FloodIntel: Advancing flood disaster forecasting through comprehensive intelligent system approach

Background: Every year, floods are the most devastating natural disaster that hits Malaysia, causing damage to people’s livelihoods, destroying property and infrastructure, and taking lives. Flood disasters are becoming more frequent and severe, necessitating the development of sophisticated forecasting and early warning systems to mitigate their effects. This study presents the design and implementation of a sophisticated flood forecasting and early warning system, utilizing intelligent technologies to enable timely prediction and proactive measures in high-risk areas. Methods: The proposed system incorporates Internet of Things (IoT) technology to collect real-time data on the river’s water level. The collected data are analyzed using an association rule technique to generate accurate forecasts of prospective flood occurrences. By using this intelligent flood disaster prediction system, users and authorities can receive early warnings and make informed decisions regarding evacuation, resource allocation, and infrastructure reinforcement. The system’s capability to provide early flood forecasts in high-risk areas can substantially enhance flood preparedness and response and save more life. Results: The findings of the study highlight the potential of the system to improve flood risk management strategies and reduce flood-related devastation and human suffering in vulnerable regions. Conclusions: In conclusion, it is important to implement IoT and AI technologies to improve flood prediction systems and reduce the negative effects of flood disasters.