نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشیار، دانشکده فنی مهندسی عمران، دانشگاه تبریز
2 کارشناس ارشد آب و سازه‌های هیدرولیکی، دانشکده فنی مهندسی عمران، دانشگاه تبریز
چکیده
در بیشتر مطالعات منابع آب، مقدار بار بستر با توجه به سختی‌ها و هزینه‌های مربوط به اندازه‌گیری به‌صورت نسبت ثابتی از بار کل در نظر گرفته می‌شود که با توجه به تغییرپذیری بالای این نسبت، معقول نیست. در این پژوهش، با استفاده از داده‌های جمع‌آوری شده از ۱۹ رودخانه با بستر شنی واقع در ایالات متحده آمریکا به مدل‌سازی بار بستر، بار کل و در نهایت نسبت این دو با ماشین بردار پشتیبان که شاخه‌ای از روش‌های هوشمند می‌باشد، پرداخته شد. سپس نتایج حاصله با روابط کلاسیک مقایسه و مورد ارزیابی و بررسی قرار گرفت. نتایج نشان داد که در تخمین بار بستر و نیز بار کل رسوبی این روش کارایی بسیار بالایی نسبت به روش‌های کلاسیک داشته، عملکرد آن نیز در پیش‌بینی نسبت بار بستر به بار کل دارای نتایج قابل قبولی است. در ضمن مدل‌سازی‌های مذکور نیز نشان دادند که نسبت سرعت متوسط به سرعت برشی جریان و عدد فرود، تاثیرگذارترین پارامترها در پیش‌بینی بار بستر، بار کل و نسبت این دو می‌باشد.
کلیدواژه‌ها
عنوان مقاله [English]
Evaluation and prediction of bed to total sediment load in gravel-bed rivers using classic and intelligent methods
نویسندگان [English]
- Kiyoumars Roushangar 1
- Saman Shahnazi 2
1 Associate Professor, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
2 MSc, Faculty of civil engineering, university of Tabriz, Tabriz, Iran
چکیده [English]
In most water resources studies, the bed load transport rate is considered as a constant proportion of total load due to the difficulty and costs associated with measuring of it, which is not reasonable due to the high variability of this ratio. In this study, data collected from 19 coarse-grained rivers in the United States were employed to predict bed load, total load transport rates and the ratio of bed to total sediment load using Support Vector Machine which is a branch of intelligent methods. Next, the results were compared and evaluated with classical methods. Results showed that this method has a very high performance compared to the classical methods and performance criteria in predicting the bed to total sediment load ratio has acceptable results. In addition, the modeling showed that the ratio of average velocity to shear flow velocity and the Froude number is the most effective parameters in predicting bed load, total load and the ratio of these.
کلیدواژه‌ها [English]
- Bed load
- Froude number
- Shear flow velocity
- Support Vector Machine
- Classic method
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