مقالههای Ahad Tavasoli
توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقالههای نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده میشوند.
۱Performance of Different Models for Curve Number Estimation (Case study: Bar Watershed in Khorasan Razavi Province, Iran)
اطلاعات انتشار: ECOPERSIA، سوم،شماره۳، Summer ۲۰۱۵، سال ۰
تعداد صفحات: ۱۹
Among different models for runoff estimation in watershed management, the Soil Conservation Services–Curve Number (SCS–CN) method along with its modifications have been widely applied to ungauged watersheds because of quickly and more accurate estimation of surface runoff. This approach has been widely accepted by hydrologists, water resources planners, foresters, and engineers, as well. Therefore, this work was aimed to estimate the curve number using CN–values through several methods viz. SCS, Sobhani (1975), Hawkins et al. (1985), Chow et al. (1988), Neitsch et al. (2002) and Mishra et al. (2008) in Bar Watershed, Iran. According to the results, the Neitsch formula showed the best performance for estimating the Curve Number in situation with low (CNI) and high (CNIII) antecedent moisture conditions. However, the weakest performance was related to Mishra (2008) in CNI and CNIII–conversions. The weakest performance was resulted from the exponential form of the Neitsch et al. formula and the variable meteorological conditions of the Bar Watershed over the year.
نویسنده(ها): Mahboobeh Moatamednia، Ahmad Nohegar، Arash Malekian، Hanieh Asadi، Ahad Tavasoli، Mahdi Safari، Kamal Karimi
اطلاعات انتشار: Desert، بيستم،شماره۱، ۲۰۱۵، سال ۰
تعداد صفحات: ۱۱
Rainfall–runoff relationship is very important in many fields of hydrology such as water supply and water resource management and there are many models in this field. Among these models, the Artificial Neural Network (ANN) was found suitable for processing rainfall–runoff and opened various approaches in hydrological modeling. In addition, ANNs are quick and flexible approaches which provide very promising results, and are cheaper and simpler to implement than their physically based models. Therefore, this study evaluated the use of ANN models to forecast daily flows in Bar watershed, a semi–arid region in the northwest Razavi Khorasan Province of Iran. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBF), were developed and their abilities to predict run off were compared for a period of fifty–five years from 1951 to 2006. The best performance was achieved based on statistical criteria such as RMSE, RE and SSE. It was found that MLP showed a good generalization of the rainfall–runoff relationship and is better than RBF. In addition, 1 day antecedent runoff affected river flow, such that the statistical criteria decreased but the 5–day antecedent rainfall remained unaffected. Furthermore, considering MLP, RE and RMSE, the best model produced the values 46.21 and 0.75 while the RBF model recorded 177.60 and 0.82, respectively.
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