Design and Evaluation of an IoT-Based Flood Early Warning System Using Conductive Water Level Sensor

Flood disasters frequently cause significant socio-economic losses in developing countries, while many existing early warning systems remain costly, complex, or insufficiently accessible for real-time community use. This study proposes a low-cost IoT-based flood early warning system using a conductive K-0135 water level sensor integrated with a NodeMCU ESP8266 microcontroller and HTTP-based communication architecture. The novelty of this work lies in the use of a conductive sensor with systematic threshold characterization under both static and dynamic conditions to reduce false alarms while maintaining reliable detection performance. The methodology involved sensor characterization through controlled laboratory experiments, including static testing with 0.5 cm depth increments and dynamic testing simulating rainfall splashes. The results show a non-linear increase in sensor output with depth, ranging from 16.3 at 0.0 cm to 565.3 at 4.0 cm. Dynamic testing produced an average maximum output of 424.7, leading to an optimal detection threshold of 425. The integrated system achieved a communication success rate of 100% in delivering real-time alerts via HTTP requests to a web server and Telegram platform. An HTTP error code −11 was observed, corresponding to a timeout condition caused by network latency; however, this did not affect successful alert transmission. The findings are limited to controlled laboratory-scale testing and have not yet been validated under real environmental conditions. Overall, the proposed system demonstrates the feasibility of a low-cost, threshold-based IoT solution for real-time flood early warning applications and highlights its potential for improving community-level disaster preparedness.