SkySlide: A Hybrid Method for Landslide Susceptibility Assessment based on Landslide-Occurring Data Only

2020 ◽  
Author(s):  
Alev Mutlu ◽  
Furkan Goz
Keyword(s):  
Hybrid Method ◽  
Landslide Susceptibility ◽  
Performance Metrics ◽  
Geometric Mean ◽  
Imbalanced Data ◽  
Majority Voting ◽  
Support Vector ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment ◽  
Skyline Operator

Abstract Landslide susceptibility assessment is the problem of determining the likelihood of a landslide occurrence in a particular area with respect to the geographical and morphological properties of the area. This paper presents a hybrid method, namely SkySlide, that incorporates clustering, skyline operator, classification and majority voting principle for region-scale landslide susceptibility assessment. Clustering and skyline operator are utilized to model landslides while classification and majority voting principle are utilized to assess landslide susceptibility. The contribution of the study is 2-fold. First, the proposed method requires properties of landslide-occurring data only to model landslides. Second, the proposed method is evaluated on imbalanced data and experimental results include performance metrics of imbalanced data. Experiments conducted on two real-life datasets show that clustering greatly improves performance of SkySlide. Experiments further demonstrate that SkySlide achieves higher class balance accuracy, Matthews correlation coefficient, geometric mean and bookmaker informedness scores compared with the most commonly used methods for landslide susceptibility assessment such as support vector machines, logistic regression and decision trees.

scholarly journals Seismic Landslide Susceptibility Assessment Using Principal Component Analysis And Support Vector Machine

2021 ◽  
Author(s):  
Ziyao Xu ◽  
Ailan Che ◽  
Yanbo Cao ◽  
Fanghao Zhang
Keyword(s):  
Landslide Susceptibility ◽  
Principal Component ◽  
Assessment Model ◽  
Support Vector ◽  
Susceptibility Assessment ◽  
Risk Class ◽  
Conditioning Factors ◽  
Landslide Susceptibility Assessment ◽  
Susceptibility Model ◽  
Seismic Landslide

Abstract Seismic landslides are dangerous natural hazards, causing immense damage in terms of human lives and property. Susceptibility assessment of earthquake triggered landslide is the scientific premise and theoretical basis of disaster emergency management of engineering. The aim of this study is to applied the seismic landslide susceptibility model to Dayong Expressway in Chenghai area prone to frequent earthquakes. Support vector machine is used to establish the assessment model based on the data of 716 landslides caused by Ludian Ms6.5 earthquake in 2014. To improve the universality of the assessment model in different regions. Principal component analysis (PCA) is used for reducing the dimension of landslide conditioning factors and weaking difference of the regional characteristics between historical earthquake regions with Dayong expressway area. To applied the SVM model for seismic landslide susceptibility in Dayong Expressway region where the conditioning factors information is similar to Ludian area. Gutenberg-Richter model and Dieterich model are used to assume an earthquake in Chenghai area for landslide susceptibility assessment. Inverse distance weight (IDW) method is used for assessing the landslide risk class of Dayong Expressway. The results show that the “Very high” landslide susceptibility class account for 0.63% of Chenghai area. The seismic landslide has the most obvious impact on the middle 13 km section of Dayong expressway and this section account for 8.9% is defined as high-risk class. The study verifies the practicability of the seismic landslide susceptibility model based on machine learning and provides constructive reference for the susceptibility assessment of engineering facilities under earthquake.

scholarly journals Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia

2018 ◽  
Vol 10 (10) ◽  
pp. 1527 ◽  
Author(s):  
Dieu Tien Bui ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Kamran Chapi ◽  
Mohsen Alizadeh ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Landslide Susceptibility ◽  
Support Vector ◽  
Susceptibility Assessment ◽  
Validation Process ◽  
Landslide Susceptibility Assessment ◽  
Tropical Environments ◽  
Index Of Entropy ◽  
Svm Model ◽  
Cameron Highlands

Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.

scholarly journals Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

2021 ◽  
Vol 9 ◽  
Author(s):  
Yin Xing ◽  
Jianping Yue ◽  
Zizheng Guo ◽  
Yang Chen ◽  
Jia Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Large Scale ◽  
Characteristic Curve ◽  
Integrated Model ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Gray Wolf ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment

Integration of different models may improve the performance of landslide susceptibility assessment, but few studies have tested it. The present study aims at exploring the way to integrating different models and comparing the results among integrated and individual models. Our objective is to answer this question: Will the integrated model have higher accuracy compared with individual model? The Lvliang mountains area, a landslide-prone area in China, was taken as the study area, and ten factors were considered in the influencing factors system. Three basic machine learning models (the back propagation (BP), support vector machine (SVM), and random forest (RF) models) were integrated by an objective function where the weight coefficients among different models were computed by the gray wolf optimization (GWO) algorithm. 80 and 20% of the landslide data were randomly selected as the training and testing samples, respectively, and different landslide susceptibility maps were generated based on the GIS platform. The results illustrated that the accuracy expressed by the area under the receiver operating characteristic curve (AUC) of the BP-SVM-RF integrated model was the highest (0.7898), which was better than that of the BP (0.6929), SVM (0.6582), RF (0.7258), BP-SVM (0.7360), BP-RF (0.7569), and SVM-RF models (0.7298). The experimental results authenticated the effectiveness of the BP-SVM-RF method, which can be a reliable model for the regional landslide susceptibility assessment of the study area. Moreover, the proposed procedure can be a good option to integrate different models to seek an “optimal” result.

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