Adla Andalu; M Gopal Naik; Sandeep Budde

doi:10.26599/JGSE.2026.9280070

doi: 10.26599/JGSE.2026.9280070

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出版历程
- 收稿日期: 2024-12-22
- 录用日期: 2025-07-28
- 网络出版日期: 2025-10-22
- 刊出日期: 2026-03-15

AI and ML in groundwater exploration and water resources management: Concepts, methods, applications, and future directions

Adla Andalu¹,
M Gopal Naik¹,
Sandeep Budde^{2, 3, 4
, ,}

1.
Department of Civil Engineering, Osmania University, Hyderabad, Telangana, India
2.
Bharti Institute of Public Policy, Indian School of Business, Hyderabad, India
3.
Nitte School of Architecture Planning and Design, Bangalore, Nitte Deemed University, India
4.
School of Urban and Regional Planning, University of Alberta, Edmonton AB - T6G 2R3, Canada

More Information

Corresponding author: sbudde@ualberta.ca

摘要

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Abstract: The integration of Artificial Intelligence (AI) and Machine Learning (ML) into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges. This review explores key AI and ML concepts, methodologies, and their applications in hydrology, focusing on groundwater potential mapping, water quality prediction, and groundwater level forecasting. It discusses various data acquisition techniques, including remote sensing, geospatial analysis, and geophysical surveys, alongside preprocessing methods that are essential for enhancing model accuracy. The study highlights AI-driven solutions in water distribution, allocation optimization, and real-time resource management. Despite their advantages, the application of AI and ML in water sciences faces several challenges, including data scarcity, model reliability, and the integration of these tools with traditional water management systems. Ethical and regulatory concerns also demand careful consideration. The paper also outlines future research directions, emphasizing the need for improved data collection, interpretable models, real-time monitoring capabilities, and interdisciplinary collaboration. By leveraging AI and ML advancements, the water sector can enhance decision-making, optimize resource distribution, and support the development of sustainable water management strategies.
- Artificial intelligence /
- Machine learning /
- Groundwater exploration /
- Hydrological modeling /
- Remote sensing applications /
- Water resources management

HTML全文

Figure 1. Key research areas in the application of AI in Hydrology (Biazar et al. 2025)

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Figure 2. Application of various AI methods for groundwater level prediction from 2001 to 2023 (Pourmorad et al. 2024). Note: ANN – Artificial Neural Network, ANFIS - Adaptive Neuro-Fuzzy Inference Systems, SVM – Support vector Machines, DL- Deep Learning, GP- Genetic Programming

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