The design and construction of underground structures are significantly affected by the distribution geological formations. Prediction interfaces using limited data has been a difficult task. A multivariate adaptive regression spline (MARS) method capable modeling nonlinearities automatically was used in this study to spatially predict elevations interfaces. Borehole from two sites Singapore we...

In this study, the viability of radial M5 model tree (RM5Tree) is investigated in prediction and estimation daily streamflow a cold climate. The RM5Tree compared with (M5Tree), artificial neural networks (ANN), basis function (RBFNN), multivariate adaptive regression spline (MARS) using data two stations from Sweden. accuracy methods assessed based on root mean square errors (RMSE), absolute (M...

In the present study, the multivariate adaptive regression splines (MARS) technique is employed to estimate the drying shrinkage of concrete. To this purpose, a very big database (RILEM Data Bank) from different experimental studies is used. Several effective parameters such as the age of onset of shrinkage measurement, age at start of drying, the ratio of the volume of the sample on its drying...

Monitoring the water contaminants is of utmost importance in resource management. Prediction total dissolved solid (TDS) particularly essential for quality management and planning areas exposed to a mixture pollutants. TDS primarily includes inorganic minerals organic matters, various salts increasing concentration causes esthetic problems. The reflection pollutant burden aquatic system can rem...

INTRODUCTION Statistical models for assessing risk of type 2 diabetes are usually additive with linear terms that use non-nationally representative data. The objective of this study was to use nationally representative data on diabetes risk factors and spline regression models to determine the ability of models with nonlinear and interaction terms to assess the risk of type 2 diabetes. METHOD...

In this paper, we have tried to predict earthquake events in a cluster of seismic data on pacific ring of fire, using multivariate adaptive regression splines (MARS). The model is employed as either a predictor for a sequence prediction task, or a binary classifier for a sequence recognition problem, which could alternatively help to predict an event. Here, we explain that sequence prediction/r...

This study adopts Multivariate Adaptive Regression Spline (MARS) and Least Square Support Vector Machine (LSSVM) for prediction of undrained shear strength (su ) of clay, based Cone Penetration Test (CPT) data. Corrected cone resistance (qt ), vertical total stress (σv ), hydrostatic pore pressure (u0 ), pore water pressure at the cone tip (u1 ), and pore water pressure just above the cone base...