Analysis and intelligent prediction of domino effect accidents in chemical storage tanks with a focus on accident chain length

The compact arrangement of chemical storage tanks significantly increases the occurrence probability of domino effect accidents. The accident chain length, a critical parameter for assessing accident severity, enables rapid comprehension of potential accident impacts and serves as a foundation for constructing accident scenarios in domino effect risk assessment. This study centers on domino effect accidents within chemical storage tanks and conducting a detailed analysis of factors influencing the accident chain length. Given the limitations in historical statistical data and quantitative risk evaluations, an intelligent prediction method is developed to forecast the accident chain length. A fully connected feedforward neural network (FC-FNN) is utilized to analyze 255 pertinent accident cases spanning from 1970 to 2024, with key features such as the type of substances implicated and the operating conditions during accidents being judiciously screened. To compensate for the insufficiency of data regarding the volume of storage tanks, a small-scale augmentation is implemented within the tolerable error range. Additionally, Shapley Additive Explanations (SHAP) is applied to optimize the feature set, reducing the number of features from 15 to 10 based on their contribution to the model's predictions. The results show that the combined application of feature selection, data augmentation, and SHAP-based optimization significantly improves the model's prediction performance. The test set prediction accuracy exceeds 0.978, demonstrating the effectiveness of the proposed approach.