## 5 Conclusions  
In this study, a modeling approach based on Fuzzy DFT with neural networks is proposed with the aim of improving the accuracy and efficiency of the probability analysis of hazardous chemical storage tank probabilities. The following are the main conclusions of the article. 1. Combining FST with Bootstrap technology, the data-driven approach effectively solves the problem of quantifying the failure probability of basic events, reduces the dependence on the subjective judgment of experts, and improves the accuracy and reliability of failure probability assessment. 2. The model was applied to the case of the vinyl chloride leakage accident that occurred at the Shenghua Chemical Company on 11.28. The research results show that the mean square error of the constructed ANN model is only \(1.23\times 10^{-6}\), indicating that the accident probability calculation method proposed in this study can effectively solve the problem of calculating the probability of hazardous chemical storage tank leakage accidents. According to the sensitivity analysis, the main risk factors calculated by the model are consistent with the accident report. In addition, the model proposed in the research can take into account the coupling relationship between basic events. The average error of the failure probability of the top-level event predicted by the model is 2.14%, representing a 38% performance improvement compared with the traditional DFT method. This research not only addresses the issue of not considering the dependency relationship between basic events in the calculation of the probability of hazardous chemical storage tank leakage accidents but also provides a fast and effective technique for the quantitative calculation of the probability of such accidents. 3. This study combines the nature of the Markov Chain to obtain the dataset and constructs the correspondence accordingly. The LSTM model based on the DFT of hazardous chemical storage tank leakage accidents is constructed, which implements the calculation of the time-series variation of the failure probability of the basic event and analyzes the dynamic evolution trend of the occurrence probability of storage tank leakage accidents over time. The results of the study demonstrate that the LSTM model performs well in handling time-series-related model predictions, and the loss value from the true value is reduced to \(5.0337\times 10^{-5}\), which is \(1.64393\times 10^{-4}\) compared with the traditional RNN method, thus validating the effectiveness of the present method. This study provides a new analytical tool for hazardous chemical tank safety management, making risk prediction more dynamic and forward-looking. Figure 13: RNN and LSTM predict the loss of the DFT model. It is worth emphasizing that the method proposed in this study has broad applicability and is not limited to the field of hazardous chemical storage tank risk analysis. It can also be applied in other related fields, such as risk data calculation in the chemical industry and failure probability calculation in the mechanical field. Specifically, it only requires replacing the dataset needed for the model with one that is tailored to the characteristics of different fields and adjusting the model parameters appropriately based on the actual situation. In this way, the method can operate effectively. Despite the positive results of this study, some challenges and limitations remain. Future research needs to explore the effectiveness and robustness of the present method in a wider range of scenarios and further optimize the parameter settings and structural design of the model to fully exploit the performance advantages of the ANN model. X.L. and N.Z. : supervisor, reviewing and editing. W.L. : writing--original draft preparation. X.Y. : validation. All authors have read and agreed to the published version of the manuscript. This study was funded by the National Key R&D Plan \"Internet of Things and Smart City Key Technologies and Demonstration\" Key Special Project (No.