بررسی روند تأثیرات پارامترهای هیدرولیکی و رسوبی بر میزان انتقال بار بستر با استفاده از همبستگی‌های آماری و روش‌های یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استاد گروه مهندسی آب، دانشکده عمران دانشگاه تبریز، تبریز، ایران

2 کارشناس ارشد مهندسی آب و سازه‌های هیدرولیکی, دانشکده عمران دانشگاه تبریز, تبریز, ایران

چکیده

در مهندسی هیدرولیک و رودخانه، بارهای جامد رسوبی نقش اساسی را در تعیین رفتار رودخانه و کنترل مورفولوژی دارند؛ به همین دلیل ارزیابی و برآورد صحیح انتقال بار جامد رسوبی از دیرباز یکی از مسائل عمده و اصلی در علوم مرتبط با مهندسی رودخانه و محیط‌زیست می­باشد. هدف از این تحقیق برآورد میزان انتقال بار بستر در 19 رودخانه با بستر شنی می‌باشد. بدین منظور، ابتدا روند همبستگی آماری بین پارامتر انتقال رسوب (دبی بار بستر) و پارامترهای هیدرولیکی و رسوبی (دبی جریان، عمق جریان، سرعت متوسط جریان، قطر متوسط ذرات رسوب، عدد فرود و...) بررسی شده و دبی بار بستر به‌صورت تابع رگرسیونی تک‌متغیره برآورد می‌شود. مطابق نتایج ارائه شده به یک همبستگی مطلوبی بین پارامتر انتقال رسوب و پارامترهای هیدرولیکی و رسوبی رسیده شد و نتایج نشان داد این روابط رگرسیون ساده در اکثر رودخانه­ها از دقت قابل­قبولی برخوردار بوده است. ثانیاً، عملکرد 10 رابطه تجربی در پیش­بینی بار بستر مورد بررسی قرار گرفت. همة فرمول­ها از نتایج خیلی ضعیفی برخوردار بوده­اند؛ به همین دلیل پارامتـرهای مربوط به فرمول­هایی که نتایج نسبتاً بهتری نسبت به فرمول­های دیگر داشته­اند، انتخاب شده و به‌منظور افزایش دقت برآورد، بار دیگر با استفاده از دو روش یادگیری ماشین مبتنی بر کرنل: ماشین بردار پشتیبان (SVM)، رگرسیون فرآیند گاوسی (GPR) مدل­سازی انجام شد. نتایج حاصله نشان داد روش­های ماشینی از دقت قابل­قبولی در پیش­بینی بار بستر برخوردار بوده‌اند و مدل مربوط به پارامترهای فرمول بگنولد که شامل پارامترهای قدرت جریان، عمق جریان و قطر متوسط ذرات رسوب می‌باشد، با دارا بودن ضریب همبستگی و شاخص نش - ساتکلیف به ترتیب برابر 923/0R= و 851/0 NSE=برترین مدل حاصل از روش‌های ماشینی می‌باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Investigation of the Effects of Hydraulic and Sedimentary Parameters on the Rate of Bed Load Transfport Using Statistical Correlations and Machine Learning Methods

نویسندگان [English]

  • Kiyoumars Roushangar 1
  • Samira Joulazadeh 2
1 professor, Department of water engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
2 M. Sc. of water and hydraulic structures engineering, Department of Civil Engineering, University of Tabriz, Tabriz, Iran
چکیده [English]

In hydraulic and river engineering, solid load sediment play an essential role in determining river behavior and morphological control; For this reason, the assessment and correct estimation of  solid load sediment transfport from a long time ago is one of the important issues in the sciences related to river engineering and the environment. The purpose of this study is to estimate the bed load transfer in 19 gravel-bed rivers. For this purpose, first the statistical correlation trend between sediment transport parameter (bed load discharge) and hydraulic and sedimentary parameters (flow discharge, flow depth, flow velocity, the median bed material particle diameter, Froude number,…) is investigated and the bed load discharge is estimated as a univariate regression function. According to the presented results, a favorable correlation was reached between the sediment transport parameter and hydraulic and sedimentary parameters and the results showed that these simple regression relationships in most rivers had acceptable accuracy. Also, the performance of 10 experimental formulas in bed load prediction was investigated. All formulas have had very poor results. For this reason, the parameters related to the formulas that had relatively better results than the other formulas were selected and, in order to increase the estimation accuracy, once again using two kernel-based machine learning methods: Support Vector Machine (SVM). Gaussian process regression (GPR) modeling was performed. The results showed that the machine methods have acceptable accuracy in predicting the bed load and the model is related to the parameters of  Begnold formula, which includes the parameters of the stream power, the average flow depth and the median bed material particle diameter, with R =0.923 and NSE =0.851 has the best results in the machine methods.

کلیدواژه‌ها [English]

  • Sediment Prediction
  • Statistical Correlation
  • Experimental Formula
  • Support vector machine
  • Gaussian process regression
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