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 |
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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
- Geography, Planning and Development
- Ecology
- Waste Management and Disposal
- Pollution
- Computers in Earth Sciences