Journal ArticleDOI
Landslide susceptibility assessment in Lianhua County (China); a comparison between a random forest data mining technique and bivariate and multivariate statistical models
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TLDR
Wang et al. as mentioned in this paper evaluated and compared landslide susceptibility maps produced using the random forest (RF) data mining technique with those produced by bivariate (evidential belief function and frequency ratio) and multivariate (logistic regression) statistical models for Lianhua County, China.About:
This article is published in Geomorphology.The article was published on 2016-04-15. It has received 303 citations till now. The article focuses on the topics: Landslide & Topographic Wetness Index.read more
Citations
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Journal ArticleDOI
Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)
Haoyuan Hong,Haoyuan Hong,Junzhi Liu,Junzhi Liu,Dieu Tien Bui,Biswajeet Pradhan,Biswajeet Pradhan,Tri Dev Acharya,Binh Thai Pham,A-Xing Zhu,A-Xing Zhu,Wei Chen,Baharin Bin Ahmad +12 more
TL;DR: Wang et al. as mentioned in this paper investigated and compared the use of current state-of-the-art ensemble techniques, such as AdaBoost, Bagging, and Rotation Forest, for landslide susceptibility assessment with the base classifier of J48 Decision Tree (JDT).
Journal ArticleDOI
Prediction of the landslide susceptibility: Which algorithm, which precision?
TL;DR: The first comprehensive comparison among the performances of ten advanced machine learning techniques (MLTs) including artificial neural networks (ANNs), boosted regression tree (BRT), classification and regression trees (CART), generalized linear model (GLM), generalized additive model (GAM), multivariate adaptive regression splines (MARS), naive Bayes (NB), quadratic discriminant analysis (QDA), random forest (RF), and support vector machines (SVM) is presented.
Journal ArticleDOI
Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling
TL;DR: The main aim of the present study is to explore and compare three state-of-the art data mining techniques, best-first decision tree, random forest, and naïve Bayes tree, for landslide susceptibility assessment in the Longhai area of China.
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.
Journal ArticleDOI
Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques
TL;DR: In this article, three well-known machine learning models namely maximum entropy (MaxEnt), support vector machine (SVM), and Artificial Neural Network (ANN) were used accompanied by their ensembles in Wanyuan area, China.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
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A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
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Measuring the accuracy of diagnostic systems
TL;DR: For diagnostic systems used to distinguish between two classes of events, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy.
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A caution regarding rules of thumb for variance inflation factors.
TL;DR: In this article, the authors examined the effect of the variance inflation factor (VIF) on the results of regression analyses, and found that threshold values of the VIF need to be evaluated in the context of several other factors that influence the variance of regression coefficients.