Dynamic landslide susceptibility Assessment integrating future land use and vegetation changes: Cellular-automata markov-models and machine learning for zigui county, China

IF 2.8 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Fei Guo, Cheng Chen, Hao Fang, Qingshan Ma, Tao Huang, Hongtao Tian, Qiaoyi Dai
{"title":"Dynamic landslide susceptibility Assessment integrating future land use and vegetation changes: Cellular-automata markov-models and machine learning for zigui county, China","authors":"Fei Guo,&nbsp;Cheng Chen,&nbsp;Hao Fang,&nbsp;Qingshan Ma,&nbsp;Tao Huang,&nbsp;Hongtao Tian,&nbsp;Qiaoyi Dai","doi":"10.1007/s12665-025-12494-9","DOIUrl":null,"url":null,"abstract":"<div><p>Landslide susceptibility assessment involves numerous dynamic factors that can influence the predictive accuracy. This study targets Zigui County, located at the head of the Three Gorges Reservoir Area, a region prone to landslides due to its complex geological and environmental conditions. To incorporate temporal variability, the Cellular Automata-Markov (CA-Markov) model is employed to simulate and predict dynamic factors, specifically land use/land cover (LULC) changes and the normalized difference vegetation index (NDVI). The GeoDetector tool is then applied to construct an evaluation index system. Logistic regression (LR), support vector machine (SVM), and random forest (RF) models are utilized to assess landslide susceptibility, followed by a comparative analysis of their results. The results confirm the effectiveness of the CA–Markov model in predicting dynamic factors. For the 2023 land use/land cover (LULC) prediction, the proportion of cultivated land, grassland, and construction land increased by 0.49%, 0.01%, and 1.61%, respectively, while forest land and water area decreased by 1.54% and 0.56%. Additionally, the 2023 NDVI prediction, the NDVI forecast shows a 1.93% reduction in areas with positive vegetation coverage. Among the models, the RF model demonstrates higher predictive accuracy and reliability compared to the LR and SVM models. The areas with extremely high and high landslide susceptibility are mainly located along on the Yangtze River and its tributaries, including Xietan, Zhaxi, Xiangxi, Qinggan (Luogudong) and Tongzhuang Rivers, as well as along major highways such as Provincial Highway S363 and National Highway G348.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 18","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Earth Sciences","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s12665-025-12494-9","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Landslide susceptibility assessment involves numerous dynamic factors that can influence the predictive accuracy. This study targets Zigui County, located at the head of the Three Gorges Reservoir Area, a region prone to landslides due to its complex geological and environmental conditions. To incorporate temporal variability, the Cellular Automata-Markov (CA-Markov) model is employed to simulate and predict dynamic factors, specifically land use/land cover (LULC) changes and the normalized difference vegetation index (NDVI). The GeoDetector tool is then applied to construct an evaluation index system. Logistic regression (LR), support vector machine (SVM), and random forest (RF) models are utilized to assess landslide susceptibility, followed by a comparative analysis of their results. The results confirm the effectiveness of the CA–Markov model in predicting dynamic factors. For the 2023 land use/land cover (LULC) prediction, the proportion of cultivated land, grassland, and construction land increased by 0.49%, 0.01%, and 1.61%, respectively, while forest land and water area decreased by 1.54% and 0.56%. Additionally, the 2023 NDVI prediction, the NDVI forecast shows a 1.93% reduction in areas with positive vegetation coverage. Among the models, the RF model demonstrates higher predictive accuracy and reliability compared to the LR and SVM models. The areas with extremely high and high landslide susceptibility are mainly located along on the Yangtze River and its tributaries, including Xietan, Zhaxi, Xiangxi, Qinggan (Luogudong) and Tongzhuang Rivers, as well as along major highways such as Provincial Highway S363 and National Highway G348.

考虑未来土地利用和植被变化的动态滑坡易感性评价:基于元胞自动机马尔可夫模型和机器学习的秭归县研究
滑坡易感性评价涉及许多影响预测精度的动态因素。本研究以秭归县为研究对象,秭归县位于三峡库区前端,地质环境条件复杂,是滑坡易发地区。为了纳入时间变化,采用细胞自动机-马尔可夫(CA-Markov)模型模拟和预测动态因子,特别是土地利用/土地覆盖(LULC)变化和归一化植被指数(NDVI)。然后应用GeoDetector工具构建评价指标体系。利用逻辑回归(LR)、支持向量机(SVM)和随机森林(RF)模型评估滑坡易感性,并对其结果进行比较分析。结果证实了CA-Markov模型在预测动态因素方面的有效性。2023年土地利用/土地覆盖(LULC)预测中,耕地、草地和建设用地比例分别增加0.49%、0.01%和1.61%,林地和水域面积分别减少1.54%和0.56%。在2023年NDVI预测中,植被覆盖度为正的区域NDVI减少了1.93%。其中,与LR和SVM模型相比,RF模型具有更高的预测精度和可靠性。滑坡易感性极高和高易感性地区主要分布在谢滩、扎西、湘西、青干(锣鼓洞)、桐庄等长江及其支流沿线,以及S363省道、G348国道等主要公路沿线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Environmental Earth Sciences
Environmental Earth Sciences 环境科学-地球科学综合
CiteScore
5.10
自引率
3.60%
发文量
494
审稿时长
8.3 months
期刊介绍: Environmental Earth Sciences is an international multidisciplinary journal concerned with all aspects of interaction between humans, natural resources, ecosystems, special climates or unique geographic zones, and the earth: Water and soil contamination caused by waste management and disposal practices Environmental problems associated with transportation by land, air, or water Geological processes that may impact biosystems or humans Man-made or naturally occurring geological or hydrological hazards Environmental problems associated with the recovery of materials from the earth Environmental problems caused by extraction of minerals, coal, and ores, as well as oil and gas, water and alternative energy sources Environmental impacts of exploration and recultivation – Environmental impacts of hazardous materials Management of environmental data and information in data banks and information systems Dissemination of knowledge on techniques, methods, approaches and experiences to improve and remediate the environment In pursuit of these topics, the geoscientific disciplines are invited to contribute their knowledge and experience. Major disciplines include: hydrogeology, hydrochemistry, geochemistry, geophysics, engineering geology, remediation science, natural resources management, environmental climatology and biota, environmental geography, soil science and geomicrobiology.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信