Machine Learning-Based Fire Detection: A Comprehensive Review and Evaluation of Classification Models

Adildabay Secilmis - Istanbul Technical University (ITU), Istanbul, Turkey
Nurullah Aksu - Istanbul Technical University (ITU), Istanbul, Turkey
Fares Dael - İzmir Bakırçay University, İzmir, Turkey
Ibraheem Shayea - Istanbul Technical University (ITU), Istanbul, Turkey
Ayman El-Saleh - A Sharqiyah University (ASU), Ibra, Oman

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Fires, regardless of their origin being natural events or human-induced, provide substantial economic and environmental hazards. Therefore, the development of efficient fire detection systems is of utmost importance. This study provides a comprehensive examination of the extant body of literature about studies on fire detection utilizing machine learning techniques. Significantly, the studies employed three distinct categories of datasets: pictures, data derived from Wireless Sensor Networks (WSNs), or a hybrid amalgamation of both. Our work mainly aims to categorize fire-related data utilizing four distinct classification models: Support Vector Machines (SVMs), Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP). The model with the highest accuracy and ROC curve performance was identified through experimental analysis. The results of our study indicate that the MLP model exhibits the highest overall accuracy, achieving a score of 0.997. In this study, we analyze the learning curves to showcase the positive training dynamics of our model. Additionally, we explore the scalability of our model to ensure its suitability in real-world situations. In general, our research underscores the possibility of employing machine learning methodologies for fire detection, specifically emphasizing the effectiveness of the Multilayer Perceptron (MLP) model. This study contributes to the existing literature by offering valuable insights into the performance of several categorization models and conducting a comprehensive investigation of the Multilayer Perceptron (MLP) architecture. The results of our study have the potential to contribute to the advancement of fire detection systems, leading to enhanced accuracy and efficiency. This, in turn, may mitigate the adverse impacts of fires on both society and the environment.


IoT; CNN; MLP; image; Machine Learning

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B. Kim and J. Lee, “A Video-Based Fire Detection Using Deep Learning Models,†Applied Sciences, vol. 9, no. 14, p. 2862, 2019, doi: 10.3390/app9142862.

N. Moussa et al., “A reinforcement learning based routing protocol for software-defined networking enabled wireless sensor network forest fire detection,†Future Generation Computer Systems, vol. 149, pp. 478–493, 2023, doi: 10.1016/j.future.2023.08.006.

H. C. Reis and V. Turk, “Detection of forest fire using deep convolutional neural networks with transfer learning approach,†Appl Soft Comput, vol. 143, p. 110362, 2023, doi: 10.1016/j.asoc.2023.110362.

X. Yang et al., “Preferred vector machine for forest fire detection,†Pattern Recognit, vol. 143, p. 109722, 2023, doi: 10.1016/j.patcog.2023.109722.

F. Saeed, A. Paul, P. Karthigaikumar, and A. Nayyar, “Convolutional neural network based early fire detection,†Multimed Tools Appl, vol. 79, no. 13, pp. 9083–9099, 2020, doi: 10.1007/s11042-019-07785-w.

S. K. Bhoi et al., “FireDS-IoT: A Fire Detection System for Smart Home Based on IoT Data Analytics,†in 2018 International Conference on Information Technology (ICIT), 2018, pp. 161–165. doi: 10.1109/ICIT.2018.00042.

A. H. Altowaijri, M. S. Alfaifi, T. A. Alshawi, A. B. Ibrahim, and S. A. Alshebeili, “A Privacy-Preserving Iot-Based Fire Detector,†IEEE Access, vol. 9, pp. 51393–51402, 2021, doi: 10.1109/ACCESS.2021.3069588.

H. Fang, M. Xu, B. Zhang, and S. M. Lo, “Enabling fire source localization in building fire emergencies with a machine learning-based inverse modeling approach,†Journal of Building Engineering, vol. 78, p. 107605, 2023, doi: 10.1016/j.jobe.2023.107605.

A. Biswas, S. K. Ghosh, and A. Ghosh, “Early Fire Detection and Alert System using Modified Inception-v3 under Deep Learning Framework,†Procedia Comput Sci, vol. 218, pp. 2243–2252, 2023, doi: 10.1016/j.procs.2023.01.200.

“Smoke Detection Dataset.†Accessed: Oct. 18, 2023. [Online]. Available:

A. Gaur et al., “Fire Sensing Technologies: A Review,†IEEE Sens J, vol. 19, no. 9, pp. 3191–3202, 2019, doi: 10.1109/JSEN.2019.2894665.

J. S. Almeida, S. K. Jagatheesaperumal, F. G. Nogueira, and V. H. C. de Albuquerque, “EdgeFireSmoke++: A novel lightweight algorithm for real-time forest fire detection and visualization using internet of things-human machine interface,†Expert Syst Appl, vol. 221, p. 119747, 2023, doi: 10.1016/j.eswa.2023.119747.

T. Song, R. Li, B. Mei, J. Yu, X. Xing, and X. Cheng, “A Privacy Preserving Communication Protocol for IoT Applications in Smart Homes,†IEEE Internet Things J, vol. 4, no. 6, pp. 1844–1852, 2017, doi: 10.1109/JIOT.2017.2707489.

C. Park, W. Park, S. Jeon, S. Lee, and J.-B. Lee, “Application and evaluation of machine-learning model for fire accelerant classification from GC-MS data of fire residue,†2021, pp. 231–239. doi: 10.5806/AST.2021.34.5.231.

S. Khan, K. Muhammad, S. Mumtaz, S. W. Baik, and V. H. C. de Albuquerque, “Energy-Efficient Deep CNN for Smoke Detection in Foggy IoT Environment,†IEEE Internet Things J, vol. 6, no. 6, pp. 9237–9245, 2019, doi: 10.1109/JIOT.2019.2896120.

J. S. Almeida, C. Huang, F. G. Nogueira, S. Bhatia, and V. H. C. de Albuquerque, “EdgeFireSmoke: A Novel Lightweight CNN Model for Real-Time Video Fire–Smoke Detection,†IEEE Trans Industr Inform, vol. 18, no. 11, pp. 7889–7898, 2022, doi: 10.1109/TII.2021.3138752.

L. Yang, J. Luo, Y. Xu, Z. Zhang, and Z. Dong, “A Distributed Dual Consensus ADMM Based on Partition for DC-DOPF With Carbon Emission Trading,†IEEE Trans Industr Inform, vol. 16, no. 3, pp. 1858–1872, 2020, doi: 10.1109/TII.2019.2937513.

S. Wang, Y. Zhang, T.-H. Hsieh, W. Liu, F. Yin, and B. Liu, “Fire situation detection method for unmanned fire-fighting vessel based on coordinate attention structure-based deep learning network,†Ocean Engineering, vol. 266, p. 113208, 2022, doi: 10.1016/j.oceaneng.2022.113208.

R. Fan and M. Pei, “Lightweight Forest Fire Detection Based on Deep Learning,†in 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021, pp. 1–6. doi: 10.1109/MLSP52302.2021.9596409.

M. Daibo, “Toroidal vector-potential transformer,†in 2017 Eleventh International Conference on Sensing Technology (ICST), 2017, pp. 1–4. doi: 10.1109/ICSensT.2017.8304422.

S. Saponara, A. Elhanashi, and A. Gagliardi, “Real-time video fire/smoke detection based on CNN in antifire surveillance systems,†J Real Time Image Process, vol. 18, no. 3, pp. 889–900, 2021, doi: 10.1007/s11554-020-01044-0.

F. Abdulhafidh Dael, U. Yavuz, and A. A. Almohammedi, “Performance Evaluation of Time Series Forecasting Methods in The Stock Market: A Comparative Study,†2022 International Conference on Decision Aid Sciences and Applications, DASA 2022, pp. 1510–1514, 2022, doi: 10.1109/DASA54658.2022.9765177.

A. Özdemir, U. Yavuz, A. Fares, and F. Dael, “Performance evaluation of different classification techniques using different datasets,†International Journal of Electrical and Computer Engineering, vol. 9, pp. 3584–3590, 2019, doi: 10.11591/ijece.v9i5.pp3584-3590.

W. S. Noble, “What is a support vector machine?,†Nat Biotechnol, vol. 24, no. 12, pp. 1565–1567, 2006, doi: 10.1038/nbt1206-1565.

S. Chen, J. Ren, Y. Yan, M. Sun, F. Hu, and H. Zhao, “Multi-sourced sensing and support vector machine classification for effective detection of fire hazard in early stage,†Computers and Electrical Engineering, vol. 101, p. 108046, 2022, doi: 10.1016/j.compeleceng.2022.108046.

C. Bogdal, R. Schellenberg, O. Höpli, M. Bovens, and M. Lory, “Recognition of gasoline in fire debris using machine learning: Part I, application of random forest, gradient boosting, support vector machine, and naïve bayes,†Forensic Sci Int, vol. 331, p. 111146, Feb. 2022, doi: 10.1016/J.FORSCIINT.2021.111146.

Y. SONG and Y. LU, “Decision tree methods: applications for classification and prediction,†Shanghai Arch Psychiatry, vol. 27, no. 2, pp. 130–135, 2015, doi: 10.11919/j.issn.1002-0829.215044.

A. Kurani, P. Doshi, A. Vakharia, and M. Shah, “A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting,†Annals of Data Science, vol. 10, no. 1, pp. 183–208, 2023, doi: 10.1007/s40745-021-00344-x.

F. Abdulhafidh Dael, Ö. Ça, grı Yavuz, and gur Yavuz, “Stock Market Prediction Using Generative Adversarial Networks (GANs): Hybrid Intelligent Model,†Computer Systems Science and Engineering, vol. 47, no. 1, pp. 19–35, Jun. 2023, doi: 10.32604/CSSE.2023.037903.

A. Billa, I. Shayea, A. Alhammadi, Q. Abdullah, and M. Roslee, “An Overview of Indoor Localization Technologies: Toward IoT Navigation Services,†in 2020 IEEE 5th International Symposium on Telecommunication Technologies (ISTT), 2020, pp. 76–81. doi: 10.1109/ISTT50966.2020.9279369.

E. Gures, I. Shayea, M. Ergen, M. H. Azmi, and A. A. El-Saleh, “Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey,†IEEE Access, vol. 10, pp. 37689–37717, 2022, doi: 10.1109/ACCESS.2022.3161511.


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