EDITORIAL: INTEGRATION OF HYDROLOGICAL MODELS AND MACHINE LEARNING TECHNIQUES FOR WATER RESOURCES MANAGEMENT
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 hydrologi...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
UNIMAS Publisher
2025
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/50522/1/Paper%202.pdf http://ir.unimas.my/id/eprint/50522/ https://publisher.unimas.my/ojs/index.php/JCEST/article/view/9191 https://doi.org/10.33736/jcest.9191.2025 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|