Random Forest and Whale Optimization Algorithm to Predict the Invalidation Risk of Backfilling Pipeline

Abstract

The problem of backfilling pipeline invalidation has become a bottleneck restricting the application and development of backfilling technology. This study applied the whale optimization algorithm and random forest (WOA–RF) to predict the invalidation risk of backfilling pipelines based on 59 datasets from actual mines. Eight influencing factors of backfilling pipeline invalidation risk were chosen as the input parameters of the WOA–RF model, and the risk level was selected as the output parameters of the WOA–RF model. Furthermore, random forest, decision tree, artificial neural network, k-nearest neighbor, and support vector machine models were also established according to the collected datasets. The prediction performance of the six classification models was compared. The evaluated results showed that the established WOA–RF hybrid model has the best prediction performance and the highest accuracy (0.917) compared to other models, with the highest kappa value (0.8846) and MCC value (0.8932). In addition, the performed sensitivity analysis showed that the deviation rate is the most important influencing factor, followed by the internal diameter of the pipeline. Eventually, the WOA–RF hybrid model was used to predict the failure risk level of the backfilling pipelines of nine actual mines in Sichuan, China. The field datasets were collected through field investigation, and engineering verification was carried out. The research results show that the WOA–RF hybrid model is reasonable and effective for backfilling pipeline invalidation risk, and it can provide a novel solution for backfilling pipeline invalidation, with good engineering practicability.