基于RUN-XGBoost算法的土石坝渗流预测模型

针对传统土石坝渗流预测模型存在局部最优、抗干扰性差和预测精度低等问题，通过RUN算法优化XGBoost算法得到RUN-XGBoost算法，构建了RUN-XGBoost模型以获得更优的土石坝渗流预测结果。该模型在种群初始化时采用RUN算法对XGBoost算法的3个主要参数进行改进，使预测结果有较高的有效性；通过自动寻找最优参数增进算法的整体收敛速度和预测精度，同时引入随机解，使算法能够排除局部最小值并继续搜索，从而获得全局最优结果。工程实例验证结果表明，RUN-XGBoost模型具有简洁、高效、预测精度高、鲁棒性强等优点。

Seepage prediction model of earth-rockfill dams based on RUN-XGBoost algorithm

Aiming at the problems of local optimality, poor interference resistance, and low prediction accuracy of the traditional seepage monitoring model for earth-rockfill dams, through optimization of the Extreme Gradient Boosting (XGBoost) algorithm (RUN-XGBoost algorithm) by the Runge Kutta optimizer (RUN) algorithm, a RUN-XGBoost model was constructed to obtain better seepage prediction results.The RUN algorithm is applied to improve the three main parameters of the XGBoost algorithm during the initialization of the population, which gives high validity to the prediction results.The overall convergence speed and prediction accuracy of the algorithm are improved by automatic searching for the optimal parameter.A stochastic variance factor is also introduced to enable the algorithm to exclude local minima and continue the search to obtain a globally optimal result.The validation results of engineering examples show that the RUN-XGBoost model has the advantages of simplicity, high efficiency, high prediction accuracy and robustness.