Integrating IoT Sensors and Machine Learning Algorithms for Early Flash Flood Detection System
Year: 2025
Type: article
Abstract: The rising frequency of flash floods due to climate change demands efficient detection systems to reduce their impact. This study presents the "Early Flash Flood Detection System Using Machine Learning Algorithms," which integrates IoT sensors and machine learning for accurate, real-time flood prediction. Developed with Agile methodology, the project utilized key technologies like Flutter and TensorFlow to enhance functionality and user engagement. Testing showed 72% prediction accuracy, demonstrating the system's potential as a scalable solution for disaster management, advancing public safety, and fostering resilient communities.
Authors: Khaled Mohamed Abdelmagid, Mohd Fitri Mohd Yakub
Institution: University of Technology Malaysia
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4408219378