Harnessing Hydrolgeological Parametrs: Prediction of Water Probability and Levels for Water Well Construction Using Ai-Enabled Models

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.

Authors

Publication Details

Journal
Journal of Water Resource Engineering and Management
Published
2025-01-01
DOI
https://doi.org/10.37591/jowrem.v12i01.208104
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

Harnessing Hydrolgeological Parametrs: Prediction of Water Probability and Levels for Water Well Construction Using Ai-Enabled Models

Pranavi Kannepogu, T. Jhansi Rani, V.S.C. Prasanthi, Deepthi Marada
Journal of Water Resource Engineering and Management
Reservoir Engineering and Simulation Methods
article

Harnessing Hydrolgeological Parametrs: Prediction of Water Probability and Levels for Water Well Construction Using Ai-Enabled Models

Pranavi Kannepogu, T. Jhansi Rani, V.S.C. Prasanthi, Deepthi Marada
article en

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.

Journal of Water Resource Engineering and Management
Clean water and sanitation
Openalex Percentile: Top 88%
Reservoir Engineering and Simulation Methods
DOIOpen Access PDFOpenAlex
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Harnessing Hydrolgeological Parametrs: Prediction of Water Probability and Levels for Water Well Construction Using Ai-Enabled Models — Pranavi Kannepogu, T. Jhansi Rani, et al. · Journal of Water Resource Engineering and Management (2025) | TGRS Research Map | TGRS