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

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 106 ◽  
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.

scholarly journals GIS-Based Evaluation of Landslide Susceptibility Models Using Certainty Factors and Functional Trees-Based Ensemble Techniques

2019 ◽  
Vol 10 (1) ◽  
pp. 16 ◽  
Author(s):  
Xia Zhao ◽  
Wei Chen
Keyword(s):  
Landslide Susceptibility ◽  
Area Under The Curve ◽  
Training Data ◽  
Statistical Parameters ◽  
Validation Data ◽  
Rotation Forest ◽  
Conditioning Factors ◽  
Susceptibility Modeling ◽  
Ensemble Techniques ◽  
Landslide Inventory Map

The main purpose of this paper is to use ensembles techniques of functional tree-based bagging, rotation forest, and dagging (functional trees (FT), bagging-functional trees (BFT), rotation forest-functional trees (RFFT), dagging-functional trees (DFT)) for landslide susceptibility modeling in Zichang County, China. Firstly, 263 landslides were identified, and the landslide inventory map was established, and the landslide locations were randomly divided into 70% (training data) and 30% (validation data). Then, 14 landslide conditioning factors were selected. Furthermore, the correlation analysis between conditioning factors and landslides was applied using the certainty factor method. Hereafter, four models were applied for landslide susceptibility modeling and zoning. Finally, the receiver operating characteristic (ROC) curve and statistical parameters were used to evaluate and compare the overall performance of the four models. The results showed that the area under the curve (AUC) for the four models was larger than 0.74. Among them, the BFT model is better than the other three models. In addition, this study also illustrated that the integrated model is not necessarily more effective than a single model. The ensemble data mining technology used in this study can be used as an effective tool for future land planning and monitoring.

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

2018 ◽  
Vol 8 (12) ◽  
pp. 2540 ◽  
Author(s):  
Wei Chen ◽  
Himan Shahabi ◽  
Shuai Zhang ◽  
Khabat Khosravi ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Support Vector ◽  
Machine Method ◽  
Conditioning Factors ◽  
Kernel Logistic Regression ◽  
Susceptibility Modeling ◽  
Tree Classifier ◽  
Predictive Rate

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.

scholarly journals Comparison of rotation forest based intelligence models for landslide susceptibility modeling

2021 ◽  
Author(s):  
Xia Zhao ◽  
Wei Chen ◽  
Tao Li ◽  
Faming Huang ◽  
Chaohong Peng ◽  
...  
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Spatial Prediction ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Rotation Forest ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract The precision of landslide susceptibility assessment has always been the focus of landslide spatial prediction research. It can be considered as the possibility of landslide disaster under the action of human activities or natural factors, or both of them. For the further exploration of the mechanism of this process, Muchuan County was proposed as the study area, and four well-known machine learning models, namely rotation forest (RF), J48 decision tree (J48), alternating decision tree (ADTree) and random forest (RaF), and their ensembles (RF-J48, RF-ADTree and RF-RaF) were introduced to explore the mechanism. These models are established by twelve landslide conditioning factors, which are selected based to the local special geological environment conditions and previous related researches, including plan curvature, profile curvature, slope angle, slope aspect, elevation, topographic wetness index (TWI), land use, normalized difference vegetation index (NDVI), soil, lithology, distance to roads, and distance to rivers, as well as training (195 landslides) and validation (84 landslides) datasets were developed. The landslide prediction performance of the above conditioning factors was analyzed through the correlation attribute evaluation (CAE) model. Then, through the landslide susceptibility maps made by the above six different models, the Jenks natural breaks method is used to divide the landslide susceptibility into five grades, which are very low, low, moderate, high, and very high. In addition, the accuracy of the above six landslide susceptibility maps was verified by implementing the relative operating characteristic curve (ROC) and the area under the ROC (AUC). That is, the capabilities of the above six models are compared and verified in the landslide spatial prediction. Finally, the obtained results show that elevation, lithology and TWI are the three most principal landslide conditioning factors in this research. The RF-RaF and RaF models in the training dataset performed best, with the AUC value of 0.75, while the RF-ADTree model (0.74), RF-J48 model (0.74), ADTree model (0.71) and J48 model (0.70) performed poorly. Meanwhile, similar results also emerge from the validation dataset, in which the RF-RaF model acquired the best performance (0.82) and the rest are the RF-ADTree model (0.80), RaF model (0.79), RF-J48 model (0.77), ADTree model (0.76) and J48 model (0.71). Last but by no means the least, the results can provide scientific references for local natural resources departments.

scholarly journals Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling

2019 ◽  
Vol 11 (20) ◽  
pp. 5659 ◽  
Author(s):  
Soyoung Park ◽  
Se-Yeong Hamm ◽  
Jinsoo Kim
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Landslide Susceptibility ◽  
Mitigation Strategies ◽  
Slope Aspect ◽  
Kappa Index ◽  
Topographic Wetness Index ◽  
Slope Length ◽  
Rotation Forest ◽  
Conditioning Factors

This study analyzed and compared landslide susceptibility models using decision tree (DT), random forest (RF), and rotation forest (RoF) algorithms at Woomyeon Mountain, South Korea. Out of a total of 145 landslide locations, 102 locations (70%) were used for model training, and the remaining 43 locations (30%) were used for validation. Fourteen landslide conditioning factors were identified, and the contributions of each factor were evaluated using the RRelief-F algorithm with a 10-fold cross-validation approach. Three factors, timber diameter, age, and density had no contribution to landslide occurrence. Landslide susceptibility maps (LSMs) were produced using DT, RF, and RoF models with the 11 remaining landslide conditioning factors: altitude, slope, aspect, profile curvature, plan curvature, topographic position index, elevation-relief ratio, slope length and slope steepness, topographic wetness index, stream power index, and timber type. The performances of the LSMs were assessed and compared based on sensitivity, specificity, precision, accuracy, kappa index, and receiver operating characteristic curves. The results showed that the ensemble learning methods outperformed the single classifier (DT) and that the RoF model had the highest prediction capability compared to the DT and RF models. The results of this study may be helpful in managing areas vulnerable to landslides and establishing mitigation strategies.

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