Feature-based evidential reasoning for probabilistic risk analysis and prediction

Abstract

Risk analysis plays an important role in quality control in engineering projects for the consideration of time, cost, safety, and the environment. This study proposes a feature-based evidential reasoning approach for probabilistic risk analysis and prediction, incorporating the learning process of belief degrees and estimation of the judgment quality. Firstly, classifiers are trained to estimate the probabilistic risk from sub-groups of factors. Secondly, the judgment from each classifier is evaluated according to the classifier's performance which is characterized by the importance weight and reliability. Finally, the judgments from classifiers are fused via evidential reasoning to give the overall probabilistic risk classification result. The proposed approach displays superior performance on the dataset from Wuhan Metro with a 16% increase in precision, a 6% increase in recall, and an 8% increase in F1-score, compared to the direct model without information fusion. The fused model achieves a classification accuracy of 0.86 on the testing samples, which is better than the direct model. Besides, the model shows good error tolerance for wrongly classified results from classifiers without information fusion. The model has an acceptable performance even when the dataset is challenging to conduct classification tasks due to high overlapping areas in the attribute space.