Pranavi Kannepogu,
T. Jhansi Rani,
V.S.C. Prasanthi,
Deepthi Marada,
Abstract
The AI-Based Decision Support System for Water Well Construction utilizes data from the National Aquifer Mapping and Management System (NAQUIM) and employs advanced AI techniques like regression analysis, decision trees, and neural networks. This system predicts crucial parameters for water well construction, including location suitability, water-bearing zone depths, and groundwater quality. By integrating large datasets such as lithology, geophysical logs, and aquifer maps provided by the Central Ground Water Board (CGWB), the system enhances the precision of predictions. Its user-friendly graphical interface allows users to select specific locations and obtain detailed insights effortlessly. The AI-driven approach offers a scalable and accurate alternative to traditional manual methods, improving the efficiency of decision-making in water well planning. By providing reliable and actionable insights, the system supports sustainable groundwater management, promoting more effective resource utilization and reducing environmental impact. This innovative solution significantly advances groundwater decision support practices.
Keywords: Water well construction, Groundwater management, Artificial intelligence (AI), NAQUIM data, Decision support system
[This article belongs to Journal of Water Resource Engineering and Management ]
Pranavi Kannepogu, T. Jhansi Rani, V.S.C. Prasanthi, Deepthi Marada. Harnessing Hydrolgeological Parametrs: Prediction of Water Probability and Levels for Water Well Construction Using Ai-Enabled Models. Journal of Water Resource Engineering and Management. 2025; 12(01):16-28.
Pranavi Kannepogu, T. Jhansi Rani, V.S.C. Prasanthi, Deepthi Marada. Harnessing Hydrolgeological Parametrs: Prediction of Water Probability and Levels for Water Well Construction Using Ai-Enabled Models. Journal of Water Resource Engineering and Management. 2025; 12(01):16-28. Available from: https://journals.stmjournals.com/jowrem/article=2025/view=208104
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- Central Ground Water Board (CGWB), Ministry of Jal Shakti, Department of Water Resources, River Development and Ganga Rejuvenation, Government of India.
- India Water Resources Information System (IWRIS), Ministry of Jal Shakti, Department of Water Resources, National Water Informatics Centre, Government of India.
Journal of Water Resource Engineering and Management
| Volume | 12 |
| Issue | 01 |
| Received | 28/10/2024 |
| Accepted | 16/01/2025 |
| Published | 20/01/2025 |
| Publication Time | 84 Days |
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