Vehicular emissions prediction with CART-BMARS hybrid models

Abstract

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.

A comparative study of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in distribution system with nondeterministic inputs

“…The network was trained many times for effective evaluation of the accuracy of the model using MSE and R value to determine the degree of association between the predicted and expected values. The system of equation for MSE and R value is established in equations (18) and (19). Y pred and Y exp denote the predicted and experimental values obtained for the NN model, respectively.…”

“…(18) If the unit cost to D7 is in 1mf1 and unit cost to D9 is in 2mf2 and unit cost to D10 is in 3mf3 and demand is in 4mf3 then supply at D10 is out 1mf18. (19) If the unit cost to D7 is in 1mf1 and unit cost to D9 is in 2mf3 and unit cost to D10 is in 3mf1 and demand is in 4mf1 then supply at D7 is out 1mf19. (20) If the unit cost to D7 is in 1mf1 and unit cost to D9 is in 2mf3 and unit cost to D10 is in 3mf1 and demand is in 4mf2 then supply at D9 is out 1mf20.…”

“…They reported that both ANN and GEP outperformed ARIMA, and that GEP produced the best result of the three using R 2 as the measure of performance. Oduro et al 19 did a comparative analysis of the prediction of vehicular emission using boosting MARS (BMARS), classification and regression trees (CART), ANN, and a hybrid of CART-BMARS. They reported that the combined system proved superior to the models of ANN and those of each of the techniques when not hybridized.…”

“…Batch update was done to train the neurons, and the general weight update rule for the NN is where ¼ ðY exp:m À Y pred Þ when the transfer function is linear, ¼ Y pred ð1 À Y pred ÞðY exp À Y pred Þ when the transfer function is sigmoid, Y pred represents the predicted ANN value, and Y exp denotes the expected or targeted output value. According to Lotfan et al, 24 equations (18) and (19) are used to test the fitness during training…”

Prediction of engine NOx for virtual sensor using deep neural network and genetic algorithm

“…They made it possible to aware of what factors influence the NOx formation and reduction. However, understanding the relationship between the engine operating condition and emissions is not straightforward and thus the relationship has been indirectly analyzed using statistical model, control-oriented model, or Artificial Neural Networks (ANN) [20][21][22][23][24][25]. However, the relationships are still complex and input parameters used in the equations were too ideal.…”