Hybrid Graph Attention Network Model for Smart Water Management and Dam Level Forecasting

The cultural, historical, economic, and environmental sustainability of India is significantly influenced by the water resources provided by dams. Dams are crucial for various purposes, including flood control, irrigation and industrial water supply, and enabling river navigation. Accurate forecasting models are crucial for optimizing dam operations and mitigating hazards in successful stormwater management. This project aims to estimate dam levels using hybrid modelling tactics and data preprocessing methods. Conventional models often yield erroneous forecasts of dam water levels due to their intricate nonlinearity and spatio-temporal variability. To address this, it provides a Hybrid GAT Model that integrates fuzzy logic with dynamic spatio-temporal grammar and an attention-based network. The model combines a fuzzy inference method and an earthquake structure to analyses historical SWM values, accounting for unknown factors affecting dam levels. The findings indicate that the proposed model surpasses existing approaches, with an MAE of 24.33 and an R-value of 0.863, signifying high predictive accuracy. To enhance water resource planning, optimize dam operations, and develop superior disaster mitigation strategies, our findings underscore the necessity of hybrid DL models.