Enhancing flood monitoring and prevention using machine learning and IoT integration

Syed Asad Shabbir Bukhari (), Imran Shafi (), Jamil Ahmad (), Hammad Tanveer Butt (), Tahir Khurshaid () and Imran Ashraf ()
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Syed Asad Shabbir Bukhari: National University of Sciences and Technology (NUST)
Imran Shafi: National University of Sciences and Technology (NUST)
Jamil Ahmad: Abasyn University, Islamabad Campus
Hammad Tanveer Butt: National University of Sciences and Technology (NUST)
Tahir Khurshaid: Yeungnam University
Imran Ashraf: Yeungnam University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 4, No 44, 4837-4864

Abstract: Abstract Floods subject human life, property, and infrastructure to the highest level of danger; hence, proper monitoring with proper prevention action is necessary. This paper proposes a new approach to enhancing flood management and prevention by integrating Internet of Things (IoT) infrastructure with machine learning (ML) methods. It is composed of three various stations: the water station, which is equipped with a radar sensor to monitor a possible overflow of water; repeater stations, to ensure the data produced and transmitted to the server is continuous in nature; and siren stations that include environmental sensors, like wind speed, wind direction, humidity, air pressure, atmospheric temperature, and rain gauges. It is composed of three various stations: the water station, with radar sensors for overflow detection; the repeater station, to ensure continuous data transmission and siren stations with environmental sensors, (e.g., wind speed, temperature). The collected data forms the basis for a dataset used to train machine learning models. Preprocessing techniques are used to clean the data and use 1D Convolutional neural network (CNN) and multivariate Long Short-Term Memory (M-LSTM) networks to predict flood events. The 1D-CNN captures spatial relations, while M-LSTM addresses temporal dependencies in multivariate time series data. Our integrated approach shows improved performance with an MSE of 0.018, outperforming existing methods. Future work will explore advanced ML algorithms, enhanced sensor data integration, and decision support systems for flood management.

Keywords: Flood prediction; Disaster risk reduction; Internet of Things; Machine learning; Flood modeling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-024-06986-3

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