Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree

2019 ◽  
Vol 34 (11) ◽  
pp. 1177-1201 ◽  
Author(s):  
Wei Chen ◽  
Xia Zhao ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Khabat Khosravi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Logistic Model ◽  
Spatial Prediction ◽  
Belief Function ◽  
Evidential Belief Function ◽  
Model Tree ◽  
Logistic Model Tree

scholarly journals Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 830 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Ayub Mohammadi ◽  
Himan Shahabi ◽  
Baharin Bin Ahmad ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Logistic Regression ◽  
Random Forest ◽  
Landslide Susceptibility ◽  
Predictive Value ◽  
Logistic Model ◽  
Model Tree ◽  
Cameron Highlands ◽  
Logistic Model Tree

We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area.

scholarly journals Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

2020 ◽  
Vol 17 (8) ◽  
pp. 2749 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
Sushant K. Singh ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Logistic Model ◽  
Naive Bayes ◽  
Shallow Landslide ◽  
Naïve Bayes ◽  
Support Vector ◽  
Model Tree ◽  
Logistic Model Tree

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

scholarly journals Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xiangang Luo ◽  
Feikai Lin ◽  
Yihong Chen ◽  
Shuang Zhu ◽  
Zhanya Xu ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Logistic Model ◽  
Economic Losses ◽  
Susceptibility Map ◽  
Random Subspace ◽  
Human Engineering ◽  
Mountain Area ◽  
Landslide Occurrence ◽  
Model Tree ◽  
Logistic Model Tree

Abstract Landslide disasters cause huge casualties and economic losses every year, how to accurately forecast the landslides has always been an important issue in geo-environment research. In this paper, a hybrid machine learning approach RSLMT is firstly proposed by coupling Random Subspace (RS) and Logistic Model Tree (LMT) for producing a landslide susceptibility map (LSM). With this method, the uncertainty introduced by input features is considered, the problem of overfitting is solved by reducing dimensions to increase the prediction rate of landslide occurrence. Moreover, the uncertainty of prediction will be deeply discussed with the rank probability score (RPS) series, which is an important evaluation of uncertainty but rarely used in LSM. Qingchuan county, China was taken as a study area. 12 landslide causal factors were selected and their contribution on landslide occurrence was evaluated by ReliefF method. In addition, Logistic Model Tree (LMT), Naive Bayes (NB) and Logistic Regression (LR) were researched for comparison. The results showed that RSLMT (AUC = 0.815) outperformed LMT (AUC = 0.805), NB (AUC = 0.771), LR (AUC = 0.785). LSM of Qingchuan county was produced using the novel model, it indicated that landslides tend to occur along with the fault belts and the middle-low mountain area that is strongly influenced by the large numbers of human engineering activities.

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