Development of an Early Prediction System for Flood Disasters Based on the Internet of Things (IoT) with the Application of Deep Learning

In this study, we propose the development of an integrated approach to flood prediction systems using the implementation of an artificial neural network approach on the backend system to improve the accuracy of the results. In this study, we propose the development of an integrated approach to flood prediction systems using an artificial neural network approach on the backend system. The Internet of Things (IoT) device used consists of a raspberry pi connected to an ultrasonic sensor and a web camera module. The ultrasonic sensor is used to measure the distance to the water surface, while the web camera module is used to take photos of the current river conditions. Our experiments show that our system successfully monitors river water level conditions, displays data on a web platform, and analyzes data for flood prediction using machine learning with an MSE error rate of 0.038%.