EDITORIAL: INTEGRATION OF HYDROLOGICAL MODELS AND MACHINE LEARNING TECHNIQUES FOR WATER RESOURCES MANAGEMENT
DOI:
https://doi.org/10.33736/jcest.9191.2025Keywords:
hydrological modelling, integration, machine learning, water resources managementAbstract
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.
References
Engeland, K., & Alfredsen, K. (2020). Hydrology and water resources management in a changing world. Hydrology Research, 51(2), 143–145. https://doi.org/10.2166/9781789062175
Singh, P. K., Dey, P., Jain, S. K., & Mujumdar, P. P. (2020). Hydrology and water resources management in ancient India. Hydrology and Earth System Sciences, 24(10), 4691–4707. https://doi.org/10.5194/hess-24-4691-2020
Abbaspour, K., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., & Klove, B. (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733–752. https://doi.org/10.1016/j.jhydrol.2015.03.027
Janjic, J., & Tadic, L. (2023). Fields of application of SWAT hydrological model- A review. Earth, 4(2), 331–344. https://doi.org/10.3390/earth4020018
Tesemma, Z., Wei, Y., Peel, M. C., & Western, A. W. (2015). The effect of year-to-year variability of leaf area index on Variable Infiltration Capacity model performance and simulation of runoff. Advances in Water Resources, 83, 310–322. https://doi.org/10.1016/j.advwatres.2015.07.002
Xia, Y., Mocko, D. M., Wang, S., Pan, M., Kumar, S. V, Peters-Lidard, C. D., Wei, H., Wang, D., & Ek, M. B. (2018). Comprehensive Evaluation of the Variable Infiltration Capacity (VIC) Model in the North American Land Data Assimilation System. Journal of Hydrometeorology, 19(11), 1853–1879. https://doi.org/10.1175/JHM-D-18-0139.1
Guduru, J. U., & Mohammed, A. S. (2024). Hydrological modeling using HEC-HMS model, case of Tikur Wuha River Basin, Rift Valley River Basin, Ethiopia. Environmental Challenges, 17, 101017. https://doi.org/10.1016/j.envc.2024.101017
Yu, X., & Zhang, J. (2023). The application and applicability of HEC-HMS model in flood simulation under the condition of river basin urbanization. Water, 15(12), 2249. https://doi.org/10.3390/w15122249
Costabile, P., Costanzo, C., Ferraro, D., Macchione, F., & Petaccia, G. (2020). Performances of the new HEC-RAS version 5 for 2-D hydrodynamic-based rainfall-runoff simulations at basin scale: Comparison with a state-of-the art model. Water, 12(9), 2326344. https://doi.org/10.3390/w12092326
Zainal, N. N., & Talib, S. H. (2024). Review paper on applications of the HEC-RAS model for flooding, agriculture, and water quality simulation. Water Practice and Technology, 19(7), 2883–2900. https://doi.org/10.2166/wpt.2024.173
Jayatilaka, C. J., Storm, B., & Mudgway, L. B. (1998). Simulation of water flow on irrigation bay scale with MIKE-SHE. Journal of Hydrology, 208(1–2), 108–130. https://doi.org/10.1016/S0022-1694(98)00151-6
Xevi, E., Christiaens, K., Espino, A., Sewnandan, W., Mallants, D., Sorensen, H., & Feyen, J. (1997). Calibration, validation and sensitivity analysis of the MIKE-SHE model using the Neuenkirchen catchment as case study. Water Resources Management, 11, 219–242. https://doi.org/10.1023/A:1007977521604
Hughes, J. D., Russcher, M. J., Langevin, C. D., Morway, E. D., & McDonald, R. R. (2022). The MODFLOW Application Programming Interface for simulation control and software interoperability. Environmental Modelling & Software, 148, 105257. https://doi.org/10.1016/j.envsoft.2021.105257
Khadri, S. F., & Pande, C. (2016). Ground water flow modeling for calibrating steady state using MODFLOW software: a case study of Mahesh River basin, India. Modeling Earth Systems and Environment, 2(39). https://doi.org/10.1007/s40808-015-0049-7
Guariso, G., Rinaldi, S., & Soncine-Sessa, R. (1985). Decision support systems for water management: The Lake Como case study. European Journal of Operational Research, 21(3), 295–306. https://doi.org/10.1016/0377-2217(85)90150-X
Vaz, T. G., Oliveira, B. B., & Brandao, L. (2024). Optimisation for operational decision-making in a watershed system with interconnected dams. Applied Energy, 367, 123385. https://doi.org/10.1016/j.apenergy.2024.123385
Loh, W., Chin, R., Ling, L., Lai, S., & Soo, E. (2021). Application of machine learning model for the prediction of settling velocity of fine sediments. Mathematics, 9(23), 3141. https://doi.org/10.3390/math9233141
Soo, E. Z., Chin, R. J., Ling, L., Huang, Y. F., Lee, J. L., & Lee, F. W. (2024). Streamflow simulation and forecasting using remote sensing and machine learning techniques. Ain Shams Engineering Journal, 15(12), 103099. https://doi.org/10.1016/j.asej.2024.103099
Liu, Y., Wang, C., Jiang, C., Chin, R. J., Xiang, Z., Long, Y., others, & Wu, Z. (2024). Water storage capacity estimation for a large complex lake system incorporating the water levels during flooding season. Journal of Hydrology: Regional Studies, 51, 101634. https://doi.org/10.1016/j.ejrh.2023.101634
Liu, Y., Yang, Y., Chin, R. J., Wang, C., & Wang, C. (2023). Long short-term memory (LSTM) based model for flood forecasting in Xiangjiang river. KSCE Journal of Civil Engineering, 27(11), 5030–5040. https://doi.org/10.1007/s12205-023-2469-7
Deng, B., Liu, P., Chin, R. J., Kumar, P., Jiang, C., Xiang, Y., Liu, Y., Lai, S. H., & Luo, H. (2022). Hybrid metaheuristic machine learning approach for water level prediction: A case study in Dongting Lake. Frontiers in Earth Science, 10, 928052. https://doi.org/10.3389/feart.2022.928052
Loh, W. S., Ling, L., Chin, R. J., Lai, S. H., Loo, K. K., & Seah, C. S. (2024). A comparative analysis of missing data imputation techniques on sedimentation data. Ain Shams Engineering Journal, 15(6), 102717. https://doi.org/10.1016/j.asej.2024.102717
Deng, B., Lai, S. H., Jiang, C., Kumar, P., El-Shafie, A., & Chin, R. J. (2021). Advanced water level prediction for a large-scale river--lake system using hybrid soft computing approach: A case study in Dongting Lake, China. Earth Science Informatics, 14, 1987–2001. https://doi.org/10.1007/s12145-021-00665-8
Yao, Y., Yang, X., Lai, S. H., & Chin, R. J. (2021). Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network. Natural Hazards, 107, 601–616. https://doi.org/10.1007/s11069-021-04597-w
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 UNIMAS Publisher

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Upon acceptance of an article, the corresponding author on behalf of all authors will be asked to complete and upload the Copyright Transfer Form (refer to Copyright Issues for more information on this) alongside the electronic proof file.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and refer the publication in the Journal.
2) For open-access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds the copyright or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subjected to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) are responsible to ensure their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. The corresponding author has obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If the submitted manuscript includes materials from others, the authors have obtained permission from the copyright owners.
5) In signing this statement, the author(s) declare that the researches which they have conducted comply with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving humans or the use of animal samples must obtain approval from the Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that they have no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor(s) or UNIMAS Publisher.