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A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

Biswajeet Pradhan
- 01 Feb 2013 - 
- Vol. 51, pp 350-365
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TLDR
In this paper, three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) were compared for landslide susceptibility mapping at Penang Hill area, Malaysia.
About: 
This article is published in Computers & Geosciences.The article was published on 2013-02-01 and is currently open access. It has received 870 citations till now. The article focuses on the topics: Landslide & Adaptive neuro fuzzy inference system.

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Citations
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Journal ArticleDOI

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

Dieu Tien Bui, +5 more
- 01 Apr 2016 - 
TL;DR: This study introduces a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods and demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptible mapping.
Journal ArticleDOI

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

Mahyat Shafapour Tehrany, +2 more
- 06 May 2014 - 
TL;DR: In this article, the authors proposed an ensemble weight-of-evidence (WoE) and support vector machine (SVM) model to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis.
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, +8 more
- 01 Apr 2017 - 
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

Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS

Mahyat Shafapour Tehrany, +2 more
- 11 Nov 2013 - 
TL;DR: In this article, the authors compared the performance of two different approaches such as rule-based decision tree (DT) and combination of frequency ratio (FR) and logistic regression (LR) statistical methods for flood susceptibility mapping at Kelantan, Malaysia.
Journal ArticleDOI

Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

Jason Goetz, +6 more
- 01 Aug 2015 - 
TL;DR: A comparison of traditional statistical and novel machine learning models applied for regional scale landslide susceptibility modeling is presented and it is suggested that the framework of this model evaluation approach can be applied to assist in selection of a suitable landslide susceptibility modeled technique.
References
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Book

The Nature of Statistical Learning Theory

Vladimir Vapnik
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Support-Vector Networks

Corinna Cortes, +1 more
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TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

Statistical learning theory

Vladimir Vapnik
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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C4.5: Programs for Machine Learning

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TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
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Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

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Frequently Asked Questions (1)
Q1. What are the contributions in this paper?

He purpose of the present study is to compare the prediction performances of three different approaches such as decision tree ( DT ), support vector machine ( SVM ) and adaptive neurofuzzy inference system ( ANFIS ) for landslide susceptibility mapping at Penang Hill area, Malaysia. The study area contains 340,608 pixels while total 8403 pixels include landslides. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches ( e. g., DT, SVM and ANFIS ) is viable.