Integrating IoT Sensors and Machine Learning Algorithms for Early Flash Flood Detection System

Khaled Mohamed Abdelmagid; Mohd Fitri Mohd Yakub

doi:10.37934/ctds.5.1.6071

Authors

Khaled Mohamed Abdelmagid Dept. Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur, Malaysia
Mohd Fitri Mohd Yakub Dept. Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.37934/ctds.5.1.6071

Keywords:

Flash flood detection, machine learning algorithms, IoT sensors, early warning systems, real-time data analysis, environmental monitoring, disaster management, agile development, climate change impact

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.