A flood expert system using machine learning and IoT: warning, detection, and prediction

Soleyman Nezhadbasaidu (), Mehdi Gheisari (), Alireza Kheyrkhah (), Mohammad Hossein Modirrousta (), Xiuqing Wang, Sherif Moussa, Hemn Barzan Abdalla () and Belal Abuhaija ()
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Soleyman Nezhadbasaidu: Shaoxing University
Mehdi Gheisari: Shaoxing University
Alireza Kheyrkhah: University of Tabriz
Mohammad Hossein Modirrousta: K. N. Toosi University of Technology
Xiuqing Wang: Shaoxing University
Sherif Moussa: Canadian University of Dubai
Hemn Barzan Abdalla: Wenzhou-Kean University
Belal Abuhaija: Wenzhou-Kean University

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 3, No 13, 1106-1122

Abstract: Abstract The integration of flood detection and warning systems utilizing machine learning and the Internet of Things (IoT) represents a significant advancement in flood risk management. Floods, being unpredictable natural disasters, necessitate timely and effective detection systems to mitigate potential damages. Traditional flood control measures, while essential, are often costly and cannot fully eliminate flood risks. Therefore, a non-structural approach that includes real-time monitoring and alerts is critical. This article proposes a comprehensive flood detection, warning, and prediction system that employs IoT devices to monitor water levels in various water bodies, including rivers, dams, canals, and lakes. The IoT nodes continuously collect data on water levels and environmental conditions. When the water level surpasses a predetermined threshold, alerts are dispatched to users within a 5 km radius. An Android application displays vital information such as temperature, water levels, flood locations, and predictions. The system’s effectiveness is underscored by the performance of the Linear Regression algorithm, which achieves an accuracy rate of 79.01%. This is notably higher than other algorithms like Decision Tree and Naïve Bayes in the context of flood detection using IoT. Additionally, the system boasts a recall rate of 91.00%, indicating its strong capability to correctly identify flooding events, while maintaining a precision rate of 84.30%.

Keywords: Flood prediction; Flood intelligent detection and warning; Flood device and application; IoT; WEKA; Linear regression; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-025-02710-x

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