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

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

نتاج البحث: المساهمة في مجلةArticleمراجعة النظراء

12 اقتباسات (Scopus)

ملخص

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.

اللغة الأصليةEnglish
الصفحات (من إلى)193-206
عدد الصفحات14
دوريةInternational Journal of Environmental Studies
مستوى الصوت66
رقم الإصدار2
المعرِّفات الرقمية للأشياء
حالة النشرPublished - أبريل 2009

ASJC Scopus subject areas

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