Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models

Abstract

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.

MODIS Sensor Capability to Burned Area Mapping—Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions

“…To assess the impact of the PB on the accuracy of a BA classified map, the area enclosed by it (the Area Under the Pareto Boundary, AUPB) was calculated, by analogy with the area of the ROC (Receiver Operating Characteristic) curve used in other burned area studies using machine learning or data mining [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. The ROC curve is a probability curve constructed from sensibility and 1-specificity pairs {(S i ,1-Sp i )}, obtained using a procedure similar to that of the PB, and the area enclosed by it, the Area Under the ROC Curve (AUC), is interpreted as a measure of the separability between the two classes considered from the selected p parameter.…”

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

“…The predictive modeling of landslide occurrence is one of the main challenges in geological hazard research. A landslide susceptibility map (LSM) is an effective visualization technology for the localization of a landslide region and sustainable land management [3][4][5]. Moreover, the landslide susceptibility model based on geological environmental conditions can supply the government with an important theoretical basis for land resource planning and disaster prevention and reduction.…”

Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

“…A large number of unlabeled sites are not utilized, which are rich in location and geographic information [35]. Meanwhile, unsupervised learning using only unlabeled data can also implement LSM due to their advantages of strong efficiency and scalability for training [36]. Therefore, how to make full use of the unlabeled information is a feasible direction for the research of LSM.…”