مقالههای Ahmad Nohegar
توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقالههای نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده میشوند.
اطلاعات انتشار: هفتمین کنفرانس بین المللی پیشرفتهای علوم و تکنولوژی، سال ۱۳۹۱
تعداد صفحات: ۱۲
Several experimental and indirect methods have been done for estimating runoff and maximum discharge of watersheds, with the Curve Number (SCS–CN) method being one of the most well–known method among the available methods. This method is used to predict the direct runoff volume for a single rainfall event. In recent years, application of GIS (Geographic Information System) and remote sensing for estimating the curve number of runoff has been increased dramatically. In this study, GIS and remote sensing have been used to determine the curve number of the runoff of Sikhuran watershed. Therefore, after creating soil, slope, geology maps and also field study, the map of hydrologic soil group, soil vegetation, and land cover (which was obtained through satellite images) was drawn. Therefore, the CN map was prepared by integrating the mentioned maps in GIS and using SCS table, and the process of formation of surface flow or runoff was determined by using this method. Therefore, HEC–HMS software was used for validation. The CN calibration by HEC–HMS model showed an appropriate consistency with the mentioned method.<\div>
اطلاعات انتشار: دومین کنفرانس بین المللی توسعه پایدار، راهکارها و چالش ها با محوریت کشاورزی، منابع طبیعی، محیط زیست و گردشگری، سال ۱۳۹۴
تعداد صفحات: ۶
This study is an attempt to design a warning system at the preparing phase of the operation in flood instances in Masooleh. In so doing, a questionnaire of 39 items was devised by using various sources such as reports, books and technical Iranian and non–Iranian papers filled in by 25 experts from the province and the given area. The reliability confirmed with Cronbach's alpha coefficient of 0.739. The analysis were conducted following by t–test and SPSS software; in addition, the response level along with the necessary measures were drawn upon the data. The most important component of preparation in case of warning flood was defined in Masooleh town. Then, the best location for building a temporary settlement were found via TOPOSIS decision modeling..<\div>
۳Performance 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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