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Machine learning hybrid dynamic best model selection algorithm for real-time fire prediction using IoT-enabled multi-sensor data in buildings

Mujeeb Ali Khan Weiguo Song Abbas Khan Mazhar Ali Rehmat Karim Jun Zhang

安全科学与韧性（英文）Issue(2)：125-142,18.

安全科学与韧性（英文）Issue(2)：125-142,18.DOI:10.1016/j.jnlssr.2025.100236

Machine learning hybrid dynamic best model selection algorithm for real-time fire prediction using IoT-enabled multi-sensor data in buildings

Mujeeb Ali Khan ¹Weiguo Song ²Abbas Khan ²Mazhar Ali ³Rehmat Karim ²Jun Zhang²

作者信息

1. State Key Laboratory of Fire Sciences,University of Science and Technology of China,Hefei,230026,China||Hefei Keda Li'an Safety Technology Co.Ltd.,Hefei,230088,China
2. State Key Laboratory of Fire Sciences,University of Science and Technology of China,Hefei,230026,China
3. Telecommunication System Research Laboratory(TSLR),Chulalongkorn University,Bangkok 10330,Thailand
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摘要

关键词

Machine learning optimization/Adaptive classifier selection/Sensor fusion/Fire hazard mitigation/Real-time analytics

Key words

Machine learning optimization/Adaptive classifier selection/Sensor fusion/Fire hazard mitigation/Real-time analytics

引用本文复制引用

Mujeeb Ali Khan,Weiguo Song,Abbas Khan,Mazhar Ali,Rehmat Karim,Jun Zhang..Machine learning hybrid dynamic best model selection algorithm for real-time fire prediction using IoT-enabled multi-sensor data in buildings[J].安全科学与韧性（英文）,2026,(2):125-142,18.

基金项目

This work was supported by the National Natural Science Foundation of China(52321003)and the China Scholarship Council(CSC).The authors thank Hefei Keda Li'an Safety Technology Co.,Ltd.,for providing the experimental devices and facilities.Additionally,we are profoundly grateful to Prof.Weiguo Song for his valuable comments and guidance throughout this research. （52321003）

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