Comparative Review of Intelligent Flood Monitoring and Risk Assessment Systems: An IoT–Random Forest Perspective

Abstract

Flooding remains one of the most destructive natural hazards worldwide, causing significant loss of life, damage to infrastructure, and socioeconomic disruption. Recent advances in the Internet of Things (IoT), wireless communication, and machine learning have enabled the development of intelligent flood monitoring systems capable of supporting real-time environmental monitoring and timely decision-making. This study presents a comparative review of recent intelligent flood monitoring systems with the objective of evaluating current technological approaches and identifying effective design strategies for flood risk assessment. The review systematically compares existing studies based on key criteria, including environmental sensing parameters, IoT architectures, wireless communication technologies, machine learning techniques, flood prediction capabilities, web-based monitoring platforms, and automated early warning mechanisms. Particular attention is given to an integrated framework that combines IoT-based environmental sensing with the Random Forest machine learning algorithm and its relative strengths when compared with other reported approaches. The comparative evaluation indicates that integrating multiple environmental sensors with Random Forest models generally provide improved predictive reliability, greater robustness to heterogeneous environmental data, reduced susceptibility to overfitting, and enhanced support for real-time flood risk classification. Furthermore, the incorporation of web-based monitoring interfaces and automated alert mechanisms contributes to more efficient dissemination of flood information and improved emergency preparedness. The findings provide a comprehensive overview of recent technological developments and highlight the value of integrating IoT sensing and Random Forest-based analytics as a practical direction for developing reliable, scalable, and intelligent flood monitoring and risk assessment systems.