Reference List for Machine Learning and its Applications in Geotechnical Engineering - PART IV: PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS USING THE SAME DATASET

1 Performance comparison of ML algorithms
1.1 Landslide

[1] Yao, X., Tham, L. G., and Dai, F. C. (2008). Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology, 101(4), 572-582. (One-class support vector machine, two-class support vector machine)
[2] Marjanovic, M., Bajat, B., and Kovacevic, M. (2009). Landslide susceptibility assessment with machine learning algorithms. In Intelligent Networking and Collaborative Systems, 2009. INCOS'09. International Conference on (pp. 273-278). IEEE. (Support vector machine with Gaussian kernel and k-Nearest Neighbor algorithms)
[3] Marjanovi, M., Kovaevi, M., Bajat, B., and Voenílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology, 123(3), 225-234. (Support vector machine, Decision tress, Logistic regression)
[4] Yilmaz, I. (2010). Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences, 61(4), 821-836. (Conditional probability, logistic regression, artificial neural networks, and support vector machine)
[5] Tien Bui, D., Pradhan, B., Lofman, O., & Revhaug, I. (2012). Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models. Mathematical Problems in Engineering, 20(5), 705718. (Support vector machines, Decision tree, and Naive Bayes models) 
[6] Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers and Geosciences, 51, 350-365. (Decision tree, Support vector marchine and adaptive neuro-fuzzy inference system (ANFIS))
[7] Goetz, J. N., A. Brenning, H. Petschko, and P. Leopold. (2015). Evaluating Machine Learning and Statistical Prediction Techniques for Landslide Susceptibility Modeling. Computers and Geosciences, 81: 111. (Generalized logistic regression, generalized additive models, weights of evidence, support vector machine, random forest classification, and bootstrap aggregated classification trees (bundling) with penalized discriminant analysis)
1.2 Liquefaction
[8] Samui, P., and Sitharam, T. G. (2011). Machine learning modelling for predicting soil liquefaction susceptibility. Natural Hazards and Earth System Sciences, 11(1), 1-9. (Artificial neural network, Support vector machine)