Big Data and Predictive Analytics in Fire Risk Using Weather Data

Puneet Agarwal, Junlin Tang, Adithya Narayanan Lakshmi Narayanan and Jun Zhuang ()

Risk Analysis, 2020, vol. 40, issue 7, 1438-1449

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.

Date: 2020
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https://doi.org/10.1111/risa.13480

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Persistent link: https://EconPapers.repec.org/RePEc:wly:riskan:v:40:y:2020:i:7:p:1438-1449

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