Causative factors and risk prediction model of hydrogen leakage accidents: Machine learning based on case evidence

Abstract

This study innovatively derives a risk prediction model for hydrogen leakage accidents based on existing case evidence by combining MORT (Management Oversight and Risk Tree) with machine learning algorithms. 30 hydrogen leakage accidents causative factors are derived by analyzing 23 accident cases through MORT, and constructed a dataset of hydrogen leakage accident causative factors through data quantification. Using feature selection to optimize the risk indicators, 14 salient feature indicators are selected from the 30 full feature indicators. Two cross-validation strategies are introduced and combined with six machine learning models to establish 24 working conditions for numerical comparison experiments. The results show that the support vector machine model based on stratified K-fold cross-validation strategy has the best prediction performance, with the model Accuracy of 0.939, Precision of 0.848, Recall of 0.848, F1-score of 0.848, and AUC score of 0.98. This will effectively solve the problem of hydrogen leakage risk prediction and accident prevention in industrial systems.