###### Abstract  
Aiming at the problems of complex calculation processes, insufficient risk data, and reliance on experts' subjective judgments that exist in traditional probability analysis methods, this paper proposes a probability analysis method for hazardous chemical storage tank leakage accidents based on neural networks and fuzzy dynamic fault trees (Fuzzy DFT). This method combines fuzzy set theory (FST) and Bootstrap technology to accurately quantify the failure probabilities of basic events (BEs) and reduce the dependence on experts' subjective judgments. Furthermore, an artificial neural network (ANN) model for tank failures is constructed. This model can accurately calculate the probability of tank leakage accidents by taking into account the dependency relationships among basic events. Finally, a long short-term memory (LSTM) network is utilized to analyze the dynamic evolution trend of the probability of storage tank accidents over time. In this paper, this method is applied to the case of the \"11.28\" Shenghua vinyl chloride leakage accident. The results show that the calculation results of this method are highly consistent with the actual situation of the accident, indicating that it is a scientific and effective method for analyzing the probability of hazardous chemical storage tank leakage accidents. tank leakage accident; dangerous chemicals; probabilistic calculation; neural network; fuzzy set theory 2020  
demonstrated the serious consequences and property losses of such accidents. Therefore, conducting effective quantitative analyses of dynamic accident probabilities is crucial for the safe management of storage tanks [5]. Fault tree analysis (FTA) is a deductive analysis method widely used in probabilistic analysis to qualitatively identify the critical and root causes of unexpected events in system failures and quantitatively assess the likelihood of accidents [6]. In the traditional FTA approach, the failure probabilities of basic events are exact values. However, because of insufficient data, performing an exact calculation of the BEs' failure probabilities in quantitative accident probability analyses is difficult to achieve [7; 8]. In the absence of a precise value for the probability of failure, it is common to approximate the probability and justify using 'likelihood' rather than 'probability' [9]. In order to solve this limitation, scholars combine FTA with other technologies. For example, [PERSON] and [PERSON] [10] combined FST with expert judgment to evaluate the failure probability of BEs in a robotic drilling system. [PERSON] and [PERSON] [7] proposed a fuzzy fault tree analysis method based on failure possibility. They combined fuzzy set theory with FTA to better deal with the inaccuracy in fault probability assessment. In view of the dynamic nature of the causes of hazardous chemical tank leakage accidents with temporal correlation, it is difficult to provide reliable results using traditional static probabilistic analysis methods. To cope with the time-series correlation in accident risk analysis, dynamic probabilistic analysis methods have been widely used in risk assessment [11; 12; 13]. In 2002, [PERSON] and [PERSON] [14] proposed the dynamic fault tree (DFT) method, which is a modified classical FT that takes into account the time dependence of system failure. To address the dynamics of accident probability, [PERSON] and [PERSON] [15] proposed the Bayesian network (BN) method. The method is a probabilistic inference technique for uncertainty reasoning and has been shown to be a practical approach for modeling and probability calculating of dynamic risk with uncertainty [16]. In view of the lack of risk data, the traditional expert evaluation method relies too much on the subjective evaluation of experts, which has great uncertainty. BN-based models assume that the state of each node (variable) is independent of each other [17]. Determining conditional probability tables may face difficulties when BN is applied to variables with unknown or nonlinear dependencies.