Real-Time Flood Risk Mapping Using ML And Iot Sensor Data

Abstract

Real-time flood risk mapping plays a crucial role in disaster management, enabling timely decision-making to mitigate the impact of floods. This paper explores the integration of Machine Learning (ML) techniques with Internet of Things (IoT) sensor data to develop an advanced flood risk mapping system. IoT sensors deployed in flood-prone areas continuously monitor environmental variables such as water levels, rainfall, and soil moisture. These data points are processed using ML algorithms to predict flood risks in real time, offering a dynamic and accurate assessment of flood-prone regions. The system provides near-instantaneous flood risk predictions, significantly enhancing traditional forecasting methods. Results demonstrate the efficacy of machine learning models in accurately predicting flood events, with significant improvements in prediction accuracy and speed compared to conventional methods. This research highlights the potential of combining IoT and ML for improving flood preparedness and response, offering new insights into the application of real-time data for environmental monitoring and disaster risk management.