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…

By D. Tien Bui, T. Tuan, Harald Klempe, Biswajeet Pradhan, Inge Revhaug. Landslides, 2016

1,161 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/216b7f26e3ecd1bad785c03d541a7480c7757e17

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Citations in the corpus

Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China (2016, methodology): “With the purpose to search an optimal separating hyperplane, the theory changed original import space into a dimensional feature space (Vapnick 1998; Xu et al. 2012; Bui et al. 2015).”
A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020, methodology): “For simple regression problems, an ANN sys-tem with only one hidden layer can provide the outcome predictions with the acceptable [81].”
Landslide susceptibility evaluation and hazard zonation techniques – a review (2020, methodology): “Support vector machines (SVM) This method is used to solve complex classification and regression problems (Tien Bui et al. 2015; Kavzoglu et al. 2013; Ballabio and Sterlacchini 2012; Kavzoglu and Colkesen 2009; Brenning 2005).”
A novel hybrid artificial intelligence approach for flood susceptibility assessment (2017, methodology): “Flood conditioning factors Factors, that affect flood susceptibility mapping, may vary, depending on the watershed characteristics (Bui et al., 2016a) and determination of these factors is vital for flood modeling (Sanyal and Lu, 2004).”
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed (2019, methodology): “ML algorithms that have been used for landslide susceptibility mapping include support vector machine [17,21,47–49], artificial neural network [50,51], decision trees such as naïve Bayes tree (NBT) [5,52], radial basis function (RBF) [53], kernel logistic regression (KLR) [54,55], Bayes’ net (BN) [5”
A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping (2017, neutral): “…& Buchroithner, 2010), decision tree model (Pradhan, 2013; Saito, Nakayama, & Matsuyama, 2009; Tsangaratos & Ilia, 2015), support vector machine (Bui et al., 2015; POURGHASEMI, JIRANDEH, PRADHAN, XU, & GOKCEOGLU, 2013; Yao, Tham, & Dai, 2008), bivariate statistics (Nandi & Shakoor, 2010;…”
Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China (2019, methodology): “The statistical model is a kind of method based on statistical analysis of existing landslides, and then predicts future landslide risk (Bui et al. 2016).”