مدل‌سازی و تعیین پارامترهای تاثیرگذار در ضریب مقاومت جریان کانال‌های فرسایش‌پذیر با شکل بستر تلماسه (دون) با استفاده از رگرسیون بردار پشتیبان

نویسندگان

1 دانشیار، گروه مهندسی عمران آب، دانشکده فنی و مهندسی عمران دانشگاه تبریز

2 استاد، گروه مهندسی عمران آب، دانشکده فنی و مهندسی عمران دانشگاه تبریز

3 دانشجوی دکتری مهندسی عمران – سازه‌های هیدرولیکی، دانشکده فنی و مهندسی عمران دانشگاه تبریز

چکیده

در مطالعه هیدرولیکی کانال‌ها و رودخانه‌ها، تعیین ضریب مقاومت جریان برای محاسبه دبی، سرعت و عمق جریان امری ضروری بوده و محاسبه مقادیر عددی این ضریب، با توجه به تأثیرگذاری پارامترهای مختلف بر آن پیچیده و مشکل است. در این پژوهش با بهره‌گیری از روش ماشین بردار پشتیبان به‌عنوان یکی از روش‌های یادگیری ماشینی، مقادیر ضریب زبری جریان در بستر فرسایش‌پذیر با شکل بستر تلماسه (دون)، برای چهار سری از داده‌های آزمایشگاهی معتبر در سه سناریو (مدل براساس مشخصات جریان، شکل بستر و جریان و مصالح بستر و جریان) تخمین زده شده و نرخ تأثیر پارامترهای ورودی با توجه به معیارهای ارزیابی مختلف مورد تجزیه و تحلیل قرار گرفته است. نتایج حاصله نشان داد که روش رگرسیون بردار پشتیبان دارای دقت قابل قبولی در تخمین ضریب زبری جریان می‌باشد. همچنین پارامتر عدد رینولدز جریان با بیشترین تأثیرگذاری، دارای اهمیت بیشتری در تخمین ضریب زبری جریان در بسترهای فرسایش‌پذیر با شکل بستر دون شناخته شد.

کلیدواژه‌ها


عنوان مقاله [English]

Modeling and Determination of Effective Parameters in Flow Roughness Coefficient in Alluvial Channels with Dun Bedforms Using Support Vector Regression

نویسندگان [English]

  • K Roushangar 1
  • MT Alami 2
  • S.M Saghebian 3
1 Assoc. Prof., Dept. of Water Eng., Faculty of Civil Eng., University of Tabriz, Iran
2 Prof., Dept. of Water Eng., Faculty of Civil Eng., University of Tabriz, Iran
3 PhD Candidate in Hydraulic Structures. University of Tabriz, Tabriz, Iran
چکیده [English]

Determination of flow roughness coefficient in the channels and river hydraulics is necessary for calculating of the discharge, the velocity and the depth of flow. Calculating the exact values of this coefficient is complex and difficult due to the influence of various parameters on it. In this study, using support vector regression as one of the machine learning approaches, the flow roughness coefficient in alluvial channel with dune bedform is predicted for four experimental data series under three scenarios (modeling based on flow characteristics, flow and bedform characteristics and flow and sediment characteristics) and the rate of input parameters is investigated using different performance criteria. The obtained results show that the support vector regression approach has desired accuracy in predicting the roughness coefficient. Also, the flow Reynolds number parameter with the most impact was recognized as the most significant parameter in estimating the roughness coefficient in the erodible beds with dune bedforms.

کلیدواژه‌ها [English]

  • Alluvial channel
  • Dune
  • Support Vector Regression
  • Roughness coefficient
  • Reynolds number
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