Journal ArticleDOI
Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization
Dieu Tien Bui,Dieu Tien Bui,Tran Anh Tuan,Nhat-Duc Hoang,Nguyen Quoc Thanh,Duy Nguyen,Ngo Van Liem,Biswajeet Pradhan,Biswajeet Pradhan +8 more
TLDR
The proposed model, namely LSSVM-BC, is a promising tool for spatial prediction of landslides at the study area and is useful for landuse planning for the Lao Cai area.Abstract:
The main objective of this study is to produce a landslide susceptibility map for the Lao Cai area (Vietnam) using a new hybrid intelligent method based on least squares support vector machines (LSSVM) and artificial bee colony (ABC) optimization, namely LSSVM-BC. LSSVM and ABC are state-of-the-art soft computing techniques that have been rarely utilized in landslide susceptibility assessment. LSSVM is adopted to develop landslide prediction model whereas ABC was used to optimize the prediction model by identifying an appropriate set of the LSSVM hyper-parameters. To establish the hybrid intelligent method, a GIS database with ten landslide-influencing factors and 340 landslide locations that occurred mainly during the last 20-years was constructed. These historical landslide locations were collected from the existing inventories that sourced from (i) five landslide projects carried out in this study areas before and (ii) interpretations of SPOT satellite images with resolution of 2.5 m. The study area was geographically split into two different parts, with landslides located in the first part was used for building models whereas the other landslides in the second part was used for the model validation. Performance of the LSSVM-BC model was assessed using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Result shows that the prediction power of the model is good with the area under the curve (AUC) = 0.900. Experiments have pointed out the prediction power of the LSSVM-BC is better than that obtained from the popular support vector machines. Therefore, the proposed model is a promising tool for spatial prediction of landslides at the study area. The landslide susceptibility map is useful for landuse planning for the Lao Cai area.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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
Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS
TL;DR: Analysis of results indicates that landslide models using machine learning ensemble frameworks are promising methods which can be used as alternatives of individual base classifiers for landslide susceptibility assessment of other prone areas.
Journal ArticleDOI
Review on landslide susceptibility mapping using support vector machines
TL;DR: A review of landslide susceptibility mapping using SVM, a machine learning algorithm that uses a small number of samples for prediction and has been widely used in recent years, and its strengths and weaknesses.
Journal ArticleDOI
Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China.
TL;DR: The experimental results demonstrated that the proportions of highly susceptible zones in all of the CNN landslide susceptibility maps are highly similar and lower than 30%, which indicates that these CNNs are more practical for landslide prevention and management than conventional methods.
References
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Journal ArticleDOI
Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China
X. Yao,L.G. Tham,Fuchu Dai +2 more
TL;DR: An overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping, where it is concluded that two- class SVM possesses better prediction efficiency than logistic regression and one- Class SVM.
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Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression
TL;DR: In this article, the authors used geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) to assess the shallow landslide susceptibility of Trabzon province (NE Turkey) using lithology, slope, land cover, aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data.
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Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS
TL;DR: Results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable and the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.
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Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines
TL;DR: Wang et al. as discussed by the authors explored potential applications of two new models such as Kernel Logistic Regression (KLR) and Alternating Decision Tree (ADT) for landslide susceptibility mapping at the Yihuang area (China).
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Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models
TL;DR: Evaluated and compared the results of evidential belief functions and fuzzy logic models for spatial prediction of landslide hazards in the Hoa Binh province of Vietnam, using geographic information systems show that all the models have good prediction capabilities.