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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Forest Fire Susceptibility Zonation in Eastern India Using Statistical and Weighted Modelling Approaches
TL;DR: In this article , the authors implemented multiple statistical and weighted modelling approaches to identify forest fire susceptibility zones in Eastern India using satellite imageries, GIS technique, and modelling approaches is highly recommended.
Journal ArticleDOI
Analyzing Risk of Service Failures in Heavy Haul Rail Lines: A Hybrid Approach for Imbalanced Data.
TL;DR: In this paper, an analysis of the factors that influence the risk of a service failure is conducted and quantitative models are developed to predict locations where service failures are most likely to occur until the next inspection.
Journal ArticleDOI
An approach based on socio-politically optimized neural computing network for predicting shallow landslide susceptibility at tropical areas
Viet-Ha Nhu,Nhat-Duc Hoang,Mahdis Amiri,Tinh Thanh Bui,Phuong Thao Thi Ngo,Pham Viet Hoa,Pijush Samui,Long Nguyen Thanh,Tu Pham Quang,Dieu Tien Bui +9 more
TL;DR: In this article, a new hybrid model approach based on Imperialist Competitive Algorithm, a socio-politically optimization, and neural computing networks (ICA-NeuralNet) was developed and proposed in this study with the aim is to improve the quality of the shallow landslide susceptibility assessment at the Ha Long city area, Quang Ninh province.
Mapeamento da suscetibilidade a deslizamentos com Redes Neurais Artificiais a partir do módulo gratuito e de código aberto r.landslide
TL;DR: In this article, a mapa obtido a partir do modulo foi comparado with o mapa de suscetibilidade elaborado pelo Servico Geologico do Brasil (CPRM).
Journal ArticleDOI
Comparison of LiDAR- and UAV-derived data for landslide susceptibility mapping using Random Forest algorithm
Felicia França Pereira,Tatiana Sussel Gonçalves Mendes,Silvio Jorge Coelho Simões,Márcio Roberto Magalhães de Andrade,Mário Luiz Lopes Reiss,Jennifer Fortes Cavalcante Renk,Tatiany Correia da Silva Santos +6 more
References
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