基于IF-GEP的河湾最大冲刷深度预测方法

Maximum Erosion Depth Prediction of River Bend Based on IF-GEP

为解决传统河流流经弯道的最大冲刷深度预测过程中存在的不足,将孤立森林(IF)和基因表达式编程(GEP)方法相结合,建立了一个基于IF的GEP河湾最大冲刷深度预测模型(IF-GEP),并将该模型与传统GS-SVR和RF模型及现有经验公式进行对比。结果表明,IF-GEP预测模型在测试集上取得了较好的预测效果,且预测精度明显高于现有公式及传统的GS-SVR和RF模型。最后将该预测模型应用于多条不同河流的预测中,IF-GEP预测模型的预测结果与实际测量值较吻合,说明该预测模型具有良好的预测能力和较高的泛化性能。

In order to address the limitations in forecasting the maximum scour depth of conventional river bends,this study amalgamated the methodologies of isolated forest(IF)and gene expression programming(GEP).An IF-GEP model for predicting the maximum scour depth of river bends was established.The validation results demonstrate that the IF-GEP prediction model surpasses existing formulations in terms of its accuracy on the test set.Moreover,it exhibits enhanced predictive performance compared to the traditional GS-SVR and RF models.Application of the prediction model to various rivers yielded remarkably close results to the actual measured values,affirming its strong predictive capability and robust generalization performance.