Multiple-input-multiple-output general regression neural networks model for the simultaneous estimation of traffic-related air pollutant emissions
Само за регистроване кориснике
2018
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Traffic-related air pollutant emissions have become a global environmental problem, especially in urban areas. The estimation of pollutant emissions is based on complex models that require the use of detailed travel-activity data, which is often unavailable and in particular, in developing countries. In order to overcome this issue, an alternative multiple-input-multiple-output general regression neural network model, based on basic socioeconomic and transport related indicators, is proposed for the simultaneous prediction of sulphur oxides (SOx), nitrogen oxides (NOx), ammonia (NH3 ), non-methane volatile organic compounds (NMVOC) and particulate matter emissions at the national level. The best model, created using only six inputs, has MAPE (mean absolute percentage error) values on testing in the range of 12-15% for all studied pollutants, except NMVOC (MAPE = 21%). The obtained predictions for SOx, NH3 and PM10 emissions were in good agreement with the reported emissions (R-2 gt = ...0.93), while the predictions for NOx and NMVOC are somewhat less accurate (R-2 approximate to 0.85). It can be concluded that the presented ANN approach can offer a simple and relatively accurate alternative method for the estimation of traffic-related air pollutant emissions.
Кључне речи:
ANN / MIMO modeling / Traffic emission / Outliers / Air pollutantsИзвор:
Atmospheric Pollution Research, 2018, 9, 2, 388-397Издавач:
- Turkish Natl Committee Air Pollution Res & Control-Tuncap, Buca
Финансирање / пројекти:
- Развој и примена метода и материјала за мониторинг нових загађујућих и токсичних органских материја и тешких метала (RS-MESTD-Basic Research (BR or ON)-172007)
DOI: 10.1016/j.apr.2017.10.011
ISSN: 1309-1042
WoS: 000429181300020
Scopus: 2-s2.0-85034969889
Институција/група
Tehnološko-metalurški fakultetTY - JOUR AU - Antanasijević, Davor AU - Pocajt, Viktor AU - Perić-Grujić, Aleksandra AU - Ristić, Mirjana PY - 2018 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4024 AB - Traffic-related air pollutant emissions have become a global environmental problem, especially in urban areas. The estimation of pollutant emissions is based on complex models that require the use of detailed travel-activity data, which is often unavailable and in particular, in developing countries. In order to overcome this issue, an alternative multiple-input-multiple-output general regression neural network model, based on basic socioeconomic and transport related indicators, is proposed for the simultaneous prediction of sulphur oxides (SOx), nitrogen oxides (NOx), ammonia (NH3 ), non-methane volatile organic compounds (NMVOC) and particulate matter emissions at the national level. The best model, created using only six inputs, has MAPE (mean absolute percentage error) values on testing in the range of 12-15% for all studied pollutants, except NMVOC (MAPE = 21%). The obtained predictions for SOx, NH3 and PM10 emissions were in good agreement with the reported emissions (R-2 gt = 0.93), while the predictions for NOx and NMVOC are somewhat less accurate (R-2 approximate to 0.85). It can be concluded that the presented ANN approach can offer a simple and relatively accurate alternative method for the estimation of traffic-related air pollutant emissions. PB - Turkish Natl Committee Air Pollution Res & Control-Tuncap, Buca T2 - Atmospheric Pollution Research T1 - Multiple-input-multiple-output general regression neural networks model for the simultaneous estimation of traffic-related air pollutant emissions EP - 397 IS - 2 SP - 388 VL - 9 DO - 10.1016/j.apr.2017.10.011 ER -
@article{ author = "Antanasijević, Davor and Pocajt, Viktor and Perić-Grujić, Aleksandra and Ristić, Mirjana", year = "2018", abstract = "Traffic-related air pollutant emissions have become a global environmental problem, especially in urban areas. The estimation of pollutant emissions is based on complex models that require the use of detailed travel-activity data, which is often unavailable and in particular, in developing countries. In order to overcome this issue, an alternative multiple-input-multiple-output general regression neural network model, based on basic socioeconomic and transport related indicators, is proposed for the simultaneous prediction of sulphur oxides (SOx), nitrogen oxides (NOx), ammonia (NH3 ), non-methane volatile organic compounds (NMVOC) and particulate matter emissions at the national level. The best model, created using only six inputs, has MAPE (mean absolute percentage error) values on testing in the range of 12-15% for all studied pollutants, except NMVOC (MAPE = 21%). The obtained predictions for SOx, NH3 and PM10 emissions were in good agreement with the reported emissions (R-2 gt = 0.93), while the predictions for NOx and NMVOC are somewhat less accurate (R-2 approximate to 0.85). It can be concluded that the presented ANN approach can offer a simple and relatively accurate alternative method for the estimation of traffic-related air pollutant emissions.", publisher = "Turkish Natl Committee Air Pollution Res & Control-Tuncap, Buca", journal = "Atmospheric Pollution Research", title = "Multiple-input-multiple-output general regression neural networks model for the simultaneous estimation of traffic-related air pollutant emissions", pages = "397-388", number = "2", volume = "9", doi = "10.1016/j.apr.2017.10.011" }
Antanasijević, D., Pocajt, V., Perić-Grujić, A.,& Ristić, M.. (2018). Multiple-input-multiple-output general regression neural networks model for the simultaneous estimation of traffic-related air pollutant emissions. in Atmospheric Pollution Research Turkish Natl Committee Air Pollution Res & Control-Tuncap, Buca., 9(2), 388-397. https://doi.org/10.1016/j.apr.2017.10.011
Antanasijević D, Pocajt V, Perić-Grujić A, Ristić M. Multiple-input-multiple-output general regression neural networks model for the simultaneous estimation of traffic-related air pollutant emissions. in Atmospheric Pollution Research. 2018;9(2):388-397. doi:10.1016/j.apr.2017.10.011 .
Antanasijević, Davor, Pocajt, Viktor, Perić-Grujić, Aleksandra, Ristić, Mirjana, "Multiple-input-multiple-output general regression neural networks model for the simultaneous estimation of traffic-related air pollutant emissions" in Atmospheric Pollution Research, 9, no. 2 (2018):388-397, https://doi.org/10.1016/j.apr.2017.10.011 . .