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 |
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
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Ettouney, R. S., Abdul-Wahab, S., & Elkilani, A. S. (2009). Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait. International Journal of Environmental Studies, 66(2), 193-206. https://doi.org/10.1080/00207230902859929