Research on the prediction model of hazardous chemical road transportation accidents |
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| Institution: | 1. Hunan University of Science and Technology, School of Resource, Environment and Safety Engineering, Xiangtan, Hunan, 411201, China;2. Central South University, School of Resources and Safety Engineering, Changsha, Hunan, 410083, China;1. School of Safety Science and Engineering, Xi''an University of Science and Technology, Xi''an Shaanxi 710054, China;2. Postdoctoral Research Mobile Station in Mining Engineering, Xi''an University of Science and Technology, Xi''an, Shaanxi 710054, China;3. Postdoctoral Program, Xi’ an University of Science and Technology, 58, Yanta Mid. Rd., Xi’ an, Shaanxi 710054, China;1. School of Emergency Management and Safety Engineering, China University of Mining and Technology - Beijing, D11 Xueyuan Road, Haidian District, Beijing, 100083, China;2. China Oil & Gas Pipeline Network Corporation, 5 Dongtucheng Road, Chaoyang District, Beijing, 100020, China;1. Physikalisch-Technische Bundesanstalt, Bundesallee 100, DE-38116, Braunschweig, Germany;2. Otto von Guericke University, Universitätsplatz 2, DE-39106, Magdeburg, Germany;1. Provincial Key Laboratory of Oil and Gas Storage and Transportation Safety in Shandong Province, China University of Petroleum (Huadong), Qingdao, Shandong, 266580, China;2. Shenzhen Gas Corporation Ltd, Shenzhen, Guangdong, 518034, China |
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| Abstract: | In recent years, hazardous chemicals road transport accidents have occurred frequently, causing huge casualties and property losses, and accident risk assessment has become the focus of researchers' research. To predict the risk probability value of hazardous chemical road transport accidents, first, we compiled data on road transportation accidents of hazardous chemicals in China in the past five years. And the nine nodes in the Bayesian network (BN) structure were defined in combination with relevant classification standards. The optimal Bayesian network structure for hazardous chemical road transport accidents was determined based on the K2 algorithm and the causalities between the nodes. Second, the node conditional probabilities were derived by parameter learning of the model using Netica, and the validity of the model was verified using the 5-fold cross-validation method. Last, the Bayesian network model of hazardous chemical road transport accidents is used to analyze accident examples, and the accident chain of “rear-end-leakage” is predicted, and the accident is most likely to be disposed of within 3–9 h. The study shows that the derived accident prediction model for hazardous chemical road transportation can reason reasonably about the evolution of accident scenarios and determine the probability values of accident risks under different parameter conditions. |
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| Keywords: | Hazardous chemicals Road transportation Bayesian network (BN) Probabilistic prediction Netica |
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