Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus

Abstract

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO 2 ), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO 2 ), 0.3-0.4 µm sized particle numbers, 0.4-0.5 µm sized particle numbers, particulate matter (PM) concentrations less than 1.0 µm (PM 1.0 ), and PM concentrations less than 2.5 µm (PM 2.5 ) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH.First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based backpropagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks.Implications: The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.

Univariate time series based back propagation neural network modeling of air quality inside a public transportation bus using available software

“…The BPNN TS database were normalized within a range of [0,1] using Eq. to avoid the overflow of network due to large or small weights produced for the dataset and to eliminate the influence of dimensions of data on the network . where,…”

Vector‐time‐series‐based back propagation neural network modeling of air quality inside a public transportation bus using available software

“…The BPNN VTS database were normalized within a range of (0,1) using Eq. to avoid the overflow of network due to large or small weights produced for the dataset and to eliminate the influence of dimensions of data on the network . where,…”

Multivariate time series based back propagation neural network modeling of air quality inside a public transportation bus using available software

“…In this study, the input parameters for CO 2 and CO for BPNN TS models are five and three, respectively, as can be seen from Table 4 independent exogeneous variables. 14 to avoid the overflow of network due to large or small weights produced for the dataset and to eliminate the influence of dimensions of data on the network [13,25]. There would be only one output neuron for both CO 2 and CO BPNN MTS models.…”

“…The selection of optimal number of neurons in the hidden layersof CO 2 and CO BPNN MTS models were based on the minimum values of MAE and the RMSE statistics for varying number of neurons [13,25]. Table 5 presents a summary of the MAE and RMSE statistics for different number of neurons Table 5, the authors used seven and six neurons in the single hidden layer of CO 2 and CO BPNN MTS models, respectively.…”

“…Applications of ANNs include and are not limited to robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing, and social/psychological sciences. Kadiyala and Kumar [24,25] developed advanced artificial intelligence models by using the identified statistically significant variables obtained from the use of ANOVA as a complimentary sensitivity analysis to the regression tree results. Kadiyala and Kumar [24,25] developed advanced artificial intelligence models by using the identified statistically significant variables obtained from the use of ANOVA as a complimentary sensitivity analysis to the regression tree results.…”