Abstract
This paper focuses on modelling of emission inventory, pollutant dispersion by the industrial source complex short term model (ISCST), and neural network analysis of air pollution in Kuwait. A novel neural network-based scheme is suggested and applied to site-specific short-and medium-term forecasting of ozone concentrations. Two feed forward artificial neural networks (ANN) are used to improve the performance of time series predictions. Results show that this forecasting technique represents a significant improvement over the conventional ANN approach.
| Original language | English |
|---|---|
| Pages (from-to) | 193-206 |
| Number of pages | 14 |
| Journal | International Journal of Environmental Studies |
| Volume | 66 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Apr 2009 |
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Keywords
- Air pollution
- Emission inventory
- ISCST and neural network modelling
- Monitoring
- Seasonal and temporal variations
ASJC Scopus subject areas
- Waste Management and Disposal
- Pollution
- Computers in Earth Sciences
- Geography, Planning and Development
- Ecology
Cite this
Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait. / Ettouney, Reem S.; Abdul-Wahab, Sabah; Elkilani, Amal S.
In: International Journal of Environmental Studies, Vol. 66, No. 2, 04.2009, p. 193-206.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait
AU - Ettouney, Reem S.
AU - Abdul-Wahab, Sabah
AU - Elkilani, Amal S.
PY - 2009/4
Y1 - 2009/4
N2 - This paper focuses on modelling of emission inventory, pollutant dispersion by the industrial source complex short term model (ISCST), and neural network analysis of air pollution in Kuwait. A novel neural network-based scheme is suggested and applied to site-specific short-and medium-term forecasting of ozone concentrations. Two feed forward artificial neural networks (ANN) are used to improve the performance of time series predictions. Results show that this forecasting technique represents a significant improvement over the conventional ANN approach.
AB - This paper focuses on modelling of emission inventory, pollutant dispersion by the industrial source complex short term model (ISCST), and neural network analysis of air pollution in Kuwait. A novel neural network-based scheme is suggested and applied to site-specific short-and medium-term forecasting of ozone concentrations. Two feed forward artificial neural networks (ANN) are used to improve the performance of time series predictions. Results show that this forecasting technique represents a significant improvement over the conventional ANN approach.
KW - Air pollution
KW - Emission inventory
KW - ISCST and neural network modelling
KW - Monitoring
KW - Seasonal and temporal variations
UR - http://www.scopus.com/inward/record.url?scp=69149093076&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=69149093076&partnerID=8YFLogxK
U2 - 10.1080/00207230902859929
DO - 10.1080/00207230902859929
M3 - Article
VL - 66
SP - 193
EP - 206
JO - International Journal of Environmental Studies
JF - International Journal of Environmental Studies
SN - 0020-7233
IS - 2
ER -