Flood Early Warning and Prediction System for Tributary Streams

Abstract

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