This paper introduces a novel hybrid approach, combining machine learning algorithms with feature selection, for efficient modelling and forecasting of complex phenomenon governed by multifactorial nonlinear behaviours, such as crop yield. We have attempted to harness the benefits soft computing algorithm multivariate adaptive regression spline (MARS) selection coupled support vector (SVR) arti...

This article describes a new non-parametric regression method that extends additive regression techniques to allow modeling of interactions among predictor variables. The proposed models consist of sums of smooth functions of one or more predictor variables. Each term involving more than one predictor is assumed to be a composition of bivariate functions of simpler terms in the model. The metho...

Rutting is one of the major distresses in the flexible pavements, which is heavily influenced by the asphalt mixtures properties at high temperatures. There are several methods for the characterization of the rutting resistance of asphalt mixtures. Flow number is one of the most important parameters that can be used for the evaluation of rutting. The flow number is measured by the dynamic creep...

Fiber-Reinforced Polymers (FRP) were developed as a new method over the past decades due to their many beneficial mechanical properties, and they are commonly applied strengthen masonry structures. In this paper, Artificial Neural Network (ANN), K-fold Cross-Validation (KFCV) technique, Multivariate Adaptive Regression Spline (MARS) method, M5 Model Tree (M5MT) utilized predict ultimate strengt...

G/SPLINES ate a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least-squares computations, and allows significantly larger problems to be considered.

There has been an increasing interest in applying machine learning methods in urban energy assessment. This research implemented six statistical learning methods in estimating domestic gas and electricity using both physical and socio-economic explanatory variables in London. The input variables include dwelling types, household tenure, household composition, council tax band, population age gr...