## 4 Results and Discussion
### Sensitivity Analysis of Input Variables  
Sensitivity analyses were used to assess the impact of different incident factors on the probability of failure in a leaking vinyl chloride storage tank incident. The extent to which different factors influence system risk is revealed by quantifying how minor changes in input variables can lead to significant changes in output results. In order to perform this analysis, a method based on changes in model input variables is proposed. Using Equation (11), the corresponding degree of change in the output values with minor changes in the model input values is calculated. As shown in Figure 10, the results of the sensitivity analysis clearly demonstrate the extent to which each factor influences the risk of storage tank leakage. \[CRIT_{i}=\frac{\Delta P(TOP)}{\Delta P(i)}*\frac{P(i)}{P(TOP)} \tag{11}\]  
where \(CRIT_{i}\) is the critical importance of input variable \(i\), i.e., the extent to which input variable \(i\) affects the final output, \(P(TOP)\) is the probability of system failure, and \(P(i)\) is the probability of component \(i\) failure. The results of the sensitivity analysis highlighted the key factors affecting the risk of leakage from vinyl chloride storage tanks, with X4, X8, X13, X14, X15, and X16 identified as having a significant impact on the risk. These findings are essential for the development of targeted safety management measures. In particular, enhanced inspection and maintenance of gas storage tanks, management and maintenance of gas trans  
Figure 10: Sensitivity analysis of BEs. consideration of natural conditions were shown to be effective strategies for improving the safety performance of storage tanks. It is noteworthy that these results are consistent with the risk factors identified in the accident investigation report, thus providing empirical support for the model proposed in this paper and further confirming the validity of the adopted methodology. The sensitivity analysis not only reveals the specific impact of each factor on the risk of storage tank leakage but also provides a scientific basis for risk management and decision-making. In addition, the study performed SHAP analysis on the model's inputs to further demonstrate its interpretability. The results are shown in Figure 11, where the \(X\)-axis represents the average SHAP values corresponding to each basic event, and the \(Y\)-axis lists the different basic event numbers. This bar chart illustrates the contribution of each basic event to the model's prediction results. From the chart, it can be observed that basic events 13, 16, 14, and 15 have relatively high SHAP values, indicating that these basic events significantly influence the model's predictions. These basic events may play a crucial role in the occurrence of accidents, and therefore, higher weights were assigned to these features during the model training process. This is consistent with the results of the sensitivity analysis, which shows that these features have a considerable impact on the model's output, further validating their importance in the prediction process. Based on the results of the SHAP analysis, it is now clearer how the model makes predictions, especially how it relies on these key features. This interpretability enhances our trust in the model's behavior and provides valuable insights for future optimization and adjustments. For example, features with greater contributions could be further optimized in terms of data preprocessing and feature selection, which would improve the model's prediction accuracy. Analysis of the Time-Efficient Development of the Failure Probability in Hazardous Chemical Storage Tank Leakage Accidents  
In order to effectively assess the dynamic evolution of the failure probability of hazardous chemical storage tank leakage accidents, an LSTM network model of storage tank DFT is constructed to further model and predict the DFT.