Journal ArticleDOI
GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China
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TLDR
Wang et al. as discussed by the authors used a geographic information system (GIS) for the Baozhong region of Baoji City, China to map landslide susceptibility through the AHP and CF models.Abstract:
The main purpose of this study was to map landslide susceptibility through the AHP and CF models, using a geographic information system (GIS), for the Baozhong region of Baoji City, China. At first, a landslide inventory map was prepared using technical reports, aerial photographs, and coupling with field surveys. A total of 79 landslides were mapped, out of which 55 (70 %) were randomly selected for building landslide susceptibility models, while the rest 24 landslides (30 %) were applied for validating the models. In this case study, the following landslide conditioning factors were evaluated: slope degree, slope aspect, plan curvature, altitude, geomorphology, lithology, distance from faults, distance from rivers, and precipitation. Subsequently, landslide susceptibility maps were produced using the AHP and CF models. Finally, the validation of landslide susceptibility map was accomplished with areas under the curve (AUC) and the Seed Cell Area Index (SCAI). The AUC plot estimation results indicated that the susceptibility map applying CF model has a higher prediction accuracy of 81.43 % than the accuracy of 75.97 % applying AHP model. Similarly, the validation results also showed that the success rate of the CF model was 85.93 %, while the success rate was 77.80 % for the AHP model. According to the validation results of the AUC evaluation, the map produced by CF model behaves better performance. Furthermore, the validation results using the SCAI also indicated that the CF model has a higher predication accuracy than the AHP model. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation.read more
Citations
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Journal ArticleDOI
A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility
Wei Chen,Xiaoshen Xie,Jiale Wang,Biswajeet Pradhan,Biswajeet Pradhan,Haoyuan Hong,Dieu Tien Bui,Zhao Duan,Jianquan Ma +8 more
TL;DR: In this article, the authors used three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility.
Journal ArticleDOI
Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan
Jie Dou,Ali P. Yunus,Dieu Tien Bui,Abdelaziz Merghadi,Mehebub Sahana,Zhongfan Zhu,Chi-Wen Chen,Khabat Khosravi,Yang Yang,Binh Thai Pham +9 more
TL;DR: It is suggested that both RF and DT models can be used in other similar non-eruption-related landslide studies in the tephra-deposited rich volcanoes, as they are capable of rapidly generating accurate and stable LSM maps for risk mitigation, management practices, and decision-making.
Journal ArticleDOI
Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques
TL;DR: In this article, three well-known machine learning models namely maximum entropy (MaxEnt), support vector machine (SVM), and Artificial Neural Network (ANN) were used accompanied by their ensembles in Wanyuan area, China.
Journal ArticleDOI
Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping
Yanli Wu,Yutian Ke,Zhuo Chen,Shouyun Liang,Hongliang Zhao,Haoyuan Hong,Haoyuan Hong,Haoyuan Hong +7 more
TL;DR: To map landslide susceptibility using Alternating decision tree (ADTree) as well as GIS-based new ensemble techniques involving ADTree with bootstrap aggregation (Bagging) and AD tree with adaptive boosting (AdaBoost) to select the best model, the two ensemble models proposed prohibited better performance than the ADTree model did.
Journal ArticleDOI
GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models
TL;DR: The results show that the KLR model has the highest AUC values and the highest degree of goodness-of-fits for both the training and validation datasets, respectively, and the NBTree model hasThe benefit of selecting the optimal machine learning techniques in landslide susceptibility mapping.
References
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Book ChapterDOI
The Analytic Hierarchy Process
Thomas L. Saaty,Kevin P. Kearns +1 more
TL;DR: Analytic Hierarchy Process (AHP) as mentioned in this paper is a systematic procedure for representing the elements of any problem hierarchically, which organizes the basic rationality by breaking down a problem into its smaller constituent parts and then guides decision makers through a series of pairwise comparison judgments to express the relative strength or intensity of impact of the elements in the hierarchy.
Journal ArticleDOI
Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning
TL;DR: In this article, the authors presented a study of the relationship between geotechnical engineering and geosciences and geophysics at the University of New South Wales and U.S. Geological Survey.
Journal ArticleDOI
A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
TL;DR: In this paper, three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) were compared for landslide susceptibility mapping at Penang Hill area, Malaysia.