融合XGBoost和SVR的滑坡位移预测  

Fusion of XGBoost and SVR for Landslide Displacement Prediction

作　　者：王惠琴[1] 梁啸 何永强[2] 李晓娟 张建良 郭瑞丽 刘宾灿 WANG Huiqin;LIANG Xiao;HE Yongqiang;LI Xiaojuan;ZHANG Jianliang;GUO Ruili;LIU Bincan(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China;School of Civil Engineering,Northwest Minzu University,Lanzhou 730030,China;SCEGC Installation Group Company LTD,Xi’an 710068,China)

机构地区：[1]兰州理工大学计算机与通信学院,甘肃兰州730050 [2]西北民族大学土木工程学院,甘肃兰州730030 [3]陕西建工安装集团有限公司,陕西西安710068

出　　处：《湖南大学学报(自然科学版)》2025年第4期149-158,共10页Journal of Hunan University:Natural Sciences

基　　金：甘肃省交通厅科研项目(2022-14);甘肃省重点研发计划(21YF1GA381);陕西省重点研发计划-工业领域(2024GXYBXM-42);甘肃省科技计划项目(25JRRA043)。

摘　　要：利用极端梯度提升与支持向量回归,同时结合猎人猎物优化算法的优势,提出了一种融合极端梯度提升和支持向量回归的滑坡位移预测模型.首先采用极端梯度提升(extreme gradient boosting,XGBoost)进行滑坡位移初步预测,进一步利用猎人猎物优化算法(hunter-prey optimizer,HPO)优化支持向量回归(support vector regression,SVR)的超参数而构建了一种组合预测模型(HPO-SVR)以修正XGBoost的预测结果.两组滑坡位移实测数据表明:HPO算法通过不断更新猎人与猎物位置的动态寻优策略,获得了更加合理的SVR的超参数.相对于XGBoost、SVR,以及其与粒子群优化算法、遗传算法和HPO的组合预测模型而言,XGBoost-HPO-SVR组合模型在阳屲山滑坡和脱甲山滑坡位移预测中取得了良好的效果,其均方根误差和平均绝对误差分别为3.505和1.357,0.550和0.538.In this paper,a landslide displacement prediction model integrating extreme gradient boosting and optimized support vector regression is proposed by using extreme gradient boosting and support vector regression,and combining the advantages of hunter-prey optimization algorithm.Firstly,extreme gradient boosting(XGBoost)is used for the preliminary prediction of landslide displacement,and then hunter-prey optimizer(HPO)is used to optimize support vector regression(SVR).A combined prediction model(HPO-SVR)is constructed by optimizing the hyperparameters of SVR using HPO to correct the prediction results of XGBoost.The validation of two sets of landslide displacement measured data shows that the HPO algorithm obtains a more reasonable hyperparameter of SVR through the dynamic optimization strategy of constantly updating the positions of the hunter and the prey.Relative to the combined prediction models of XGBoost,SVR,and its combination with particle swarm optimization algorithm,genetic algorithm,and HPO,the combined XGBoost-HPO-SVR model achieves good results in predicting the displacements of Yangwashan landslide and Tuojiashan landslide,with mean square errors of 3.505 and 0.550,and mean absolute errors of 1.357 and 0.538,respectively.

关 键 词：极端梯度提升 支持向量回归 猎人猎物优化算法 滑坡位移预测 

分 类 号：P694[天文地球—地质学]