摘要
滑坡易发性评价对区域防灾减灾具有重要意义。针对机器学习算法的滑坡易发性评价中单一分类器精确度欠佳,以及滑坡负样本选择较为随意的问题,提出一种基于信息量法的滑坡负样本选择方式耦合集成学习算法的滑坡易发性评价模型。以黄河上游李家峡至公伯峡段为研究区,选取高程、坡度、降水量等13个因子作为滑坡发生的评价因子,采用缓冲区、低坡度和信息量法3种滑坡负样本选择方式,通过构建分类回归树(CART)以及3种集成学习算法(Bagging、Boosting和随机森林)的滑坡易发性评价模型,分析不同集成学习算法和不同滑坡负样本选择方式下评价模型的性能。结果表明:集成学习算法均可以提升单一基分类器的模型性能,且Boosting算法的提升效果最为突出;信息量法负样本选择方式充分考虑了大多数评价因子,模型可靠性更高。
The evaluation of landslide susceptibility is of great significance for regional disaster prevention and mitigation.In view of the is⁃sues that the single classifier in the landslide susceptibility evaluation using machine learning algorithms had poor precision,and the selection of negative samples of landslides was relatively arbitrary,a landslide susceptibility evaluation model was proposed,which combined the selec⁃tion method of negative samples of landslides based on the information quantity method with machine learning integration algorithms.Taking the section from Lijiaxia to Gongboxia in the upper reaches of the Yellow River as the study area,13 evaluation factors such as elevation,slope gradient and precipitation were selected as the evaluation factors for landslide occurrence.Three selection methods for negative samples of landslides,namely buffer zone,low slope gradient and information quantity were adopted.By building the landslide susceptibility evalua⁃tion models of the classification and regression tree(CART)and three integrated learning algorithms(Bagging,Boosting,and random for⁃est),the performance of the evaluation models under different integrated learning algorithms and different selection methods for negative sam⁃ples of landslides was analyzed.The results show that the integrated learning algorithm can significantly improve the performance of the single base classifier,and the improvement effect of the Boosting algorithm is the most prominent.The selection method of negative samples based on the information quantity takes most of the evaluation factors into full consideration,and the reliability of the model is higher.
作者
郑元勋
周康康
胡少伟
张海超
于国卿
徐路凯
彭浩
ZHENG Yuanxun;ZHOU Kangkang;HU Shaowei;ZHANG Haichao;YU Guoqing;XU Lukai;PENG Hao(College of Water Conservancy and Transportation,Zhengzhou University,Zhengzhou 450001,China;Power China Guiyang Engineering Corporation Limited,Guiyang 550081,China;Yellow River Institute of Hydraulic Research,YRCC,Zhengzhou 450003,China)
出处
《人民黄河》
北大核心
2025年第7期116-123,共8页
Yellow River
基金
国家重点研发计划项目(2022YFC3004400)。
关键词
滑坡易发性
集成学习
信息量法
滑坡负样本
黄河上游
landslide susceptibility
integrated learning
information value method
landslide negative samples
upper reaches of the Yellow River
作者简介
郑元勋(1978-),男,河南驻马店人,教授,博士生导师,主要从事水利工程灾害监测方面的研究工作;通信作者:周康康(1999-),男,河南洛阳人,硕士研究生,主要从事水利防灾减灾方面的研究工作,E⁃mail:1754759331@qq.com。
