探索先进的机器学习技术,绘制中国延川县滑坡易发性地图

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Wei Chen, Chao Guo, Fanghao Lin, Ruixin Zhao, Tao Li, Paraskevas Tsangaratos, Ioanna Ilia
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引用次数: 0

摘要

中国每年都会发生多起山体滑坡,造成大量财产损失和人员伤亡。绘制滑坡易发性地图对于政府或相关机构预防灾害、保护人民生命财产安全至关重要。本研究采用优化策略,比较了随机森林(RF)、分类与回归树(CART)、贝叶斯网络(BN)和逻辑模型树(LMT)方法在生成延川县滑坡易感性图方面的性能。通过实地调查,绘制了 311 幅滑坡地图。数据集按 7:3 的比例分为训练数据集和验证数据集。根据研究区域的地质调查,确定了 16 个影响滑坡的因素,包括海拔、平面曲率、剖面曲率、坡面、坡角、坡长、地形位置指数(TPI)、地形崎岖指数(TRI)、收敛指数、归一化差异植被指数(NDVI)、道路距离、河流距离、降雨量、土壤类型、岩性和土地利用。训练数据集用于在 Weka 软件中训练模型,滑坡易发性地图则在 GIS 软件中生成。通过接收器操作特征曲线(ROC)、混淆矩阵、卡方检验和其他统计分析方法对四种模型的性能进行了评估。比较结果表明,四种机器学习模型都适用于评估研究区域的滑坡易发性。RF 和 LMT 方法的性能比其他两种模型更稳定,因此适合用于滑坡易感性绘图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Exploring advanced machine learning techniques for landslide susceptibility mapping in Yanchuan County, China

Exploring advanced machine learning techniques for landslide susceptibility mapping in Yanchuan County, China

Many landslides occurred every year, causing extensive property losses and casualties in China. Landslide susceptibility mapping is crucial for disaster prevention by the government or related organizations to protect people's lives and property. This study compared the performance of random forest (RF), classification and regression trees (CART), Bayesian network (BN), and logistic model trees (LMT) methods in generating landslide susceptibility maps in Yanchuan County using optimization strategy. A field survey was conducted to map 311 landslides. The dataset was divided into a training dataset and a validation dataset with a ratio of 7:3. Sixteen factors influencing landslides were identified based on a geological survey of the study area, including elevation, plan curvature, profile curvature, slope aspect, slope angle, slope length, topographic position index (TPI), terrain ruggedness index (TRI), convergence index, normalized difference vegetation index (NDVI), distance to roads, distance to rivers, rainfall, soil type, lithology, and land use. The training dataset was used to train the models in Weka software, and landslide susceptibility maps were generated in GIS software. The performance of the four models was evaluated by receiver operating characteristic (ROC) curves, confusion matrix, chi-square test, and other statistical analysis methods. The comparison results show that all four machine learning models are suitable for evaluating landslide susceptibility in the study area. The performances of the RF and LMT methods are more stable than those of the other two models; thus, they are suitable for landslide susceptibility mapping.

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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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