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 الأصلية | English |
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الصفحات (من إلى) | 193-206 |
عدد الصفحات | 14 |
دورية | International Journal of Environmental Studies |
مستوى الصوت | 66 |
رقم الإصدار | 2 |
المعرِّفات الرقمية للأشياء | |
حالة النشر | Published - أبريل 2009 |
ASJC Scopus subject areas
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