Evaluation of Goaf Stability Based on Transfer Learning Theory of Artificial Intelligence

Current artificial intelligence models for evaluating goaf stability in underground metal mines need a large amount of sample data for training, and their accuracy declines with small sample size. With the aim of solving this problem, this paper proposes an improved TrAdaBoost algorithm based on transfer learning theory. The scope of the TrAdaBoost algorithm is extended from the two-level classification to multi-level classification problems, which makes it suitable for evaluating goaf stability. The isolated forest method is used to filter the bad points of the auxiliary training set, thereby eliminating the interference of abnormal data. The dynamic factor concept is introduced to solve the problem that the weight of source domain data decreases too quickly and irreversibly, and this enhances the generalization performance of the algorithm for different goaf samples. To test the accuracy of the proposed model in predicting goaf stability, an evaluation model is constructed and the performance compared with other algorithms in current use. The prediction accuracy and generalization ability of the model are evaluated by mean square error and F1 measurements, which prove that the performance of the model is excellent. The most obvious finding to emerge from this paper is that, with suitable improvements, goaf evaluation models can still maintain a high level of accuracy with small sample size.