Skip to main content

A Fire Source Localization Algorithm Based on Temperature and Smoke Sensor Data Fusion


Traditional video surveillance, temperature-based or smoke-based fire source location methods are difficult to timely and accurately locate the fire source in warehouses with the characteristics of burning intensely, smoke spreading quickly, and being sheltered by shelves and goods. To overcome the drawbacks, a deep-learning-based fire source localization algorithm with temperature and smoke sensor data fusion according to the different stages of the combustion process is proposed in this paper. The temperature and smoke concentration information are collected from sensors distributed in different spatial locations of a warehouse. A convolutional neural network is used to exact the fusion data feature. The deep learning algorithm is adopted to construct the fire source localization model where the fusion data feature of temperature and smoke concentrations are the inputs and the fire source coordinates are the outputs. By using Fire Dynamics Simulator, a warehouse that meets the practical application is constructed and kinds of fire scenes are simulated. The experimental results show that the RMSE of the model localization reaches 0.63, 0.08, and 0.17 in three stages respectively, which verifies the effectiveness of the proposed fire source localization algorithm.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20


  1. Benichou N, Kashef AH, Reid I, Hadjisophocleous GV, Torvi DA, Morinville G (2005) FIERAsystem: a fire risk assessment tool to evaluate fire safety in industrial buildings and large spaces. J Fire Prot Eng 15(3):145–172.

    Article  Google Scholar 

  2. Ding L, Khan F, Ji J (2020) Risk-based safety measure allocation to prevent and mitigate storage fire hazards. Process Saf Environ Prot 135:282–293.

    Article  Google Scholar 

  3. Ahrens M (2016) High-rise building fires. Quincy, NFPA (National Fire Protection Association)

    Google Scholar 

  4. Yan B, Li J, Zhang M, Zhang J, Qiao L, Wang T (2019) Raman distributed temperature sensor with optical dynamic difference compensation and visual localization technology for tunnel fire detection. Sensors 19(10):2320.

    Article  Google Scholar 

  5. Li P, Zhao W (2020) Image fire detection algorithms based on convolutional neural networks. Case Stud Thermal Eng 19:100625.

    Article  Google Scholar 

  6. Geetha S, Abhishek CS, Akshayanat CS (2021) Machine vision based fire detection techniques: a survey. Fire Technol 57:591–623.

    Article  Google Scholar 

  7. Huang X, Du L (2020) Fire detection and recognition optimization based on virtual reality video image. IEEE Access 8:77951–77961.

    Article  Google Scholar 

  8. Gaur A, Singh A, Kumar A et al (2020) Video flame and smoke based fire detection algorithms: a literature review. Fire Technol 56:1943–1980.

    Article  Google Scholar 

  9. Naser MZ, Lautenberger C, Kuligowski E (2021) Special Issue on “Smart Systems in Fire Engineering.” Fire Technol 57(6):2737–2740.

    Article  Google Scholar 

  10. Ryder NL, Geiman JA, Weckman EJ (2021) Hierarchical temporal memory continuous learning algorithms for fire state determination. Fire Technol.

    Article  Google Scholar 

  11. Abid F (2021) A survey of machine learning algorithms based forest fires prediction and detection systems. Fire Technol 57:559–590.

    Article  Google Scholar 

  12. Cetin AE, Dimitropoulos K, Gouverneur B et al (2013) Video fire detection—review. Digit Signal Process A Rev J 23:1827–1843.

    Article  Google Scholar 

  13. Chen SJ, Hovde DC, Peterson KA, Marshall AW (2007) Fire detection using smoke and gas sensors. Fire Saf J 42(8):507–515.

    Article  Google Scholar 

  14. Wang S, Berentsen M, Kaiser T (2005) Signal processing algorithms for fire localization using temperature sensor arrays. Fire Saf J 40(8):689–697.

    Article  Google Scholar 

  15. Xia D, Wang S, Zhu M, Tang H (2008) A method research on fire source localization using dual-line gas sensor array. In: 2008 7th world congress on intelligent control and automation, pp 5862–5865. IEEE.

  16. Kaiser T (2000) Fire detection with temperature sensor arrays. In: Proceedings IEEE 34th annual 2000 international carnahan conference on security technology (Cat. No. 00CH37083). IEEE, pp 262–268.

  17. Yang P, Tan X, Xin W (2011) Experimental study and numerical simulation for a storehouse fire accident. Build Environ 46(7):1445–1459.

    Article  Google Scholar 

  18. Gawad AFA, Ghulman HA (2015) Prediction of smoke propagation in a big multi-story building using fire dynamics simulator (FDS). Am J Energy Eng 3(4):23.

    Article  Google Scholar 

  19. Regulations for fire safety management in warehouses (Ministry of Public Security Order No. 6). Ministry of Public Security Network.1990–04–10

  20. Technical standard for smoke management systems in buildings GB51251–2017

  21. Wu X, Park Y, Li A, Huang X, Xiao F, Usmani A (2021) Smart detection of fire source in tunnel based on the numerical database and artificial intelligence. Fire Technol 57(2):657–682.

    Article  Google Scholar 

  22. Bahrami D, Zhou L, Yuan L (2021) Field verification of an improved mine fire location model. Min Metall Explor 38(1):559–566.

    Article  Google Scholar 

  23. Yao Y, Cheng X, Zhang S et al (2017) Maximum smoke temperature beneath the ceiling in an enclosed channel with different fire locations. Appl Therm Eng 111:30–38.

    Article  Google Scholar 

  24. Code for design of automatic fire alarm system GB 50116 – 2013

  25. Hu J, Zhang L, Wang R, Ma Q (2018) Fire accident inversion method base on STAMP and topological network for LNG depot. In: Pressure vessels and piping conference, vol 51708. American Society of Mechanical Engineers, p V007T07A032.

  26. Parish EJ, Duraisamy K (2016) A paradigm for data-driven predictive modeling using field inversion and machine learning. J Comput Phys 305:758–774.

    Article  MathSciNet  MATH  Google Scholar 

  27. Kim Y, Nakata N (2018) Geophysical inversion versus machine learning in inverse problems. Lead Edge 37(12):894–901.

    Article  Google Scholar 

  28. Holland JR, Baeder JD, Duraisamy K (2019) Towards integrated field inversion and machine learning with embedded neural networks for rans modeling. In: AIAA Scitech 2019 forum, p 1884.

  29. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298.

    Article  Google Scholar 

  30. Eren L, Ince T, Kiranyaz S (2019) A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. J Signal Process Syst 91(2):179–189.

    Article  Google Scholar 

  31. Muhammad K, Ahmad J, Lv Z, Bellavista P, Yang P, Baik SW (2018) Efficient deep CNN-based fire detection and localization in video surveillance applications. IEEE Trans Syst Man Cybern 49(7):1419–1434.

    Article  Google Scholar 

  32. Liu Z, Tang H, Lin Y, Han S (2019) Point-voxel cnn for efficient 3d deep learning. arXiv preprint

  33. Karlik B, Olgac AV (2011) Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int J Artif Intell Expert Syst 1(4):111–122

    Google Scholar 

  34. Ketkar N (2017) Introduction to keras. In: Deep learning with Python. Apress, Berkeley, pp 97–111.

  35. Mehta S, Paunwala C, Vaidya B (2019) CNN based traffic sign classification using adam optimizer. In: 2019 international conference on intelligent computing and control systems (ICCS). IEEE, pp 1293–1298.

  36. Dong J, Zhuang D, Huang Y, Fu J (2009) Advances in multi-sensor data fusion: algorithms and applications. Sensors 9(10):7771–7784.

    Article  Google Scholar 

  37. Prabhakar S, Jain AK (2002) Decision-level fusion in fingerprint verification. Pattern Recogn 35(4):861–874.

    Article  MATH  Google Scholar 

Download references


This research is supported by the National Natural Science Foundation of China (61873121, 52004131), the Natural Science Foundation of Jiangsu Province (BK20181376).

Author information

Authors and Affiliations



LL: Conceptualization, Methodology, Analysis, Writing—review & editing. JY: Methodology, Validation, Visualization, Analysis, Writing—original draft. CW: Investigation, Methodology, Validation, Writing—review & editing. CG: Conceptualization, Methodology, Visualization, Writing—original draft. YY: Resources, Supervision. QZ: Project administration.

Corresponding author

Correspondence to Yuan Yu.

Ethics declarations

Competing Interests

There are no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



The difference between the temperature data collected by the nearest detector of the fire source point. From Figure 21, the smaller the grid size, the higher the temperature of the sensor collection. The overall trend of temperature rise is consistent, but when the size of the grid is reduced by 0.1 m, the CFD fire model calculation time was more than doubled. Considering the calculation accuracy and time consumption, this article selects a grid size of 0.25 m.

Figure 21
figure 21

Comparison of the temperature of the same detector at a different grid size

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, L., Ye, J., Wang, C. et al. A Fire Source Localization Algorithm Based on Temperature and Smoke Sensor Data Fusion. Fire Technol 59, 663–690 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Warehouse fires
  • FDS
  • Fire source location
  • CNN