Dynamic linear regression models are used widely in applied econometric research. Most applications employ autoregressive (AR) models, distributed lag (DL) or (ARDL) models. These however, perform poorly for data sets with unknown, complex nonlinear patterns. This paper studies and semiparametric extensions of the dynamic model explores two techniques to allow unknown forms nonlinearities funct...

This study mapped and analyzed groundwater potential using two different models, logistic regression (LR) and multivariate adaptive regression splines (MARS), and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70%) were used for ...

To model the trajectory of pandemic in Kuwait from February 24, 2020 to 28, 2021, we used two modeling procedures: Auto Regressive Integrated Moving Average (ARIMA) with structural breaks and Multivariate Adaptive Regression Splines (MARS), then mapped key breakpoints models set government-enforced interventions. The MARS model, as opposed ARIMA provides a more precise interpretation interventi...

In this study, the Multivariate Adaptive Regression Splines (MARS) model is employed to create a data-driven prediction for bearing capacity of strip footing on rock mass subjected an inclined and eccentric load. The strengths masses are based Hoek-Brown failure criterion. To develop set training data in MARS, lower upper bound finite element limit analysis (FELA) carried out obtain numerical r...

This study presents a hybrid framework to predict stability solutions of buried structures under active trapdoor conditions in natural clays with anisotropy and heterogeneity by combining physics-based data-driven modeling. Finite-element limit analysis (FELA) newly developed anisotropic undrained shear (AUS) failure criterion is used identify the underlying mechanisms as well develop numerical...

This paper presents multiple methods for predicting heavy/mediumduty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel tru...

This project attempts to predict the interest spread of a property loan based on the borrower and property related attributes. Each attribute can be regarded as a potential feature. The problem is how to predict the spread accurately based on those features. This report describes our approaches of using linear and segmented linear models as well as other clustering methods. The comparative resu...

In this paper, two different data driven models, genetic programming (GP) and multivariate adoptive regression splines (MARS), have been adopted to create the models for prediction of bridge risk score. Input parameters of bridge risks consists of safe risk rating (SRR), functional risk rating (FRR), sustainability risk rating (SUR), environmental risk rating (ERR) and target output. The total ...