Assessment of rockburst risk using multivariate adaptive regression splines and deep forest model

Regression Splines ◽

Forest Model ◽

Deep Forest ◽

Adaptive Regression ◽

Estimation of modified expansive soil CBR with multivariate adaptive regression splines, random forest and gradient boosting machine

Innovative Infrastructure Solutions ◽

10.1007/s41062-021-00568-z ◽

2021 ◽

Vol 6 (4) ◽

Author(s):

Chijioke Christopher Ikeagwuani

Keyword(s):

Random Forest ◽

Expansive Soil ◽

Gradient Boosting ◽

Regression Splines ◽

Gradient Boosting Machine ◽

Adaptive Regression ◽

Prediction of transportation energy demand: Multivariate Adaptive Regression Splines

Energy ◽

10.1016/j.energy.2021.120090 ◽

2021 ◽

Vol 224 ◽

pp. 120090

Author(s):

Mohammad Ali Sahraei ◽

Hakan Duman ◽

Muhammed Yasin Çodur ◽

Ecevit Eyduran

Keyword(s):

Energy Demand ◽

Regression Splines ◽

Transportation Energy ◽

Adaptive Regression ◽

Global Optimization With Multivariate Adaptive Regression Splines

IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) ◽

10.1109/tsmcb.2006.883430 ◽

2007 ◽

Vol 37 (2) ◽

pp. 333-340 ◽

Cited By ~ 31

Author(s):

Scott Crino ◽

Donald E. Brown

Keyword(s):

Global Optimization ◽

Regression Splines ◽

Adaptive Regression ◽

Flexible and Robust Implementations of Multivariate Adaptive Regression Splines Within a Wastewater Treatment Stochastic Dynamic Program

Quality and Reliability Engineering International ◽

10.1002/qre.708 ◽

2005 ◽

Vol 21 (7) ◽

pp. 689-699 ◽

Cited By ~ 9

Author(s):

Julia C. C. Tsai ◽

Victoria C. P. Chen

Keyword(s):

Wastewater Treatment ◽

Dynamic Program ◽

Stochastic Dynamic ◽

Regression Splines ◽

Adaptive Regression ◽

Stochastic Dynamic Program ◽

Performance of Multivariate Adaptive Regression Splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data / Performances de régressions par splines multiples et adaptives (MARS) pour la prévision d'écoulement au sein de micro-bassins versants Himalayens d'altitudes intermédiaires avec peu de données

Hydrological Sciences Journal ◽

10.1623/hysj.53.6.1165 ◽

2008 ◽

Vol 53 (6) ◽

pp. 1165-1175 ◽

Cited By ~ 68

Author(s):

V. N. SHARDA ◽

S. O. PRASHER ◽

R. M. PATEL ◽

P. R. OJASVI ◽

CHANDRA PRAKASH

Keyword(s):

Regression Splines ◽

Limited Data ◽

Adaptive Regression ◽

Prediction of chloride diffusion in cement mortar using Multi-Gene Genetic Programming and Multivariate Adaptive Regression Splines

Measurement ◽

10.1016/j.measurement.2017.08.031 ◽

2017 ◽

Vol 112 ◽

pp. 141-149 ◽

Cited By ~ 21

Author(s):

Nhat-Duc Hoang ◽

Chun-Tao Chen ◽

Kuo-Wei Liao

Keyword(s):

Genetic Programming ◽

Cement Mortar ◽

Chloride Diffusion ◽

Regression Splines ◽

Adaptive Regression ◽

A Multivariate Adaptive Regression Splines model for determining horizontal wall deflection envelope for braced excavations in clays

Tunnelling and Underground Space Technology ◽

10.1016/j.tust.2018.11.046 ◽

2019 ◽

Vol 84 ◽

pp. 461-471 ◽

Cited By ~ 21

Author(s):

Wengang Zhang ◽

Runhong Zhang ◽

Wei Wang ◽

Fan Zhang ◽

Anthony Teck Chee Goh

Keyword(s):

Regression Splines ◽

Wall Deflection ◽

Braced Excavations ◽

Adaptive Regression ◽

Horizontal Wall ◽

Predicting object-oriented software maintainability using multivariate adaptive regression splines

Journal of Systems and Software ◽

10.1016/j.jss.2006.10.049 ◽

2007 ◽

Vol 80 (8) ◽

pp. 1349-1361 ◽

Cited By ~ 126

Author(s):

Yuming Zhou ◽

Hareton Leung

Keyword(s):

Object Oriented ◽

Regression Splines ◽

Software Maintainability ◽

Adaptive Regression ◽

Prediction of clearing temperatures of bent-core liquid crystals using decision trees and multivariate adaptive regression splines

Liquid Crystals ◽

10.1080/02678292.2016.1155769 ◽

2016 ◽

Vol 43 (8) ◽

pp. 1028-1037 ◽

Cited By ~ 6

Author(s):

Jelena Antanasijević ◽

Viktor Pocajt ◽

Davor Antanasijević ◽

Nemanja Trišović ◽

Katalin Fodor-Csorba

Keyword(s):

Liquid Crystals ◽

Decision Trees ◽

Regression Splines ◽

Adaptive Regression ◽

Advances in Intelligent Systems and Computing - 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) ◽

A Multivariate Approach to Time Series Forecasting of Copper Prices with the Help of Multiple Imputation by Chained Equations and Multivariate Adaptive Regression Splines

10.1007/978-3-030-57802-2_66 ◽

2020 ◽

pp. 691-701

Author(s):

Fernando Sánchez Lasheras ◽

Javier Gracia Rodríguez ◽

Paulino José García Nieto ◽

Esperanza García-Gonzalo ◽

Gregorio Fidalgo Valverde

Keyword(s):

Time Series ◽

Multiple Imputation ◽

Time Series Forecasting ◽

Regression Splines ◽

Multivariate Approach ◽

Adaptive Regression ◽