期刊
SCIENCE OF THE TOTAL ENVIRONMENT
卷 663, 期 -, 页码 1-15出版社
ELSEVIER
DOI: 10.1016/j.scitotenv.2019.01.329
关键词
Landslide susceptibility; Longhai area; Naive Bayes; RBF Classifier; RBF Network
资金
- International Partnership Program of Chinese Academy of Sciences [115242KYSB20170022]
- National Natural Science Foundation of China [41807192, 41572287, 41602359, 41602212]
- China Postdoctoral Science Foundation [2018T111084, 2017M613168]
- Shaanxi Province Postdoctoral Science Foundation [2017BSHYDZZ07]
- NationalMajor Science and Technology Project [2017ZX05030-002]
- Doctoral Scientific Research Foundation of Xi'anUniversity of Science and Technology [2013QDJ038]
- Universiti Teknologi Malaysia (UTM) [Q.J130000.2527.17H84]
Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naive Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world. (C) 2019 Published by Elsevier B.V.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据