Air dispersion model and neural network: A new perspective for integrated models in the simulation of complex situations

Pelliccioni, Armando; Tirabassi, T.

doi:10.1016/j.envsoft.2004.07.015

Cited by 46 publications

(19 citation statements)

References 15 publications

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“…Similar work was done in Santiago, Chile [14], and in Perugia, Italy [15]. Furthermore, Grivas et al [16] used neural networks to predict PM10 hourly concentrations in the metropolitan area of Athens, comparing their performance with a multivariate regression model, whereas Pelliccioni et al [17] showed that the integrated use of dispersion models and neural networks can improve the prediction performance of models. Galatioto [18] analyzed the importance of traffic parameters in the urban parts of Palermo, and the authors concluded that, after a sensitivity analysis, the most correlated traffic parameter to emission concentration was queue length.…”

Section: Introductionmentioning

confidence: 86%

Air quality estimation by computational intelligence methodologies

et al. 2012

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The subject of this study is to compare different computational intelligence methodologies based on artificial neural networks used for forecasting an air quality parameter - the emission of CO2, in the city of Niš. Firstly, inputs of the CO2 emission estimator are analyzed and their measurement is explained. It is known that the traffic is the single largest emitter of CO2 in Europe. Therefore, a proper treatment of this component of pollution is very important for precise estimation of emission levels. With this in mind, measurements of traffic frequency and CO2 concentration were carried out at critical intersections in the city, as well as the monitoring of a vehicle direction at the crossroad. Finally, based on experimental data, different soft computing estimators were developed, such as feed forward neural network, recurrent neural network, and hybrid neuro-fuzzy estimator of CO2 emission levels. Test data for some characteristic cases presented at the end of the paper shows good agreement of developed estimator outputs with experimental data. Presented results are a true indicator of the implemented method usability. [Projekat Ministarstva nauke Republike Srbije, br. III42008-2/2011: Evaluation of Energy Performances and br. TR35016/2011: Indoor Environment Quality of Educational Buildings in Serbia with Impact to Health and Research of MHD Flows around the Bodies, in the Tip Clearances and Channels and Application in the MHD Pumps Development

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Section: Introductionmentioning

confidence: 86%

Air quality estimation by computational intelligence methodologies

et al. 2012

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“…Similar work was done in Santiago, Chile [13], and in Perugia, Italy [15]. Furthermore, Grivas and Chaloulakou [6] used the neural networks to predict PM 10 hourly concentrations in the metropolitan area of Athens comparing their performance with a multivariate regression model, whereas Pelliccioni and Tirabassi [12] showed that the integrated use of dispersion models and neural networks can improve the prediction performance of models.…”

Section: Introductionmentioning

confidence: 88%

Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks

Galatioto

Zito

2007

Environ Model Assess

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The analysis aims to evaluate which is the most important among traffic parameters (flows, queues length, occupancy degree, and travel time) to forecast CO and C 6 H 6 concentrations. The study area was identified by Notarbartolo Road and bounded by Libertà Street and Sciuti Street in the urban area of Palermo in Southern Italy. In this area, various loop detectors and one pollutionmonitoring site were located. Traffic data related to the pollution-monitoring site immediately near the road link were estimated by Simulation of Urban MObility (SUMO) traffic microsimulator software using as input the flows measured by loop detectors on other links of road network. Traffic and weather data were used as input variables to predict pollutant concentrations by using neural networks. Finally, after a sensitivity analysis, it was showed that queues length were the mostly correlated traffic parameters to pollutant concentrations.

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“…The back-propagation training algorithm uses this procedure to attempt to locate an absolute (or global) minimum on the error surface. A learning algorithm is an adaptive method by which the neural network's weights self-organize to reproduce the desired model (Pellicioni and Tirabassi 2006). The weights in the network are initially set to small, random values.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

An empirical analysis of the impact of a bus rapid transit system on the concentration of secondary pollutants in the roadside areas of the TransJakarta corridors

Nugroho

Fujiwara

Zhang

2011

Stoch Environ Res Risk Assess

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A bus rapid transit (BRT) system began operation in Jakarta City, Indonesia, in January 2004 and led to a modal shift from private to public modes of transport. This modal shift from car and motorcycle to BRT reduced the emission intensity of primary pollutants, such as NO x and CO. We applied a combined structural equation model and an artificial neural network to evaluate the impact of the BRT system on the concentration of secondary pollutants in the roadside areas in the BRT corridors. An empirical analysis was carried out using data collected at five continuous ambient air quality monitoring stations located near to the BRT TransJakarta corridors in 2005. The establishment of our structural equation model gives a better understanding of the cause-effect relationship among the factors influencing roadside ambient air pollution, and was useful in simplifying the complexity of our artificial neural network model for predicting the modal shift's impact on the PM 10 values and concentration of O 3 . The introduction of the BRT system, and the modal shift it produced, had a greater influence on rapidly decaying pollutants, such as PM 10 , than on O 3 because of the exposure to near-source microenvironments, such as the roadside of the TransJakarta corridors.

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Air dispersion model and neural network: A new perspective for integrated models in the simulation of complex situations

Cited by 46 publications

References 15 publications

Air quality estimation by computational intelligence methodologies

Air quality estimation by computational intelligence methodologies

Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks

An empirical analysis of the impact of a bus rapid transit system on the concentration of secondary pollutants in the roadside areas of the TransJakarta corridors

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