地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (5): 953-966.doi: 10.12082/dqxxkx.2023.220567

• 地球信息科学理论与方法 • 上一篇    下一篇

顾及土地利用动态变化的滑坡易发性评估方法

林炫歆1,2(), 肖桂荣1,2,*(), 周侯伯3,4   

  1. 1.福州大学 数字中国研究院(福建),福州 350108
    2.福州大学 空间数据挖掘与信息共享教育部重点实验室,福州 350108
    3.中国科学院城市环境研究所,厦门 361021
    4.中国科学院大学,北京 100049
  • 收稿日期:2022-08-03 修回日期:2022-11-15 出版日期:2023-05-25 发布日期:2023-04-27
  • 通讯作者: *肖桂荣(1972— ),男,福建龙岩人,研究员,主要从事地理信息系统、空间信息网络服务、政务数据可视化研究。E-mail: xiaogr@fzu.edu.cn
  • 作者简介:林炫歆(1999— ),女,福建福州人,硕士研究生,主要从事数据挖掘、机器学习与地质灾害风险评估研究。E-mail: linxuanxin0928@163.com
  • 基金资助:
    中央引导地方科技发展专项(2020L3005);中国科学院A类战略性先导科技专项(XDA23100504)

Landslide Susceptibility Assessment Method Considering Land Use Dynamic Change

LIN Xuanxin1,2(), XIAO Guirong1,2,*(), ZHOU Houbo3,4   

  1. 1. The Academy of Digital China(Fujian), Fuzhou University, Fuzhou 350108, China
    2. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China
    3. Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
    4. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2022-08-03 Revised:2022-11-15 Online:2023-05-25 Published:2023-04-27
  • Contact: XIAO Guirong
  • Supported by:
    The Central Guided Local Development of Science and Technology Project(2020L3005);The Strategic Priority Research Program of the Chinese Academy of Sciences(Class A)(XDA23100504)

摘要:

滑坡灾害成因复杂,滑坡易发性评估对灾害预警与防控管理具有重要意义。由于以往易发性制图研究中未顾及土地利用变化因素,本文提出考虑土地利用动态变化的滑坡易发性评估因子组合。采用滑坡频率比定量衡量土地利用变化与滑坡发育的相关关系,并基于逻辑回归模型(Logistic Regression, LR)对比引入土地利用变化因子前后的模型预测效果。在此基础上,构建决策树(Decision Tree, DT)、梯度提升树(Gradient Boosting Decision Tree, GBDT)与随机森林(Random Forest, RF)模型,利用AUC等指标对模型预测性能进行对比分析。最后以福建省三明市为研究区,以整个福建省为验证区,开展滑坡易发性评估研究。研究表明:土地利用变化因子与滑坡发育之间具有更强的关联特征,在滑坡易发性评估中引入动态变化因子具有必要性。RF模型比DT与GBDT具有更高的预测精度与准确率。滑坡高易发区主要分布在三明市西部与中部地区,该区域土地利用变化程度较大、受人类活动影响大;低易发区基本位于人类活动影响较小的高海拔区域。本研究为滑坡易发性评估提出一种新的研究角度,为探究人类对灾害形成的影响提供帮助。

关键词: 滑坡, 易发性, 土地利用变化, 集成学习, 决策树, 梯度提升树, 随机森林, 三明市

Abstract:

The causes of landslide disasters are complex, and landslide susceptibility assessment is of great significance for disaster warning, prevention, and control management. In the previous mapping studies on landslide susceptibility assessment, land use change factor was not considered. This paper proposed a combination of factors for landslide susceptibility assessment by considering land use dynamic change factor. The landslide frequency ratio was used to quantitatively measure the correlation between land use change and landslide development. And Logistic Regression (LR) model was used to compare the prediction ability of the model before and after the introduction of land use change factor. We constructed three machine learning models: Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF). We used AUC and other indicators to compare model performance. Finally, we took Sanming City of Fujian Province as the study area and the whole Fujian Province as the verification area to conduct the landslide susceptibility assessment research. The results show that there is a strong correlation between land use change factor and landslide development. The inclusion of land use change factor improves model prediction accuracy, which indicates that it is necessary to introduce dynamic factor in the assessment of landslide susceptibility. The verification results show that RF model has higher prediction accuracy than DT and GBDT. The high landslide prone areas are mainly distributed in the west and central of Sanming City, where the land use change degree is high, and the impact of human activities is great. The low landslide prone areas basically locate in the high-altitude areas with little influence of human activities. This study provides a new research perspective for landslide susceptibility assessment and helps to explore the impact of human activities on disaster formation.

Key words: landslide, susceptibility, land use change, ensemble learning, decision tree, gradient boosting decision tree, random forest, Sanming