Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

By Isik Yilmaz, O. Kaynar. Expert systems with applications, 2010

480 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/0ed9a5233897b2f3c76b0a508ee44fda23c61f86

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Citations in the corpus

Prediction of MEUF process performance using artificial neural networks and ANFIS approaches (2012, background): “The goal of ANFIS is to find a model or mapping, which associate the input values with the target values correctly [30].”
Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, background): “The main function of it is to minimize the error between the actual network outputs and the outputs of training input / output pairs [71].”
Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS (2012, methodology): “TheRMSE,R 2,andVAFcanbe evaluated,respectively,usingtheequationsasfollows: RMSE¼ð1=nÞ Xn i¼1 ðfoutiÀtiÞ 2 " #0:5 ð14Þ R 2 ¼1À Xn i¼1 ðfoutiÀtiÞ 2= Xn i¼1 fouti”
Estimation of Soil Infiltration and Cation Exchange Capacity Based on Multiple Regression, ANN (RBF, MLP), and ANFIS Models (2014, methodology): “Using multiple regression, ANN (MLP, RBF), and ANFIS models, Yilmaz and Kaynar (2011) predicted the swell potential of clay soils.”
Application of PCA, SVR, and ANFIS for modeling of rock fragmentation (2015, methodology): “Correlation coefficient, bias, VAF, and MAPE generated by the ANFIS model (respectively 0.89, 0.257, 88.19, and 10.37) were higher than referred values for the SVM model (0.83, 1.87, 75.24, and 16.25, respectively) as well as Kuz-Ram inference.”
Regional flood frequency analysis using support vector regression in arid and semi-arid regions of Iran (2018, background): “The general purpose of multiple regression (MLP) is to determine the relationship between several independent variables and a dependent variable (Yilmaz and Kaynar 2011).”
Application of ANFIS and LR in prediction of scour depth in bridges (2014, methodology): “Fuzzy inference systems (FIS) and neuro-fuzzy/fuzzy-neural systems have been used successfully for modeling in many engineering applications as well as in agricultural [26], water science [20,24], soil science [31] and stability of structures [7].”
Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS) (2019, methodology): “The artificial neural network (ANN) is a machine learning method widely used for prediction of unsaturated hydraulic conductivity (Al-Sulaiman 2015; Arshad et al. 2013; Yilmaz and Kaynar 2011).”
Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) (2018, methodology): “The main difference between RBF and MLP is that MLP is more general, while RBF is considered as more localist learning and is associated with input data (Yilmaz and Kaynar 2011).”