基于相关性准则和R-ELM模型的岩溶隧道涌水量预测研究

为实现隧道涌水量的高精度预测，以相关系数法和极限学习机为理论基础，构建隧道涌水量预测模型。首先，结合工程实例对隧道涌水的影响因素进行分析，并利用相关系数法分析各因素与涌水量之间的相关性，以筛选出重要影响因素；其次，将筛选出的重要因素作为预测模型的输入层，并利用试算法和经验公式优化极限学习机的模型参数，再利用M估计弱化预测误差，进而构建出用于隧道涌水预测的R-ELM模型。研究表明： 1）岩溶隧道涌水灾害的影响因素较多，包括5类一级因素和12类二级因素，不同因素对隧道涌水灾害的影响程度存在一定差异； 2）R-ELM模型预测结果的平均相对误差仅为1.12%，具有较高的预测精度，不仅验证了模型参数优化和M估计优化的有效性，也验证了R-ELM模型在隧道涌水量预测中的适用性。

Prediction of Water Inflow in Karst Tunnels Based on Correlation Criterion and R ELM Model

A prediction model of tunnel water inflow is established based on correlation coefficient method and extreme learning machine to achieve high precision prediction of tunnel water inflow. Firstly, the influencing factors of tunnel water inflow are analyzed based on engineering practices, and the correlation coefficient method is used to analyze the correlation between each factor and water inflow so as to screen out the important influencing factors. Secondly, the selected important factors are used as the input layer of prediction model, the model parameters of extreme learning machine are optimized by trial algorithm and empirical formula, and then the prediction error is weakened by M estimation to construct R ELM model for predicting tunnel water inflow. The study results show that: (1) There are many factors affecting the water inflow disaster in karst tunnels, including 5 primary factors and 12 secondary factors, and the influence degree of different factors on the water inflow disaster varies. (2) The average relative error of R ELM model prediction results is only 1.12%, which shows that the model has high prediction accuracy and verifies the validity of model parameter optimization and M estimation optimization and the applicability of the model in the prediction of tunnel water inflow.