Flood Early Warning and Prediction System for Tributary Streams

Flood Early Warning system development is relatively new and costly area for developing countries, though it has captured attention of the respective parties since such System can avoid loss lives reduce property damages from floods. Rather than standalone early warning system, with ability to forecast or predict flood events more useful relevant stake holders where be used plan act fast. Geological areas around tributary streams are likely without considerable warnings nature as highly depends on main river behavior. This uses Internet Things (IoT) devices data capture transfer. River Water level, Rain status flow rate (discharge rate) measured using Sensors. An Artificial Neural Network (ANN) trained collected integrated live feed in order water level. By doing so forecasting according current readings. Notifications sent via pre-defined notification channels. Due higher number types considered, ANN predicts level accuracy. collective approach IoT made easier reliable while variables predictions accurate. In addition, these future disaster recovery mitigation planning they kept cloud environment public access.