Development and Implementation of an IoT-Based Early Flood Detection and Monitoring System Utilizing Time Series Forecasting for Real-Time Alerts in Resource-Constrained Environments

Abstract

Flooding is a recurrent natural catastrophe in Malaysia, demanding excellent early warning and monitoring systems to reduce the impact on those affected. Traditional flood monitoring systems have severe limitations, including reliance on human data gathering, a lack of real-time capabilities, expensive prices, and slow response times, particularly in developing countries. To solve these issues, this research aims to design an Early Flood Detection and Monitoring System that uses Internet of Things (IoT) technology to provide a cost-effective, efficient, and real-time solution for detecting increasing water levels and sending early alerts. The system uses commonly accessible components such as NodeMCU ESP8266, HC-SRO4 Ultrasonic Sensors, and MAX7219 Dot Matrix Displays to build a sensor network in flood-prone locations. These sensors continually send data to a central processing unit for analysis, and a machine learning model based on Time Series forecasting is used for predictive analysis in the ThingSpeak platform, which is available via an internet dashboard for real-time monitoring. Testing revealed that the system efficiently monitors water levels and sends timely alerts, hence increasing flood readiness and response. Its real-time monitoring capacity guarantees communities receive early information, allowing for proactive flood risk mitigation actions. This study presents a scalable and sustainable solution for improving flood monitoring efficiency and reliability, addressing the limitations of traditional systems and significantly advancing flood preparedness and resilience, thereby supporting effective flood mitigation in resource-constrained environments.