Abstract:

In order to solve the problems that machine learning exists in the hazard intelligent prediction field of tunnel water inrush, such as relatively simple models and imperfect prediction accuracy, a prediction model based on the stacking ensemble learning was proposed. Firstly, the tunnel water inrush disaster dataset was established by collecting 232 groups of water inrush disaster data from 95 tunnels, and the data was preprocessed. Then, 3 base learners and 2 meta learners were selected to train 8 sets of stacking ensemble models in different combinations, and 6 sets of optimal ensemble models were selected. Finally, the optimal stacking ensemble model was selected by comparing and analyzing the prediction results of 6 groups of parameters optimized and stacking ensemble model with the grid search parameters and the 5-fold cross-validation hyperparameter optimization model. The results show that SVM(Support Vector Machine )+NB (Naive Bayes) + LR (Linear Regression) ensemble model is obtained after the optimal single model SVM is improved with the stacking ensemble learning method. Its accuracy, recall, and F₁ score are 0.94, 0.91, and 0.92, respectively. The overall prediction effect is better than that of other compative models, and it can accurately predict the hazard level of tunnel water inrush.

Key words: Stacking ensemble learning, tunnel water inrush, prediction model, hazard level, machine learning