Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait

Reem S. Ettouney; Sabah Abdul-Wahab; Amal S. Elkilani

doi:10.1080/00207230902859929

Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait

Reem S. Ettouney, Sabah Abdul-Wahab, Amal S. Elkilani

Mechanical and Industrial Engineering

Research output: Contribution to journal › Article

10 Citations (Scopus)

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	https://doi.org/10.1080/00207230902859929
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

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

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Access to Document

10.1080/00207230902859929