Extracting Natech Reports from Large Databases: Development of a Semi-Intelligent Natech Identification Framework

Xiaolong Luo · Ana Maria Cruz · Dimitrios Tzioutzios

41 citations0/0 files storedInternational Journal of Disaster Risk Science

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Risk and Safety Analysis Disaster Management and Resilience Text and Document Classification Technologies

Abstract

Abstract Natural hazard-triggered technological accidents (Natechs) refer to accidents involving releases of hazardous materials (hazmat) triggered by natural hazards. Huge economic losses, as well as human health and environmental problems are caused by Natechs. In this regard, learning from previous Natechs is critical for risk management. However, due to data scarcity and high uncertainty concerning such hazards, it becomes a serious challenge for risk managers to detect Natechs from large databases, such as the National Response Center (NRC) database. As the largest database of hazmat release incidents, the NRC database receives hazmat release reports from citizens in the United States. However, callers often have incomplete details about the incidents they are reporting. This results in many records having incomplete information. Consequently, it is quite difficult to identify and extract Natechs accurately and efficiently. In this study, we introduce machine learning theory into the Natech retrieving research, and a Semi-Intelligent Natech Identification Framework (SINIF) is proposed in order to solve the problem. We tested the suitability of two supervised machine learning algorithms, namely the Long Short-Term Memory (LSTM) and the Convolutional Neural Network (CNN), and selected the former for the development of the SINIF. According to the results, the SINIF is efficient (a total number of 826,078 records were analyzed) and accurate (the accuracy is over 0.90), while 32,841 Natech reports between 1990 and 2017 were extracted from the NRC database. Furthermore, the majority of those Natech reports (97.85%) were related to meteorological phenomena, with hurricanes (24.41%), heavy rains (19.27%), and storms (18.29%) as the main causes of these reported Natechs. Overall, this study suggests that risk managers can benefit immensely from SINIF in analyzing Natech data from large databases efficiently.

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References

2013Impact of the 11 March 2011, Great East Japan earthquake and tsunami on the chemical industry 2009Industrial accidents triggered by flood events: Analysis of past accidents 2011Industrial accidents triggered by earthquakes, floods and lightning: lessons learned from a database analysis 2006A methodology for the quantitative risk assessment of major accidents triggered by seismic events 2009Development of a framework for the risk assessment of Na-Tech accidental events 2016Historical analysis of U.S. onshore hazardous liquid pipeline accidents triggered by natural hazards 2015Quantitative assessment of risk due to NaTech scenarios caused by floods 2008Hazardous-materials releases from offshore oil and gas facilities and emergency response following Hurricanes Katrina and Rita

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2023Natech accidents triggered by cold waves 2023Vulnerability Assessment of Industrial Sites to Interface Fires and Wildfires 2021Climate change and temporal-spatial variation of tropical storm-related Natechs in the United States from 1990 to 2017: Is there a link?2023Natech Accidents Triggered by Heat Waves

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