Enhanced Flood Monitoring using Deep Learning and IOT

Flood are one of the major natural hazards, causing significant impairment to structure and defeat of social survives. Early plus accurate Flood Monitoring is vital to mitigate such risks. This work introduces an advanced Flood alert system leveraging a deep learning model, specifically a Recurrent Neural Network (RNN). The system is trained on historical Flood data alongside real-time sensor inputs, including critical parameters including soil moistness, hotness, and moisture, in addition ground vibration. The RNN model effectively captures temporal patterns, enabling precise probability calculations for Flood occurrence. The accomplished RNN method is assessed on unseen test data to validate its predictive accuracy and generalization capability. Standards like accuracy, recall, precision and F1 score are used to measure its performance.