# Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm
###### Abstract  
To prevent large-scale water inrush accidents during the excavation process of a water-rich tunnel, a method, based on a random forest (RF) algorithm, for predicting the hazard level of water inrush is proposed. By analyzing hydrogeological conditions, six factors were selected as evaluating indicators, including stratigraphic lithology, inadequate geology, rock dip angle, negative terrain area ratio, surrounding rock grade, and hydrodynamic zonation. Through the statistical analysis of 232 accident sections, a dataset of water inrush accidents in water-rich tunnels was established. We preprocessed the dataset by detecting and replacing outliers, supplementing missing values, and standardizing the data. Using the RF model in machine learning, an intelligent prediction model for the hazard of water inrush in water-rich tunnels was established through the application of datasets and parameter optimization processing. At the same time, a support vector machine (SVM) model was selected for comparison and verification, and the prediction accuracy of the RF model reached 98%, which is higher than the 87% of the SVM. Finally, the model was validated by taking the water inrush accident in the Yuanliangshan tunnel as an example, and the predicted results have a high degree of consistency with the actual hazard level. This indicates that the RF model has good performance when predicting water inrush in water-rich tunnels and that it can provide a new means by which to predict the hazard of water inrush in water-rich tunnels. random forest; water-rich tunnel; water inrush; data preprocessing; machine learning +
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0000-0001-6000-0001-60001-60001-60001-60001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-6001-60001-6001-6001-6001-6001-Zhou [PERSON]'s [6] attribute interval recognition method, various methods have been successfully applied in some tunnels. However, since they presuppose some patterns or specify some parameters in advance, their results are subjective. In recent years, artificial intelligence has gradually emerged, underpinning systems such as the BP neural network, RBF neural network and SVM, which have gradually been applied to predict the hazard of water and mud inrush disasters in tunnels [7; 8; 9; 10; 11; 12]. Although these methods are all used to determine the hazard of water inrush in tunnels, the performances of the models are different. Although BP neural networks have strong learning abilities and can handle complex nonlinear relationships, they have drawbacks, such as requiring a large sample size, having a slow learning convergence speed, and being prone to falling into local optima. Although RBF neural networks have fast computation speed, fewer parameters, and nonlinear mapping ability, they have disadvantages, such as their difficulty when selecting suitable centers and adjusting the network structure and their inability to learn online. Additionally, although SVM networks have the advantages of high efficiency, strong model generalization ability, and high robustness, they have some problems in parameter tuning, handling multi classification problems, and storing and computing large-scale datasets. Considering the above issues, it is necessary to find a model with high accuracy and stability and good robustness in order to be able to apply it to the identification of water inrush hazards in water-rich tunnels. RF has the above characteristics and has shown good performance in practical applications [13]. RF is an ensemble learning method that uses autonomous sampling, cross validation, and other methods to improve the accuracy, stability, and robustness of the model during the model building process. However, research has found that stability and robustness are generally not used as evaluation indicators for classification performance in machine learning.