EDITORIAL: INTEGRATION OF HYDROLOGICAL MODELS AND MACHINE LEARNING TECHNIQUES FOR WATER RESOURCES MANAGEMENT

Ren Jie, Chin and Sai Hin, Lai (2025) EDITORIAL: INTEGRATION OF HYDROLOGICAL MODELS AND MACHINE LEARNING TECHNIQUES FOR WATER RESOURCES MANAGEMENT. Journal of Civil Engineering, Science and Technology, 16 (1). pp. 1-5. ISSN 2462-1382

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Abstract

Hydrology and water resources management ensure the sustainable use, conservation, and allocation of water in natural and engineered systems. Climate change, urbanization, and rising water demand necessitate advanced modeling approaches to enhance water security and resilience to extreme hydrological events. This editorial scope explores the integration of conventional hydrological models with machine learning to improve predictive accuracy, decision-making, and resource optimization. Physics-based models such as SWAT, VIC, and HEC-HMS simulate watershed processes, while hydraulic models like HEC-RAS and MIKE SHE assess flood risks. Groundwater models (e.g., MODFLOW) analyze aquifer dynamics, and optimization models support efficient reservoir and watershed management. Despite their reliability, these models require extensive calibration, high-resolution data, and struggle with capturing nonlinear hydrological complexities. Advancements in computational power and data availability enable machine learning to complement traditional models. Algorithms such as ANNs, SVMs, and RF enhance hydrological forecasting, while deep learning methods (LSTMs, CNNs) improve spatio-temporal predictions. Hybrid models integrating physical-based simulations with machine learning-driven corrections reduce uncertainties, enhance computational efficiency, and enable adaptive water management. Machine learning applications extend to flood forecasting, drought risk assessment, and climate change impact analysis, strengthening disaster mitigation efforts. Integrating AI with hydrological models offers promising advancements in real-time monitoring, infrastructure resilience, and water governance. However, challenges related to data availability, model interpretability, and computational complexity remain. Future research should focus on explainable AI, refined hybrid modeling, and machine learning-based decision-support systems. As AI, remote sensing, and big data evolve, their convergence with hydrological sciences will drive more intelligent and sustainable water management solutions.

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