Artificial intelligence-integrated water level monitoring system for flood detection enhancement - Tan, Kar Ban; Kien, Fei Lee; Ng, Zi Neng; Balachandran, Ruthramurthy; Chong, Abraham Shiau Lun; Chan, Kah Yoong (Ismail Saritas, 2024)
Flash floods are increasingly becoming a common disaster in Malaysia, triggered by a combination of natural and human induced factors. The natural factors include climate changes, landforms due to the environmental impacts, while the human-induced factors are associated with the negligence in river conservation, clogged drainage, and polluted water retention systems due to industrial and domestic wastes. These factors affect the water levels in rivers and drainage systems, leading to potential flash floods once the danger mark is exceeded. Flash floods could result in severe property damage and even loss of lives. Considering the devastating impact of flash floods, it is imperative to develop an early warning system that facilitates timely remedial measures. This system could monitor the water levels in rivers and other water retention areas. Herein, this study aims to design a water level monitoring system using a cost-effective camera module powered by the Internet of Things (IoT). The system, which includes an ESP32-Camera module powered by a solar panel, captures the water level data using OpenCV at one-minute intervals. Then, the data are made available on IoT platforms like ThingSpeak, enabling the authorized parties to keep track of the critical water levels in water retention areas.