scispace - formally typeset
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

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
More filters

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

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

Yu Huang, +1 more
- 01 Jun 2018 - 
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
More filters
Journal ArticleDOI

A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area

TL;DR: In this article, the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques were compared for the spatial prediction of landslides.
Journal ArticleDOI

Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China

TL;DR: In this paper, a typical landslide study area, namely Qinggan River delta, situated in Three Gorges, China, is selected for a study and the following environmental factors are determined as independent variables of the model: elevation, elevation, normalized difference vegetation index (NDVI), slope, aspect, distance to rivers, plan curvature, and profile curvature.
Journal ArticleDOI

Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS

TL;DR: The present study indicates that RFCE is an encouraging method that can be used for spatial prediction of landslides.
Journal ArticleDOI

Predicting landslides for risk analysis — Spatial models tested by a cross-validation technique

TL;DR: In this article, the authors propose a systematic procedure comprising two analytical steps: relative hazard mapping and empirically probability estimation for estimating the probabilities of future landslides, by applying a cross-validation technique.
Journal ArticleDOI

Relationships among remotely sensed soil moisture, precipitation and landslide events

TL;DR: In this paper, a qualitative comparison among soil moisture derived from AMSR-E, precipitation from TRMM and major landslide events was conducted to establish the soil moisture and landslide relationship.
Related Papers (5)