توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Hydrological Assessment of Environmental Flow of Rivers
اطلاعات انتشار: کنفرانس بین المللی توسعه پایدار، راهکارها و چالش ها با محوریت کشاورزی ، منابع طبیعی، محیط زیست و گردشگری، سال
تعداد صفحات: ۱۲
Freshwater resources are assumed as one of the vital needs of all the living creatures. As times go on the human needs of water for his activities rise up. The human and ecosystems’ need to water caused a growing competition for water between them. Due to this fact, a requirement to develop methods of assessment of environmental flow of river arose. These methods are categorized to some groups that one of the most important and widely applicable categories is allocated to hydrological methods. The hydrological methods are based on river and fish characteristics and river’s flow and water level that maintain the river in a favorable condition. One of these methods is Tennent method that has been used widely all over the world. Considering this method and the condition of ecosystems caused some changes in that method. This has resulted in providing new methods such as Tessman method, Arkansas, flow duration curves (FDC), aquatic base flow, percentage of flow (POF) method, sustainability boundary approach (SBA), Smakhtin and Hoppe method that is brought in this study.<\div>

۲Short–term prediction of atmospheric concentrations of ground–level ozone in Karaj using artificial neural network
اطلاعات انتشار: Pollution، دوم،شماره۴، ۲۰۱۶، سال
تعداد صفحات: ۱۴
Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground–level ozone in Karaj City in Iran. Input data included humidity, hour temperature, wind speed, wind direction, PM2.5, PM10 and benzene, which were monitored in 2014. The coefficient of determination between the observed and predicted data was 0.955 and 0.999 for the TDNN and RBF, respectively. The Index of Agreement (IA) between the observed and predicted data was 0.921 for TDNN and 0.9998 for RBF. Both methods determined reliable results. However, the RBF neural network performance had better results than the TDNN neural network. The sensitivity analysis related to the TDNN neural network indicated that the PM2.5 had the greatest and benzene had the minimum effect on prediction of ground–level ozone concentration in comparison with other parameters in the study area.
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