Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression

Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the over-fitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas.

Download Full-text

Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽

10.3390/e20110884 ◽

2018 ◽

Vol 20 (11) ◽

pp. 884 ◽

Cited By ~ 28

Author(s):

Tingyu Zhang ◽

Ling Han ◽

Wei Chen ◽

Himan Shahabi

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Landslide Susceptibility ◽

Shaanxi Province ◽

Slope Aspect ◽

Support Vector ◽

Operating Characteristics ◽

Study Region ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

Download Full-text

Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models

Geomatics Natural Hazards and Risk ◽

10.1080/19475705.2018.1481147 ◽

2018 ◽

Vol 9 (1) ◽

pp. 1053-1070 ◽

Cited By ~ 16

Author(s):

Hyun-Joo Oh ◽

Prima Riza Kadavi ◽

Chang-Wook Lee ◽

Saro Lee

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Landslide Susceptibility ◽

Belief Function ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Support Vector ◽

Evidential Belief Function

Download Full-text

Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

Entropy ◽

10.3390/e21020106 ◽

2019 ◽

Vol 21 (2) ◽

pp. 106 ◽

Cited By ~ 20

Author(s):

Qingfeng He ◽

Zhihao Xu ◽

Shaojun Li ◽

Renwei Li ◽

Shuai Zhang ◽

...

Keyword(s):

Decision Tree ◽

Landslide Susceptibility ◽

Entropy Method ◽

Decision Tree Classifier ◽

Rotation Forest ◽

Conditioning Factors ◽

Susceptibility Modeling ◽

Landslide Hazards ◽

Tree Classifier ◽

Hybrid Intelligence

Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.

Download Full-text

Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)

Geomatics Natural Hazards and Risk ◽

10.1080/19475705.2017.1407368 ◽

2017 ◽

Vol 9 (1) ◽

pp. 49-69 ◽

Cited By ~ 102

Author(s):

Bahareh Kalantar ◽

Biswajeet Pradhan ◽

Seyed Amir Naghibi ◽

Alireza Motevalli ◽

Shattri Mansor

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Logistic Regression ◽

Artificial Neural Networks ◽

Landslide Susceptibility ◽

Training Data ◽

Data Selection ◽

Landslide Susceptibility Mapping ◽

Support Vector ◽

Training Data Selection

Download Full-text

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

Forests ◽

10.3390/f11010118 ◽

2020 ◽

Vol 11 (1) ◽

pp. 118 ◽

Cited By ~ 6

Author(s):

Viet-Hung Dang ◽

Nhat-Duc Hoang ◽

Le-Mai-Duyen Nguyen ◽

Dieu Tien Bui ◽

Pijush Samui

Keyword(s):

Machine Learning ◽

Random Forest ◽

Landslide Susceptibility ◽

Spatial Prediction ◽

Shallow Landslide ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Conditioning Factors ◽

Susceptibility Modeling

This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.

Download Full-text