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تعیین مناسب‌ترین روش منحنی سنجه و مقایسة آن با شبکة عصبی مصنوعی به منظور برآورد رسوبات معلق (مطالعة موردی: استان لرستان) | ||
| نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
| مقاله 14، دوره 68، شماره 2، شهریور 1394، صفحه 413-426 اصل مقاله (1.11 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jrwm.2015.54939 | ||
| نویسندگان | ||
| محسن یوسفی* 1؛ فاطمه برزگر2 | ||
| 1کارشناس‌ارشد آبخیزداری، دانشکدة منابع طبیعی و کویرشناسی، دانشگاه یزد، ایران | ||
| 2عضو هیئت‌علمی دانشکدة کشاورزی دانشگاه پیام نور، ایران | ||
| چکیده | ||
| برآورد میزان رسوبات معلق در رودخانهها از ابعاد مختلف کشاورزی، حفاظت خاک، سد‌سازی، حیات آبزیان، و همچنین جنبههای مختلف تحقیقاتی اهمیت فراوانی دارد. روشهای مختلفی برای بررسی و برآورد رسوبات معلق رودخانهها وجود دارد که البته توانایی این روشها در برآورد رسوبات متفاوت است. هدف از این مطالعه برآورد رسوبات معلق رودخانه با استفاده از شبکة عصبی پیش‌خور پس‌انتشار خطا با الگوریتم آموزشی لونبرگ- مارکوآرت و مقایسة نتایج با بهترین ترکیب منحنی سنجة رسوب و ضرایب اصلاحی است. در این مطالعه از آمار دبی و رسوب متناظر ده ایستگاه استان لرستان به صورت روزانه، ماهانه، فصلی، و دسته‌بندی‌شده استفاده شد. نتایج نشان داد از بین انواع مختلف منحنی سنجه و ضرایب اصلاحی استفاده‌شده، که جمعاً شامل بیست ترکیب بود، ترکیب منحنی سنجة ماهانه و ضریب اصلاحی MUVE بر اساس ضریب ناش- ساتکلیف و شاخص دقت مناسب‌تر است. در مرحلة بعد، نتایج حاصل از برآورد رسوب با مناسب‌ترین منحنی سنجه با نتایج حاصل از شبکة عصبی با استفاده از ضریب ناش- ساتکلیف و مجذور میانگین مربعات خطا مقایسه شد. نتایج نشان‌دهندة مناسب‌بودن شبکة عصبی پیش‌خور پس‌انتشار خطا در قیاس با منحنی سنجة رسوب است. | ||
| کلیدواژه‌ها | ||
| الگوریتم لونبرگ- مارکوآرت؛ رسوبات معلق؛ شبکة عصبی؛ منحنی سنجه | ||
| عنوان مقاله [English] | ||
| Estimation suspended sediment load with sediment rating curve and artificial neural network method (Case Study: Lorestan province) | ||
| نویسندگان [English] | ||
| mohsen yosefi1؛ Fatemeh Barzegari2 | ||
| 1MSC of watershed management, Faculty of Desert and Natural Resources, Yazd University, Iran | ||
| 2Academic staff of agricultural department of Payamnoor university, Iran | ||
| چکیده [English] | ||
| Suspended sediment estimation is an important factor from different aspects including, farming, soil conservation, dams, aquatic life, as well as various aspects of the research. There are different methods for suspended sediment estimation. This study aims to estimate suspended sediment using feed forward neural network with error back propagation with Levenberg-Marquardt back propagation algorithm and compare the results with best sediment rating curves among commonly used sediment rating curves, including: linear, seasonal, monthly and Mean load within discharge classes. To attain this, the sediment discharge and the corresponding water discharge data for ten hydrometric stations of Lorestan province of Iran were used. In next step different methods of sediment rating curves along with different correction factors, a total of 20 methods were applied to data. Results showed among examined methods; monthly rating curve with MUVE correction factor has been selected as best, based on Nash and Sutcliffe index and accuracy index. Then results of estimating sediment load by using selected sediment rating curve were compared with the results of the neural network. Mean-square error and Nash and Sutcliffe index were applied to select more appropriate method. The results showed the suitability of the feed forward neural network error propagation in compare with sediment rating curves. | ||
| کلیدواژه‌ها [English] | ||
| Suspended sediment load, Sediment Rating Curve, Levenberg-Marquardt algorithm | ||
| مراجع | ||
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