Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing

Flood disasters affect millions of people across the world by causing severe loss of life and colossal damage to property. Internet of Things (IoT) has been applied in areas such as flood prediction, flood monitoring, and flood detection. Although IoT technologies cannot stop the occurrence of flood disasters, they are an exceptionally valuable apparatus for conveyance of catastrophe readiness data. Advances have been made in flood prediction using artificial neural networks (ANN). Despite the various advancements in flood prediction systems, there has been less focus on the utilisation of edge computing for improved efficiency and reliability of such systems. In this paper, we present a system for short-term flood prediction that uses IoT and ANN, where the prediction computation is carried out on a low power edge device. The system monitors real-time rainfall and water level sensor data and uses the temporal correlative information for ahead-of-time prediction of flood water levels using long short-term memory. The system can be deployed on battery-powered IoT devices. The results of evaluating a prototype of the system indicate that our model is suitable for real-time flood prediction. Furthermore, we obtain a low response time for running the ANN prediction analysis on the low-power edge device. The application of ANN with edge computing will help improve the efficiency of real-time flood early warning systems by bringing the prediction computation close to where data is collected.