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
More filters
Journal ArticleDOI
Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification
TL;DR: The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics, and can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial Neural network (78%).
Journal ArticleDOI
An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping
Israr Ullah,Bilal Aslam,Syed Hassan Iqbal Ahmad Shah,Aqil Tariq,Shujing Qin,Muhammad Nauman Majeed,Hans-Balder Havenith +6 more
TL;DR: In this article , three Machine Learning (ML) techniques, namely Linear Regression, Logistic Regression (LoR), and Support Vector Machine (SVM), were used to map the landslide susceptibility over these regions in Pakistan by using three machine learning techniques.
Book ChapterDOI
A Novel Hybrid Model of Rotation Forest Based Functional Trees for Landslide Susceptibility Mapping: A Case Study at Kon Tum Province, Vietnam
Binh Thai Pham,Viet-Tien Nguyen,Van-Liem Ngo,Phan Trong Trinh,Huong Thanh Thi Ngo,Dieu Tien Bui +5 more
TL;DR: In this article, the authors proposed a novel hybrid model named Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state-of-the-art machine learning techniques of functional trees (FT) classifier and Rotation forest (RF) ensemble for landslide susceptibility mapping at the Kon Tum Province, Viet Nam.
Journal ArticleDOI
Estimating construction duration of diaphragm wall using firefly-tuned least squares support vector machine
Min-Yuan Cheng,Nhat-Duc Hoang +1 more
TL;DR: The FLSVM model can deliver accurate forecasts since it has achieved a comparatively low prediction deviation which is <10%.
Journal ArticleDOI
Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides
Stefan Abela,Sharon Anderson +1 more
TL;DR: In this paper , the authors developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorithm (WOA), for spatially explicit prediction of landslide susceptibility.
References
More filters
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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
Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
Journal ArticleDOI
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm that is used for optimizing multivariable functions and the results showed that ABC outperforms the other algorithms.
Book
Global Sensitivity Analysis: The Primer
TL;DR: In this article, the authors present a method for setting up Uncertainty and Sensitivity Analyses using Monte Carlo and Linear Regression (MCF) models and a set of experiments.