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

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

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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 languageEnglish
Pages (from-to)193-206
Number of pages14
JournalInternational Journal of Environmental Studies
Issue number2
Publication statusPublished - Apr 2009


  • 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


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