顾及样本敏感性的滑坡易发性评价

doi:10.13474/j.cnki.11-2246.2022.0319

测绘通报 ›› 2022, Vol. 0 ›› Issue (11): 20-25.doi: 10.13474/j.cnki.11-2246.2022.0319

顾及样本敏感性的滑坡易发性评价

吕蓓茹^1,2, 彭玲^1,2, 李樵民³

1. 中国科学院空天信息创新研究院, 北京 100094;
2. 中国科学院大学资源与环境学院, 北京 100049;
3. 宁夏回族自治区遥感调查院, 宁夏银川 750021

收稿日期:2021-12-06 发布日期:2022-12-08
通讯作者: 彭玲,E-mail:pengling@aircas.ac.cn
作者简介:吕蓓茹(1996-),女,硕士生,主要研究方向为多源时空数据融合。E-mail:lvbeiru@qq.com
基金资助:
宁夏回族自治区重点研发计划(2020BFG02013)

Landslide susceptibility evaluation considering sample sensitivity

Lü Beiru^1,2, PENG Ling^1,2, LI Qiaomin³

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
2. School of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
3. Ningxia Hui Autonomous Region Remote Sensing lnvestigation Institute, Yinchuan 750021, China

Received:2021-12-06 Published:2022-12-08

摘要/Abstract

摘要： 滑坡作为一种危害极大的自然地质现象,严重威胁着人民的生命财产安全。因此,科学、准确地评价滑坡体的易发性至关重要。随着机器学习的发展,基于机器学习的滑坡易发性评价逐渐成为研究热点。而在真实情况中,滑坡区域与非滑坡区域面积占比悬殊,这使得机器学习模型的应用存在较严重的样本不均衡问题。本文采用样本敏感性分析方法,综合多个机器学习模型在不同比例的正负滑坡样本集上的表现,以获取最均衡滑坡样本集;并在此样本集基础上采用深度随机森林模型,在示范研究区开展滑坡易发性评价。最终的评价结果接近真实分布,表明本文方法具有较好的有效性。

关键词: 滑坡易发性, 样本敏感性分析, 机器学习, 深度随机森林

Abstract: As natural geological phenomena of involving great danger, landslides have seriously threatened people's lives and property. Therefore, it is very important to predict the susceptibility of landslide scientifically and accurately. With the development of machine learning, the prediction of landslide susceptibility based on machine learning has become a research hotspot. But in the real situation, the area ratio of non-landslide and landslide area is very large, which makes the application of machine learning model exist serious sample imbalance problem. In order to obtain the most balanced landslide sample set, the performance of multiple machine learning models on different proportion of positive and negative landslide sample set is analyzed. The multigraded cascade forest model is trained on this sample set and used to predict the landslide susceptibility in the study area. The final prediction results are close to the real distribution, which shows that the method presented in this paper is effective.

Key words: landslide susceptibility, sample sensitivity analysis, machine learning, deep random forest

中图分类号:

P237

吕蓓茹, 彭玲, 李樵民. 顾及样本敏感性的滑坡易发性评价[J]. 测绘通报, 2022, 0(11): 20-25.

Lü Beiru, PENG Ling, LI Qiaomin. Landslide susceptibility evaluation considering sample sensitivity[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 20-25.

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顾及样本敏感性的滑坡易发性评价

Landslide susceptibility evaluation considering sample sensitivity

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