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Landslide susceptibility prediction (LSP) has been widely and effectively implemented by machine learning (ML) models based on remote sensing (RS) images and Geographic Information System (GIS). However, comparisons of the applications of ML models for LSP from the perspectives of supervised machine learning (SML) and unsupervised machine learning (USML) have not been explored. Hence, this study aims to compare the LSP performance of these SML and USML models, thus further to explore the advantages and disadvantages of these ML models and to realize a more accurate and reliable LSP result. Two representative SML models (support vector machine (SVM) and CHi-squared Automatic Interaction Detection (CHAID)) and two representative USML models (K-means and Kohonen models) are respectively used to scientifically predict the landslide susceptibility indexes, and then these prediction results are discussed. Ningdu County with 446 recorded landslides obtained through field investigations is introduced as case study. A total of 12 conditioning factors are obtained through procession of Landsat TM 8 images and high-resolution aerial images, topographical and hydrological spatial analysis of Digital Elevation Modeling in GIS software, and government reports. The area value under the curve of receiver operating features (AUC) is applied for evaluating the prediction accuracy of SML models, and the frequency ratio (FR) accuracy is then introduced to compare the remarkable prediction performance differences between SML and USML models. Overall, the receiver operation curve (ROC) results show that the AUC of the SVM is 0.892 and is slightly greater than the AUC of the CHAID model (0.872). The FR accuracy results show that the SVM model has the highest accuracy for LSP (77.80%), followed by the CHAID model (74.50%), the Kohonen model (72.8%) and the K-means model (69.7%), which indicates that the SML models can reach considerably better prediction capability than the USML models. It can be concluded that selecting recorded landslides as prior knowledge to train and test the LSP models is the key reason for the higher prediction accuracy of the SML models, while the lack of a priori knowledge and target guidance is an important reason for the low LSP accuracy of the USML models. Nevertheless, the USML models can also be used to implement LSP due to their advantages of efficient modeling processes, dimensionality reduction and strong scalability.

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It is significant to do landslide susceptibility assessment (LSA) accurately and efficiently using an appropriate model for landslide prediction and prevention. This article aims to compare the frequency ratio (FR) model with the support vector machine (SVM), for mapping the landslide susceptibility of Nantian area in southeastern hilly area, China. To begin, 70 recorded landslides are identified through field investigation and the land and recourse department, 50% of the landslide grid cells are used to train the models and the remaining 50% of the landslide grid cells are used to test the models. Ten environmental factors are used in the modeling of LSA, including the elevation, slope, aspect, plan curvature, profile curvature, relief amplitude, lithology factor, distance to river, Normalized Difference Build-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Then the landslide susceptibility maps of Nantian area are produced by the FR and SVM models, respectively. Finally, the accuracies and efficiencies of both two models are evaluated and compared. The results show that the landslide susceptibility distribution characteristics of Nantian area are explored well by the two models, and the FR model has higher prediction rate and is considerably more efficient than SVM for LSA.