A Human-AI Collaborative Framework for Sustainable Water Resource Management Using Multi-Modal Sensing and Explainable Deep Learning

Sustainable water resource management has become a global imperative as urbanization, climate variability, and population growth continue to intensify pressure on freshwater ecosystems. Existing hydrological prediction systems rely on isolated data streams and opaque machine learning models, limiting their usefulness for large-scale decision-making and policy adoption. This paper proposes a novel Human-AI Collaborative Framework that integrates multi-modal sensing including satellite imagery, IoT-based water level sensors, meteorological variables, and historical hydrological records with an explainable deep learning architecture designed to support transparent and accountable water governance. The system employs a hybrid cross-attention fusion model to combine spatial, temporal, and environmental features, while SHAP- and Grad-CAM-based interpretability modules provide human-readable explanations for each prediction. A human-in-the-loop reinforcement mechanism further allows domain experts to refine model outputs, enabling continuous performance improvement. Experimental evaluations conducted on real-world river basin datasets demonstrate a 21% improvement in forecast accuracy and a 34% reduction in false flood alarms compared with existing baselines, while qualitative assessments show enhanced user trust and decision acceptance among water management authorities. By combining transparency, accuracy, and human oversight, the proposed framework represents a significant step toward AI-enabled sustainable water resource management, supporting the broader goals of SDG 6 (Clean Water) and SDG 13 (Climate Action).