| Summary: | It is very important to predict the long-term shutdown karst tunnel water inrush for preventing tunnel construction accidents. However, it is urgent to study a new prediction model to solve the problems of insufficient sample size and low prediction accuracy for long-term shutdown karst tunnel water inrush prediction. In this study, the water inrush and atmospheric rainfall in a tunnel project in China were monitored for over five months. By adopting hybrid grey wolf optimization (HGWO) algorithm and support vector regression (SVR) method, the HGWO-SVR tunnel water inrush prediction model was proposed. The atmospheric rainfall of the day and yesterday and yesterday’s water inrush were considered in the HGWO-SVR model, and the model was used to predict the tunnel water inrush. The results show that the predicted water inrush value is basically consistent with the measured value. After the parameters of SVR model are optimized by HGWO algorithm, the HGWO-SVR prediction model has the advantages of high precision and less sample demand. The model is more suitable for the prediction of long-term shutdown tunnel water inrush with less measured sample. Thus, the proposed prediction model can effectively be used as a new approach for tunnel water inrush in some similar projects.
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