HYDROLEARN: A SELF-SUPERVISED SPATIOTEMPORAL LEARNING FRAMEWORK FOR PREDICTING AND MANAGING WATER RESOURCES UNDER CLIMATE VARIABILITY

Sangeethavani B

HYDROLEARN: A SELF-SUPERVISED SPATIOTEMPORAL LEARNING FRAMEWORK FOR PREDICTING AND MANAGING WATER RESOURCES UNDER CLIMATE VARIABILITY

Authors

Sangeethavani B

Assistant professor, Centre for rural technology, The Gandhigram Rural Institute, Gandhigram, Dindigul District 624302, India.

Keywords:

Water Resource Management, Self-supervised Learning, Spatiotemporal Modeling, Climate Adaptation, Graph Neural Networks

Synopsis

Water resource management faces unprecedented challenges due to climate variability and increasing demand. Traditional hydrological models often fail to capture complex spatiotemporal dependencies in water systems. We propose HydroLearn, a novel self-supervised learning framework that integrates multi-scale spatiotemporal attention mechanisms with adaptive climate encoding to predict water resource availability and optimize management strategies. Our methodology introduces three key innovations: (1) Hierarchical Spatiotemporal Graph Neural Networks (HST-GNN) that model watershed connectivity at multiple scales, (2) Climate-Aware Contrastive Learning (CACL) for self-supervised representation learning from unlabeled meteorological data, and (3) Dynamic Water Balance Optimization (DWBO) that adapts management policies in real-time. Extensive experiments on five major watersheds demonstrate that HydroLearn achieves 23.7% improvement in prediction accuracy over state-of-the-art methods, with 31.2% better performance during extreme climate events. The framework successfully optimized water allocation strategies, reducing shortage events by 42% while maintaining ecological flow requirements.

References

[1] Ferdous, R. (2025). Satellite soil moisture Data-Driven to predict paddy production using machine learning approach. International Journal for Research in Applied Science and Engineering Technology, 13(5), 6640–6652. https://doi.org/10.22214/ijraset.2025.71198

[2] Hänchen, L., Potter, E., Klein, C., Calanca, P., Maussion, F., Gurgiser, W., & Wohlfahrt, G. (2025). A novel framework for analyzing rainy season dynamics in semi-arid environments: a case study in the Peruvian Rio Santa basin. Hydrology and Earth System Sciences, 29(12), 2727–2747. https://doi.org/10.5194/hess-29-2727-2025

[3] Jacobs, T. (2025). Comments: If US shale levels off, will innovation pick up the slack? Journal of Petroleum Technology, 77(07), 1–4. https://doi.org/10.2118/0725-0001-jpt

[4] Kumar, P., Agrawal, T., Shah, H., Moronfoye, A., Dolant, A., Wang, J., Druhan, J., & Goodwell, A. (2025). From information flow to generative insights: Advancing AI/ML applications for predicting solute dynamics in streams. ARPHA Conference Abstracts, 8. https://doi.org/10.3897/aca.8.e155752

[5] Nisa, E., Adriyansyah, N., & Fahria, I. (2025). Prediction of Water Availability in Kolong ST 12 Reservoir as a Raw Water Source using Singular Spectrum Analysis. Bentang Jurnal Teoritis Dan Terapan Bidang Rekayasa Sipil, 13(2), 261–275. https://doi.org/10.33558/bentang.v13i2.10605

[6] Ratterman, C., Zhang, W., Affram, G., & Bean, B. (2025). Improving CFSv2 Snow Water Equivalent Forecasts in the Colorado River Basin with Generalized Analog Regression Downscaling. Weather and Forecasting. https://doi.org/10.1175/waf-d-23-0196.1

[7] Sbai, Z. (2025). Deep learning models and their ensembles for robust agricultural yield prediction in Saudi Arabia. Sustainability, 17(13), 5807. https://doi.org/10.3390/su17135807

[8] Shaikh, M. A. (2025). Advancing Groundwater Resource Management through Artificial Intelligence: Future Directions for GSDA Solapur. International Scientific Journal of Engineering and Management, 04(05), 1–9. https://doi.org/10.55041/isjem03791

[9] V, V., Pimpale, M., Kapure, V., Mishra, P., Rokade, A., Bhushan, T., & Singh, J. (2025). A Hybrid IoT and Machine Learning Framework for Smart Greenhouse Automation in Sustainable Agriculture. International Research Journal of Multidisciplinary Technovation, 58–69. https://doi.org/10.54392/irjmt2545

[10] Van Zweel, K. N., Gourdol, L., Iffly, J. F., Léonard, L., Barnich, F., Pfister, L., Zehe, E., & Hissler, C. (2025). One year of high-frequency monitoring of groundwater physico-chemical parameters in the Weierbach experimental catchment, Luxembourg. Earth System Science Data, 17(5), 2217–2229. https://doi.org/10.5194/essd-17-2217-2025

[11] Varalaxmi, B. (2025). Smart irrigation system. International Scientific Journal of Engineering and Management, 04(06), 1–9. https://doi.org/10.55041/isjem04659