case study from Ganga Alluvial Plain covering Hooghly district of India using remote sensing, geographic information system and analytic hierarchy process. Journal of Cleaner Production, 172, 2485-2502.
-
Yadav, B., Malav, L. C., Jangir, A., Kharia, S. K., Singh, S. V., Yeasin, M., & Yadav, K. K. (2023). Application of analytical hierarchical process, multi-influencing factor, and geospatial techniques for groundwater potential zonation in a semi-arid region of western India. Journal of Contaminant Hydrology, 253, 104122.
-
Öcül, M., & Şişman, A. (2023). Landslide susceptibility analysis with multi criteria decision methods; a case study of Taşova. Advanced GIS, 3(1), 14–21. Retrieved from https://publish.mersin.edu.tr/index.php/agis/article/view/835
-
Das, S., Gupta, A., & Ghosh, S. (2017). Exploring groundwater potential zones using MIF technique in semi-arid region: a case study of Hingoli district, Maharashtra. Spatial Information Research, 25, 749-756.
-
Saravanan, S., Saranya, T., & Abijith, D. (2022). Application of frequency ratio, analytical hierarchy process, and multi-influencing factor methods for delineating groundwater potential zones. International Journal of Environmental Science and Technology, 19(12), 12211-12234.
-
Thanh, N. N., Chotpantarat, S., Trung, N. H., & Ngu, N. H. (2022). Mapping groundwater potential zones in Kanchanaburi Province, Thailand by integrating of analytic hierarchy process, frequency ratio, and random forest. Ecological Indicators, 145, 109591.
-
Masroor, M., Rehman, S., Sajjad, H., Rahaman, M. H., Sahana, M., Ahmed, R., & Singh, R. (2021). Assessing the impact of drought conditions on groundwater potential in Godavari Middle Sub-Basin, India using analytical hierarchy process and random forest machine learning algorithm. Groundwater for sustainable development, 13, 100554.
-
Roy, P., Pal, S. C., Chakrabortty, R., Chowdhuri, I., Saha, A., & Shit, M. (2022). Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation. Science of the total environment, 849, 157850.
-
Das, S. (2019). Comparison among influencing factor, frequency ratio, and analytical hierarchy process techniques for groundwater potential zonation in Vaitarna basin, Maharashtra, India. Groundwater for Sustainable Development, 8, 617-629.
-
Khosravi, K., Sartaj, M., Tsai, F. T. C., Singh, V. P., Kazakis, N., Melesse, A. M., & Pham, B. T. (2018). A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Science of the total environment, 642, 1032-1049.
-
Chowdhury, A., Jha, M. K., Chowdary, V. M., & Mal, B. C. (2009). Integrated remote sensing and GIS‐based approach for assessing groundwater potential in West Medinipur district, West Bengal, India. International Journal of Remote Sensing, 30(1), 231-250.
-
Rahmati, O., Pourghasemi, H. R., & Melesse, A. M. (2016). Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena, 137, 360-372.
-
Nag, S. K., Chowdhury, P., Das, S., & Mukherjee, A. (2021). Deciphering prospective groundwater zones in Bankura district, West Bengal: a study using GIS platform and MIF techniques. International Journal of Energy and Water Resources, 5, 323-341.
-
Sahu, B. B., & Kumar, A. (2022). Aquifer Mapping and Management of Ground Water Resources Bankura District West Bengal. https://www.cgwb.gov.in/cgwbpnm/public/uploads/documents/16905335181706431115file.pdf
-
Abate, S. G., Amare, G. Z., & Adal, A. M. (2022). Geospatial analysis for the identification and mapping of groundwater potential zones using RS and GIS at Eastern Gojjam, Ethiopia. Groundwater for Sustainable Development, 19, 100824.
-
Saranya, T., & Saravanan, S. (2020). Groundwater potential zone mapping using analytical hierarchy process (AHP) and GIS for Kancheepuram District, Tamilnadu, India. Modeling Earth Systems and Environment, 6(2), 1105-1122.
-
Çitfçi, H., & KUŞAK, L. (2021). Determination of unsuitability points on the route of Van Gölü-Kapıköy railway line by using GIS and AHP method. Advanced GIS, 1(1), 27–37. Retrieved from https://publish.mersin.edu.tr/index.php/agis/article/view/69
-
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.
-
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
-
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
-
Cutler, D. R., Edwards Jr, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783-2792.
-
Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003). Random forest: a classification and regression tool for compound classification and QSAR modeling. Journal of chemical information and computer sciences, 43(6), 1947-1958.
-
Ayalke, G. Z., & Şişman, A. (2024). Google Earth Engine kullanılarak makine öğrenmesi tabanlı iyileştirilmiş arazi örtüsü sınıflandırması: Atakum, Samsun örneği. Geomatik, 9 (3), 375-390.
-
Özdemir, E. G., Zengin, T. U., & Güleç, H. A. (2024). Orman ekosistemindeki ağaç boylarının, optik, radar, lazer altimetre uydu verileri ve yardımcı kaynaklar kullanılarak Google Earth Engine platformunda modellenmesi. Geomatik, 9 (2), 259-268.
-
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