Flood Detection System Using IoT and Machine Learning

There is a severe lack of effective technology for detecting floods at the community level, which this study aims to address. Residents are frequently unprepared for the sudden increase in floodwater levels. There is an immediate need for an easily accessible and dependable flood monitoring solution due to the significant risk to public safety and property posed by the lack of early local warning capabilities. The research employs IoT devices, including ultrasonic sensors, water level sensors, and DHT22 (Digital Humidity, and Temperature Sensor), strategically positioned to monitor water levels in real-time. This data is utilized in machine learning models such as decision trees, random forests, and linear regressions to predict floods. The IoT-based system employs real-time monitoring and a machine learning algorithm to provide early flood warnings. Among all models utilized in this research, the Decision Tree achieved the highest accuracy at 98.14%, demonstrating superior efficacy compared to alternatives such as Random Forest and Linear Regression in flood prediction. It reflects the integration of IoT and innovative models for machine learning flood warning systems, thereby promoting the establishment of superior preventive and contingency measures. This study can change flood detection structures and methods, especially in areas with underdeveloped flood monitoring systems. The system saves lives and property from floods and helps authorities allocate and manage resources efficiently. This research contributes to disaster management knowledge by integrating IoT and machine learning to address complex environmental issues.