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Fire Loss Assessment Model Based on Internet Search Engine Query Data

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Abstract

There is an interaction between public fire safety awareness and fire direct economic loss, while how to quantify public fire safety awareness is still a challenge. Driven by the rapid development of Internet networks and information technology, Internet search engine query data provides an opportunity for quantifying the level of public fire safety awareness. Based on the fire-related Baidu Search Volume (BSV) data, this paper investigates the relationship between public fire safety awareness and fire direct economic loss with an integration machine learning method. Firstly, the fire-related keywords selection framework is constructed based on the analysis of the fire safety demand generated at different responding stages of fire accidents. Then, the most important keywords associated with fire direct economic losses are identified by correlation analysis. Finally, four types of assessing models, including multiple regression model, principal component analysis model, vector auto-regression model, and artificial neural network model are developed to simulate the ability of fire-related keywords to assess fire direct economic losses. The results show that the fire-related keywords BSV data have a strong correlation with the fire direct economic loss. Meanwhile, BSV data presents a good applicability in evaluating the fire direct economic loss. The artificial neural network model presents the best assessment performance in this work. This research could contribute to quantifying the public awareness of fire safety, indirectly present the public fire safety behavior, and also provide a new solution for how to assess urban fire risk.

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References

  1. Shokouhi M, Nasiriani K, Cheraghi Z, Ardalan A, Khankeh H, Fallahzadeh H, Khorasani-Zavareh D (2019) Preventive measures for fire-related injuries and their risk factors in residential buildings: a systematic review. J Inj Violence Res 11(1):1–14

    Google Scholar 

  2. Jonsson A, Bonander C, Nilson F, Huss F (2017) The state of the residential fire fatality problem in Sweden: epidemiology, risk factors, and event typologies. J Safety Res 62:89–100

    Article  Google Scholar 

  3. Thompson OF, Wales D (2015) A qualitative study of experiences, actions and motivations during accidental dwelling fires. Fire Mater 39(4):453–465

    Article  Google Scholar 

  4. Nilson F, Bonander C, Jonsson A (2015) Differences in Determinants amongst individuals reporting residential fires in Sweden: results from a cross-sectional study. Fire Technol 51(3):615–626

    Article  Google Scholar 

  5. Xin J, Huang CF (2014) Fire risk assessment of residential buildings based on fire statistics from China. Fire Technol 50(5):1147–1161

    Article  Google Scholar 

  6. Sierra FJM, Rubio-Romero JC, Gámez MCR (2012) Status of facilities for fire safety in hotels. Saf Sci 50(7):1490–1494

    Article  Google Scholar 

  7. Chen YY, Chuang YJ, Huang CH, Lin CY, Chien SW (2012) The adoption of fire safety management for upgrading the fire safety level of existing hotel buildings. Build Environ 51:311–319

    Article  Google Scholar 

  8. Li SY, Tao G, Zhang LJ (2018) Fire risk assessment of high-rise buildings based on gray-FAHP mathematical model. Procedia Eng 211:395–402

    Article  Google Scholar 

  9. Granda S, Ferreira TM (2019) Assessing vulnerability and fire risk in old urban areas: application to the historical centre of Guimarães. Fire Technol 55(1):105–127

    Article  Google Scholar 

  10. Richardson LR (2007) Fire losses in selected property classifications of non-residential, commercial and residential wood buildings.Part 1: hotels/motels and care homes for aged. Fire Mater 31(2):97–123

    Article  Google Scholar 

  11. Hou G, Li Q, Song Z, Zhang H (2021) Optimal fire station locations for historic wood building areas considering individual fire spread patterns and different fire risks. Case Stud Therm Eng 28:101548

    Article  Google Scholar 

  12. Butry DT, Thomas DS (2017) Cigarette fires involving upholstered furniture in residences: the role that smokers, smoker behavior, and fire standard compliant cigarettes play. Fire Technol 53(3):1123–1146

    Article  Google Scholar 

  13. Hu J, Shu XM, Xie ST, Tang SY, Wu JJ, Deng BY (2019) Socioeconomic determinants of urban fire risk: a city-wide analysis of 283 Chinese cities from 2013 to 2016. Fire Saf J 110:e102890

    Article  Google Scholar 

  14. Jennings CR (1999) Socioeconomic characteristics and their relationship to fire incidence: a review of the literature. Fire Technol 35(1):7–34

    Article  Google Scholar 

  15. Manes M, Rush D (2018) A critical evaluation of BS PD 7974–7 structural fire response data based on USA fire statistics. Fire Technol 55(4):1243–1293

    Article  Google Scholar 

  16. Amatulli G, Rodrigues MJ, Trombetti M, Lovreglio R (2006) Assessing long-term fire risk at local scale by means of decision tree technique. J Geophys Res Biogeosci. https://doi.org/10.1029/2005JG000133

    Article  Google Scholar 

  17. (2021) 2022–2028 China Fire Industry Analysis and Market Prospect Forecast Report,

  18. Ferreira TM (2019) Notre dame cathedral: another case in a growing list of heritage landmarks destroyed by fire. Fire 2(2):20

    Article  Google Scholar 

  19. Ahrens M, Messerschmidt B (2021) Fire safety in the United States since 1980. National Fire Protection Association (NFPA): Quincy, USA

    Google Scholar 

  20. Lehna C, Coty MB, Fahey E, Williams J, Scrivener D, Wishnia G, Myers J (2015) Intervention study for changes in home fire safety knowledge in urban older adults. Burns 41(6):1205–1211

    Article  Google Scholar 

  21. Regulations on Fire Safety Management in Colleges and Universities. In. Regulations on Fire Safety Management in Colleges and Universities. 2009.

  22. Zhang K, Suo J, Chen J, Liu X, Gao L. (2017) Design and Implementation of Fire Safety Education System on Campus based on Virtual Reality Technology. Conference Ttile. 1297–1300.

  23. Ardianto R, Chhetri P (2018) Modeling spatial-temporal dynamics of urban residential fire risk using a markov chain technique. Int J Disaster Risk Sci 10(1):57–73

    Article  Google Scholar 

  24. Thompson OF, Galea ER, Hulse LM (2018) A review of the literature on human behaviour in dwelling fires. Saf Sci 109:303–312

    Article  Google Scholar 

  25. Holborn P, Nolan P, Golt J (2003) An analysis of fatal unintentional dwelling fires investigated by London Fire Brigade between 1996 and 2000. Fire Saf J 38(1):1–42

    Article  Google Scholar 

  26. Xiong L, Bruck D, Ball M (2015) Comparative investigation of ‘survival’and fatality factors in accidental residential fires. Fire Saf J 73:37–47

    Article  Google Scholar 

  27. Jennings CR. (1999) The promise and pitfalls of fire service deployment analysis methods. Proceedings of the First International Conference on Fire Service Deployment Analysis, Indianapolis, IN. Alexandria, Va.: Institution of Fire Engineers (US Branch).

  28. Odland J, Balzer B (1979) Localized externalities, contagious processes and the deterioration of urban housing: an empirical analysis. Socioecon Plann Sci 13(2):87–93

    Article  Google Scholar 

  29. Cloninger DO (1990) Arson and abandonment: a restatement. J Risk Insur. https://doi.org/10.2307/252848

    Article  Google Scholar 

  30. Wallace R (1981) Fire service productivity and the New York City fire crisis: 1968–1979. Hum Ecol 9(4):433–464

    Article  Google Scholar 

  31. Wallace D, Wallace R (1998) A plague on your houses: How New York was burned down and national public health crumbled. Verso

    Google Scholar 

  32. Shai D (2006) Income, housing, and fire injuries: a census tract analysis. Public Health Rep 121(2):149–154

    Article  Google Scholar 

  33. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2009) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012–1014

    Article  Google Scholar 

  34. Ettredge M, Gerdes J, Karuga G (2005) Using web-based search data to predict macroeconomic statistics. Commun ACM 48(11):87–92

    Article  Google Scholar 

  35. Askitas N, Zimmermann KF (2009) Google econometrics and unemployment forecasting. Appl Econ Q 55(2):107–120

    Article  Google Scholar 

  36. Wu L, Brynjolfsson E (2015) The future of prediction: How Google searches foreshadow housing prices and sales. In. The future of prediction: How Google searches foreshadow housing prices and sales. University of Chicago Press, pp. 89–118.

  37. Preis T, Reith D, Stanley HE (2010) Complex dynamics of our economic life on different scales: insights from search engine query data. Philos Trans R Soc A: Math, Phys Eng Sci 368(1933):5707–5719

    Article  MATH  Google Scholar 

  38. Zhu J, Wang X, Qin J, Wu L. (2012) Assessing public opinion trends based on user search queries: validity, reliability, and practicality. The Annual Conf. of the World Association for Public Opinion Research. 1–7.

  39. Preis T, Moat HS, Stanley HE (2013) Quantifying trading behavior in financial markets using Google Trends. Sci Rep 3(1):1–6

    Article  Google Scholar 

  40. Song TM, Song J, An JY, Hayman LL, Woo JM (2014) Psychological and social factors affecting Internet searches on suicide in Korea: a big data analysis of Google search trends. Yonsei Med J 55(1):254–263

    Article  Google Scholar 

  41. Li X, Ma J, Wang S, Zhang X (2015) How does Google search affect trader positions and crude oil prices? Econ Model 49:162–171

    Article  Google Scholar 

  42. Hu HP, Tang L, Zhang SH, Wang HY (2018) Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing 285:188–195

    Article  Google Scholar 

  43. Chen SX, Liu XJ, Wu YS, Xu GX, Zhang XB, Mei SJ, Zhang Z, O’Meara M, O’Gara MC, Tan XR (2019) The application of meteorological data and search index data in improving the prediction of HFMD: a study of two cities in Guangdong Province, China. Sci Total Environ 652:1013–1021

    Article  Google Scholar 

  44. Wang SF, Gong MG, Li HL, Yang JW (2016) Multi-objective optimization for long tail recommendation. Knowl-Based Syst 104:145–155

    Article  Google Scholar 

  45. Kullback S (1997) Information theory and statistics, Courier Corporation.

  46. Vergara JR, Estévez PA (2014) A review of feature selection methods based on mutual information. Neural Comput Appl 24:175–186

    Article  Google Scholar 

  47. Rajarethinam J, Aik J, Tian J (2020) The influence of South East asia forest fires on ambient particulate matter concentrations in Singapore: an ecological study using random forest and vector autoregressive models. Int J Environ Res Public Health 17(24):9345

    Article  Google Scholar 

  48. Shrestha N (2021) Factor analysis as a tool for survey analysis. Am J Appl Math Stat 9(1):4–11

    Article  MathSciNet  Google Scholar 

  49. Ahrens M (2009) Smoke alarms in US home fires, National Fire Protection Association, Fire Analysis and Research Division, September 2009.

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Acknowledgements

The research was supported by the Project of Chengdu Fire and Rescue Bureau (No.LR01HX1135Y16082), the National Natural Science Foundation of China (No. 72204136) and the China Scholarship Council.

Funding

National Natural Science Foundation of China, 72204136, Jianyu Wang, Project of Chengdu Fire and Rescue Bureau, LR01HX1135Y16082, Junmin Chen.

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Authors and Affiliations

  1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China

    Yulu Hao, Chang Liu, Linyao Li, Jianyu Wang, Yanqiu Chen & Junmin Chen

  2. Institute of Guangzhou Urban Planning Survey and Design, Guangzhou, 510053, Guangdong, China

    Chang Liu

  3. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing, 100084, China

    Jianyu Wang

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  1. Yulu Hao
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Correspondence to Junmin Chen.

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Hao, Y., Liu, C., Li, L. et al. Fire Loss Assessment Model Based on Internet Search Engine Query Data. Fire Technol (2023). https://doi.org/10.1007/s10694-023-01509-1

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