Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions

Nagendra, S.M. Shiva; Khare, Mukesh

doi:10.1016/j.ecolmodel.2005.01.062

Cited by 97 publications

(51 citation statements)

References 42 publications

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“…The network outputs are then compared with the target output, and their difference is taken as the network error, which will then be propagated backward through the network, to update various weights within the network. The iteration continues until the minimum error is obtained using the gradient descent technique [33,34]. In this study, the ANN training was implemented using nnet [35] and caret [27] packages of the R software for statistical analysis [28].…”

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

Suleiman

Tight

Quinn

2016

Environ Model Assess

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This paper examines the application of artificial neural network (ANN) and boosted regression tree (BRT) methods in air quality modelling. The methods were applied to developing air quality models for predicting roadside particle mass concentration (PM 10 , PM 2.5 ) and particle number counts (PNC) based on air pollution, traffic and meteorological data from Marylebone Road in London. Elastic net, Lasso and principal components analysis were used as feature selection methods for the ANN models to reduce the number of predictor variables and improve their generalisation. The performance of the ANN with feature selection (ANN hybrid) and the BRT models was evaluated and compared using statistical performance metrics. The performance parameters include root mean square error (RMSE), fraction of prediction within a factor of two of the observation (FAC2), mean bias (MB), mean gross error (MGE), the coefficient of correlation (R) and coefficient of efficiency (CoE) values. The input variables selected by the elastic net produced the best performing ANN models. The ANN hybrid produced models performed only slightly better than the BRT models. The R values of the ANN elastic net and BRT models were 0.96 and 0.95 for PM 10 , 0.96 and 0.96 for PM 2.5 and 0.89 and 0.87 for PNC, respectively. Their corresponding CoE values were 0.72 and 0.70 for PM 10 , 0.74 and 0.76 for PM 2.5 and 0.81 and 0.71 for PNC respectively. About 80-99% of all the model predictions are within a factor of two of the observed particle concentrations. The BRT models offer more advantages regarding model interpretation and permit feature selection. Therefore, the study recommends the use of BRT over ANN where the model interpretation is a priority.

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Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

Suleiman

Tight

Quinn

2016

Environ Model Assess

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“…In the recent past, the ANN has been applied to model large data with large dimensionality (i.e. Gevrey et al, 2003;Nagendra and Khare, 2006). Most of the ANN studies spoke to the problem allied with pattern recognition, forecasting and comparison of the neural network with other traditional approaches in ecological and atmospheric sciences.…”

Section: Methodsmentioning

confidence: 99%

“…Most of the ANN studies spoke to the problem allied with pattern recognition, forecasting and comparison of the neural network with other traditional approaches in ecological and atmospheric sciences. However, the step-by-step procedure involved in development of ANN-based models is less discussed (Nagendra and Khare, 2006). This paper develops ANN model step-by-step to predict the average rainfall over India during summer-monsoon by exploring the data available at the website http://www.tropmet.res.in published by Indian Institute of Tropical Meteorology.…”

Section: Methodsmentioning

confidence: 99%

Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

Chattopadhyay¹

2007

Acta Geophys.

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In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the best predictive model.

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“…The fuel parameters affecting most significantly the emissions from diesel engines are density and backend volatility. Nagendra [12] described a methodology consisting of step-by-step procedures involved in the development of ANN-based vehicular fuel consumption and exhaust emissions (VEE) model for urban roadways. The model was used to predict CO and NOx emissions for the purpose of air quality prediction at a traffic intersection and arterial road.…”

Section: Previous Studiesmentioning

confidence: 99%

‘ENPROD’ MODEL – estimating the energy impact of the use of heavy duty construction equipment by using productivity rate

Hajji¹,

Muladi²,

Larasati³

2016

AIP Conference Proceedings

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The effect of the electric fields on crystalline structure and functional group of mendong fiber (Fimbristylis globulosa) AIP Conference Proceedings 1778, 030007 (2016) Abstract. Heavy duty diesel (HDD) construction equipment plays an important role in building and rebuilding Indonesia's infrastructure and has a significant impact on national energy consumption in the form of diesel fuel. This paper demonstrates an 'energy-productivity' (ENPROD) model, a practice-ready methodology in developing an energy estimating model for HDD equipment that establishes a baseline of current fuel use based on the equipment's productivity rates. The model was formulated by using the productivity model to obtain the total duration of work, and combined with the NONROAD's brake specific fuel consumption (BSFC) to gain the fuel consumption rate in term of liter per horsepower-hour (liter/hp-hr). ENPROD model is good to be used as a benchmark for estimating fuel use from some certain types of construction equipment performing earthwork activities. Based on the methodology presented in this paper, the results revealed several trends related to total fuel use of the equipment.

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Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions

Cited by 97 publications

References 42 publications

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

‘ENPROD’ MODEL – estimating the energy impact of the use of heavy duty construction equipment by using productivity rate

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