Development of internet of things based flood monitoring system with real-time dashboard at flood monitoring project seelab kencana SDN. BHD. Shah Alam

Abstract

This research develops an Internet of Things (IoT)-based flood monitoring system equipped with a real-time web dashboard and artificial intelli- gence (AI)-based prediction features, as a solution to the frequent flooding in Shah Alam, Malaysia, due to high rainfall and a sub-optimal drainage system. The system uses AJ-SR04M sensors to measure water lev- els, MH-RD sensors to detect rainfall, and ESP32 and ESP32-CAM for data processing and image cap- ture. Data is sent wirelessly to a Django-based back- end server and displayed on a web dashboard. The backend also processes the data and runs a time-series forecasting-based machine learning model to predict conditions five minutes ahead, with the predicted re- sults displayed alongside the actual data. In addition, the system provides automatic notifications via Tele- gram when sensor values exceed a threshold. The test results show that the system is able to display envi- ronmental data accurately and responsively, provide real-time early warnings, and generate predictions that match historical trends. The system has successfully supported effective flood risk mitigation in vulnerable areas by providing accurate sensor data and AI-based predictions that match historical trends.