Abstract :

Groundwater Plays A Vital Role In Supporting Agriculture, Domestic Needs, And Industrial Activities, Making The Identification Of Suitable Well Locations Essential For Sustainable Water Resource Management. Traditional Groundwater Exploration Methods Are Often Expensive, Time-consuming, And Dependent On Extensive Field Surveys. This Study Presents An AI-Driven Groundwater Potential Estimation And Well Recommendation System That Uses Artificial Intelligence To Estimate Groundwater Availability And Recommend Suitable Drilling Locations. The Proposed Framework Analyses Hydrogeological Data Such As Borehole Information, Soil Characteristics, Groundwater Levels, And Drilling Depth Through Data Preprocessing, Feature Extraction, And Machine Learning Techniques. Multiple Predictive Models Are Employed To Improve The Accuracy And Reliability Of Groundwater Estimation. The Performance Of The System Is Evaluated Using Standard Metrics To Ensure Consistent Prediction Results. Experimental Findings Demonstrate That The Proposed Approach Effectively Identifies Regions With High Groundwater Potential While Reducing The Uncertainty Associated With Conventional Exploration Methods. The Developed System Serves As A Practical Decision Support Tool For Engineers, Planners, And Water Resource Authorities By Improving Groundwater Exploration Efficiency, Reducing Drilling Costs, And Supporting Sustainable Groundwater Management.

Published:

30-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1374-1379

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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