Hydrolearn: a Self-supervised Spatiotemporal Learning Framework for Predicting and Managing Water Resources Under Climate Variability
Year: 2025
Type: article
Abstract: 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 selfsupervised 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 Sangeethavani B
Author: Sangeethavani B
Institution: Gandhigram Rural Institute
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4413021064