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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Journal ArticleDOI
From landslide susceptibility to landslide frequency: A territory-wide study in Hong Kong
Florence W.Y. Ko,Frankie L.C. Lo +1 more
TL;DR: In this article, the authors developed a landslide susceptibility model to predict the number of natural terrain landslides that may occur in an anticipated rainfall event and transformed the storm-based landslide density to the annual landslide frequency to compile the territorywide landslide frequency map by incorporating the mean annual frequency of occurrence of different probable rainfall scenarios.
Journal ArticleDOI
GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam
Viet-Tien Nguyen,Trong Hien Tran,Ngoc Anh Ha,Van Liem Ngo,Al-Ansari Nadhir,Van Phong Tran,Huu Duy Nguyen,Malek M. A.,Ata Amini,Indra Prakash,Lanh Si Ho,Binh Thai Pham +11 more
TL;DR: In this article, the authors developed GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam and developed novel hybrid models of alternating decision trees with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, respectively.
Proceedings ArticleDOI
Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms
Husam Abdulrasool H. Al-Najjar,Bahareh Kalantar,Biswajeet Pradhan,Biswajeet Pradhan,Vahideh Saeidi +4 more
TL;DR: This study investigates the effectiveness of four sets of landslide conditioning variable(s) by analyzing and determining the most important factors using variance-inflated factor (VIF), Pearson’s correlation and Chi-square techniques.
Journal ArticleDOI
Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment
TL;DR: In this article, the authors compared the performance of Logistic Model Tree (LMT), Random Forest (RF) and Naive Bayes T (NBT) for landslide mitigation.
Journal ArticleDOI
Deep-seated rainfall-induced landslides on a new expressway: a case study in Vietnam
TL;DR: In this article, the authors examined the causative factors, failure mechanisms, and characteristics of the landslides through detailed geological investigation, unmanned aerial vehicle (UAV) surveys, and analysis of data from geology, geomorphology, well-prepared documents of rainfall events, and the expressway project.
References
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