Multivariate adaptive regression splines models for vehicular emission prediction

Xia

et al. 2019

Hydrological Processes

Regression-based methods are commonly used for riverine constituent concentration/flux estimation, which is essential for guiding water quality protection practices and environmental decision making. This paper developed a multivariate adaptive regression splines model for estimating riverine constituent concentrations (MARS-EC). The process, interpretability and flexibility of the MARS-EC modelling approach, was demonstrated for total nitrogen in the Patuxent River, a major river input to Chesapeake Bay. Model accuracy and uncertainty of the MARS-EC approach was further analysed using nitrate plus nitrite datasets from eight tributary rivers to Chesapeake Bay. Results showed that the MARS-EC approach integrated the advantages of both parametric and nonparametric regression methods, and model accuracy was demonstrated to be superior to the traditionally used ESTIMATOR model. MARS-EC is flexible and allows consideration of auxiliary variables; the variables and interactions can be selected automatically. MARS-EC does not constrain concentrationpredictor curves to be constant but rather is able to identify shifts in these curves from mathematical expressions and visual graphics. The MARS-EC approach provides an effective and complementary tool along with existing approaches for estimating riverine constituent concentrations. K E Y W O R D S concentration-discharge curve, concentration-season curve, pollutant flux, uncertainty analysis, water quality, watershed management

italicGCV = italicRSS / ((), N {(1 - ENP / N)}^{2}),

where RSS is the residual sum‐of‐squares measured on the training data, ENP is the effective number of parameters, and N is the number of observations.…”

Section: Methodologiesmentioning

confidence: 99%

Multivariate adaptive regression splines for estimating riverine constituent concentrations

Xia

et al. 2019

Hydrological Processes

Transportation Research Part D: Transport and Environment

“…where h(x; a) is the weak learner with basis functions {h(x, a m )} M m=1 and ρ m is the corresponding multiplier. The LS-boost algorithm [31], tuned to the problem of vehicular emissions prediction, has been described in [11], using Boosting Multivariate Adaptive Regression Splines (BMARS).…”

Section: Bmars Modellingmentioning

confidence: 99%

“…Here, we propose to incorporate a regression tree with CART modelling to the BMARS algorithm [11] for improving the performance of air pollution prediction. CART builds the regression trees for predicting continuous dependent variables in the regression model.…”

Section: Cart-bmars Hybrid Modellingmentioning

confidence: 99%

See 1 more Smart Citation

Vehicular emissions prediction with CART-BMARS hybrid models

Oduro

2016

Self Cite

Vehicular emission models play a key role in the development of reliable air quality modelling systems. To minimise uncertainties associated with these models, it is essential to match the high-resolution requirements of emission models with up-to-date information. However, these models are usually based on average trip speed, not on environmental parameters like ambient temperature, and vehicle's motion characteristics, such as speed, acceleration, load and power. This contributes to the degradation of its predictive performance. In this paper, we propose to use the non-parametric Classification and Regression Trees (CART), the Boosting Multivariate Adaptive Regression Splines (BMARS) algorithm and a combination of them in hybrid models to improve the accuracy of vehicular emission prediction using on-board measurements and the chassis dynamometer testing. The experimental comparison between the proposed CART-BMARS hybrid model with the BMARS and artificial neural networks (ANNs) algorithms demonstrates its effectiveness and efficiency in estimating vehicular emissions.

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

“…Oduro et al [17] developed a model for prediction NO X vehicular emissions using on-board measurement and chassis dynamometer testing. Misra et al [18] proposed an integrated modelling approach to estimate micro-scale urban traffic CO and NO X emissions.…”

Section: Introductionmentioning

confidence: 99%

Predicting Carbon Monoxide Emissions with Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Networks (ANNs)

Oduro

Metia

H.³

et al. 2015

Self Cite

-Emissions from motor vehicles need to be predicted fairly accurately to ensure an appropriate air quality management plan. This research work explores the use of a nonparametric regression algorithm known as the multivariate adaptive regression splines (MARS) in comparison with the artificial neural networks (ANN) for the purpose of best approximation of the relationship between the input and output from datasets recorded from on-board measurement and dynamometer testings. The performance of the models was evaluated by comparing the MARS and ANN predictions to the measured data using several performance indices. The results are evaluated in terms of accuracy, flexibility and computational efficiency. While MARS are more computationally efficient to reach the final model ANN are slightly more accurate. The proposed techniques may be used to assist in a decision-making policy regarding urban air pollution.