Flood Forecasting Model Using LSTM-Neural Network-Application and Challenges

Public safety in disaster operations relies heavily on accurate predictions which help minimize flood damages. The Long Short-Term Memory (LSTM) neural network demonstrates exceptional forecasting abilities in time-series applications thus making it an ideal choice for flood prediction operations. The presented research design implements a data-based flood prediction model which unites LSTM neural networks and collects hydrological information together alongside meteorological and topographical data to enhance forecasting precision. The predictive model uses IoT sensor along with satellite observation data to optimize hyperparameter adjustments which leads to better generalization outcomes. The forecasting performance is validated by three metrics including RMSE, MAE and R² which ensure dependable results. The model tackles three common limitations such as scarce data and excessive noise and calculation restrictions using transfer learning and regularization capabilities and cloud storage deployment. The experimental results show that LSTM maintains superiority over conventional statistical models and brings an effective scalable flood risk management system. The research investigates both hybrid AI systems and explainable AI approaches for enhancing AI readability and accuracy levels.