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Title
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Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms
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Type
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JournalPaper
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Keywords
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Landslide susceptibility Longhai area Naïve Bayes RBF Classifier RBF Network
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Abstract
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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 Naïve 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.
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Researchers
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Baharin Ben Ahmad (Not In First Six Researchers), Kamran Chapi (Not In First Six Researchers), Xiaojing Wang (Not In First Six Researchers), Yingtao Chen (Not In First Six Researchers), Jianquan Ma (Not In First Six Researchers), Huiyuan Bian (Not In First Six Researchers), Huichan Chai (Not In First Six Researchers), Nianqin Wang (Not In First Six Researchers), Wei Chen (Fifth Researcher), Shaojun Li (Fourth Researcher), Ataollah Shirzadi (Third Researcher), Himan Shahabi (Second Researcher), Qingfeng He (First Researcher)
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