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.

由自然灾害触发的技术事故（Natechs）是指涉及由自然灾害引发的有害物质（危险品）释放的事故。Natechs造成了巨大的经济损失以及人类健康和环境问题。在这方面，向以前的Natechs学习对风险管理至关重要。但是，由于数据稀缺和有关此类危害的高度不确定性，对于风险管理人员来说，从大型数据库（例如国家响应中心（NRC）数据库）中检测Natech成为一项严峻的挑战。作为最大的危险品释放事件数据库，NRC数据库接收来自美国公民的危险品释放报告。但是，呼叫者通常对其所报告事件的信息不完整。这导致许多记录的信息不完整。所以，准确有效地识别和提取Natechs相当困难。在这项研究中，我们将机器学习理论引入了Natech检索研究，并提出了一种半智能Natech识别框架（SINIF）以解决该问题。我们测试了两种受监督的机器学习算法（长短期记忆（LSTM）和卷积神经网络（CNN））的适用性，并选择了前者来开发SINIF。根据结果​​，SINIF是高效的（已分析总数826,078条记录）和准确的（准确性超过0.90），同时从NRC数据库中提取了1990年至2017年之间的32,841份Natech报告。此外，Natech的大多数报告（97.85％）与气象现象有关，其中飓风（24.41％），据报道，Natechs的主要原因是大雨（19.27％）和暴风雨（18.29％）。总体而言，这项研究表明，风险管理者可以从SINIF中受益匪浅，可以有效地分析大型数据库中的Natech数据。