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.
Highlights
Natural hazard-triggered technological accidents are known as Natechs (Showalter and Myers 1992; Cruz et al 2006; Cruz and Okada 2008)
According to the results of the keyword extraction method, the total number of Natechs reported to the National Response Center (NRC) from 1990 to 2017 is 32,348, which represents 3.93% of all incidents reported to the NRC during that period and corresponds to 2.22% to 7.39% of the total incidents in each year
This study developed a Semi-Intelligent Natech Identification Framework (SINIF) to retrieve Natech related reports and identify the associated triggering natural hazards from the NRC database
Summary
Natural hazard-triggered technological accidents are known as Natechs (Showalter and Myers 1992; Cruz et al 2006; Cruz and Okada 2008). We are interested in the Natechs that involve facility/equipment damage with the subsequent release of hazardous materials (hazmat). Natechs can cause huge economic losses (Girgin and Krausmann 2016; Krausmann and Salzano 2017) as well as long-term effects on human health and the environment (Krausmann and Cruz 2013). Natechs are generally considered as hazmat release accidents that occur from the impact of a natural hazard on vulnerable industrial infrastructures (for example, storage tanks, fixed facilities, oil drill platforms). Natechs are more complex, and can have more severe consequences than the triggering natural hazard alone. Natechs bring a huge challenge to risk managers
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