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Big Data and Predictive Analytics in Fire Risk Using Weather Data

Author

Listed:
  • Puneet Agarwal
  • Junlin Tang
  • Adithya Narayanan Lakshmi Narayanan
  • Jun Zhuang

Abstract

The objective of this article is to study the impact of weather on the damage caused by fire incidents across the United States. The article uses two sets of big data—‐fire incidents data from the National Fire Incident Reporting System (NFIRS) and weather data from the National Oceanic and Atmospheric Administration (NOAA)—to obtain a single comprehensive data set for prediction and analysis of fire risk. In the article, the loss is referred to as “Total Percent Loss,” a metric that is calculated based on the content and property loss incurred by an owner over the total value of content and property. Gradient boosting tree (GBT), a machine learning algorithm, is implemented on the processed data to predict the losses due to fire incidents. An R2 value of 0.933 and mean squared error (MSE) of 124.641 out of 10,000 signify the extent of high predictive accuracy obtained by implementing the GBT model. In addition to this, an excellent predictive performance demonstrated by the GBT model is further validated by a strong fitting between the predicted loss and the actual loss for the test data set, with an R2 value of 0.97. While analyzing the influence of each input variable on the output, it is observed that the state in which a fire incident takes place plays a major role in determining fire risk. This article provides useful insights to fire managers and researchers in the form of a detailed framework of big data and predictive analytics for effective management of fire risk.

Suggested Citation

  • Puneet Agarwal & Junlin Tang & Adithya Narayanan Lakshmi Narayanan & Jun Zhuang, 2020. "Big Data and Predictive Analytics in Fire Risk Using Weather Data," Risk Analysis, John Wiley & Sons, vol. 40(7), pages 1438-1449, July.
    • Handle: RePEc:wly:riskan:v:40:y:2020:i:7:p:1438-1449
    DOI: 10.1111/risa.13480
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    References listed on IDEAS

    as
    1. Adam Behrendt & Vineet M. Payyappalli & Jun Zhuang, 2019. "Modeling the Cost Effectiveness of Fire Protection Resource Allocation in the United States: Models and a 1980–2014 Case Study," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1358-1381, June.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Citations

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    Cited by:

    1. Francesco Granata & Fabio Di Nunno, 2026. "Advancing fire weather forecasting: deep learning with Kolmogorov–Arnold and Fourier networks across Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 122(2), pages 1-23, January.
    2. Deshun Zhang & Manqing Yao & Yingying Chen & Yujia Liu, 2025. "The Role of Urban Vegetation in Mitigating Fire Risk Under Climate Change: A Review," Sustainability, MDPI, vol. 17(6), pages 1-25, March.
    3. Wang, Ning & Zhao, Shiyue & Wang, Sutong, 2024. "A novel clustering-based resampling with cost-sensitive boosting method to model and map wildfire susceptibility," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Esther Jose & Puneet Agarwal & Jun Zhuang, 2023. "A data-driven analysis and optimization of the impact of prescribed fire programs on wildfire risk in different regions of the USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 181-207, August.
    5. Wang, Ning & Xu, Yan & Wang, Sutong, 2022. "Interpretable boosting tree ensemble method for multisource building fire loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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