GeoMorphNet: A Causal-Inference-Based Framework with Spatial-Hierarchical Attention for Water Quality Prediction and Management

GeoMorphNet is a real-time deep learning model that improves water quality prediction by combining different types of data. Traditional water quality checks are often based on limited yearly data and fail to capture changes over time and across locations. GeoMorphNet solves this issue with a multi-step approach. First, it analyzes past state-level data to understand overall trends. Then, it refines these predictions using district-level data for more precise insights. Since real-time water quality can change due to weather and pollution, the model integrates live sensor readings to track environmental shifts. It also uses groundwater survey data to detect hidden contamination sources and regional variations. By combining historical, real-time, and geographical data, GeoMorphN et provides a detailed and accurate assessment of water quality. Extensive testing confirms its reliability, making it a valuable tool for policymakers and environmental experts to manage water resources effectively.