@article{ref1,
title="Machine learning framework for Hazard Extraction and Analysis of Trends (HEAT) in wildfire response",
journal="Safety science",
year="2023",
author="Andrade, Sequoia R. and Walsh, Hannah S.",
volume="167",
number="",
pages="e106252-e106252",
abstract="This research proposes a natural language processing enabled risk analysis framework, named Hazard Extraction and Analysis of Trends (HEAT), and applies the framework to the ICS-209-PLUS data set of wildfire incident response forms. The HEAT framework produces safety- and risk-relevant analyses, consisting of: (1) a set of hazards extracted from text data, (2) a primary analysis using hazard-relevant metrics, such as rate and severity, to form an FMEA-style table and risk matrix, (3) a time series analysis of metric trends, and (4) a secondary analysis examining potential predictors for hazards. <br><br>RESULTS from HEAT provide quantitative risk-relevant information for high-level hazards documented in existing-state operations. Because of the generalizability of the steps and limited data requirements, HEAT can be applied to any dataset containing narrative text, thus providing a framework for data-driven machine learning-enabled quantitative risk analysis across a variety of domains. To demonstrate HEAT in a case study, we apply the framework to the ICS-209-PLUS dataset of wildland fire incident response forms. Hazards identified in wildfire response arise from environmental conditions, the mission, and the wildland urban interface. The resulting risk matrix identifies evacuations as high-risk hazards, while all other identified hazards are medium or serious risk.<p /> <p>Language: en</p>",
language="en",
issn="0925-7535",
doi="10.1016/j.ssci.2023.106252",
url="http://dx.doi.org/10.1016/j.ssci.2023.106252"
}