Intelligent Multi-Sensor IoT Framework for Predictive Flood Risk Assessment and Smart Dam Safety
TL;DR
An intelligent Internet of Things framework that combines real-time water-level monitoring with machine-learning-based flood-risk classification for smart dam safety and combines local safety alerts with remote IoT monitoring and predictive analytics is presented.
Abstract
Floods associated with intense rainfall, rapid water-level rise, and sudden dam discharge pose serious risks to communities, infrastructure, agriculture, and the environment. Conventional flood alert systems generally rely on predefined water-level thresholds and therefore respond only after a critical condition has occurred. This paper presents an intelligent Internet of Things (IoT) framework that combines real-time water-level monitoring with machine-learning-based flood-risk classification for smart dam safety. The proposed system is developed from an existing IoT flood-alert prototype using a NodeMCU ESP8266, multi-level water sensors, LCD display, buzzer, and Wi-Fi communication. The original threshold-based monitoring mechanism is extended with a Random Forest (RF) classifier to provide intelligent classification of water conditions into Normal, Warning, and Critical states. The sensor layer continuously acquires water-level information, while the ESP8266 performs local processing and communicates the observations through Wi-Fi. A structured dataset containing sensor-state features and corresponding flood-risk labels is used for model development. The Random Forest approach is selected because of its ability to model nonlinear relationships, handle categorical and numerical features, and provide computationally efficient inference. The resulting framework combines local safety alerts with remote IoT monitoring and predictive analytics. The proposed architecture provides a practical pathway for transforming a low-cost threshold-based flood alert prototype into an intelligent early-warning system. The reported prototype observations confirm correct detection of low, medium, and highwater conditions and immediate activation of the danger alarm. Quantitative machine-learning performance should be reported only after validation using a sufficiently large measured dataset collected from the deployed system.