Risk Prediction of Water Inrush in Diversion Tunnel Crossing Water-Rich Fault Based on NRBO-XGBoost Algorithm

Water inrush can easily occur during the construction of diversion tunnels crossing water-rich faults, and large-scale water inrushes pose a great threat to construction personnel and machinery. For the construction safety of the diversion tunnel, it is very important to accurately predict the risk of water inrush. Therefore, to reduce the occurrence of water inrush disasters in tunnels, this paper establishes a diversion tunnel water inrush risk prediction model based on the NRBO-XGBoost algorithm on the basis of giving full play to the value of engineering data. Nine indicators were selected from engineering geological conditions, hydrogeological conditions, and tunnel construction conditions on the basis of fully mining engineering data, and the prediction indicator system of the water inrush risk of tunnels through water-rich faults was established. The model was trained and tested using 120 valid samples collected from the Longjinxi diversion tunnel, which realizes accurate and fast water inrush risk prediction in the construction process. Its predictive performance was compared with that of BPNN and the standard XGBoost model. The R2 and MAE of the novel method are 0.9129 and 0.0667, respectively, which are both superior to those of other methods. It confirms the proposed model’s reliability and effectiveness.