مقایسه‌ کارایی هیدرولیکی سرریز های کنگره ای با فرم تاج ربع دایره ای و نیم دایره ای با استفاده از روش های فرامدلی QNET, SVM, GEP, ANN))

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار دانشکده فنی و مهندسی-دانشگاه مراغه

2 دانشجوی کارشناسی ارشد، مهندسی عمران -آب و سازه‌های هیدرولیکی-دانشگاه مراغه،

3 گروه عمران. واحد ملکان. دانشگاه آزاد اسلامی. ملکان. ایران

چکیده

سرریزهای غیرخطی ضمن برخوردار بودن از مزیت‌های اقتصادی، قابلیت عبوردهی جریان بیشتری را نسبت به سرریزهای خطی دارند. الگوریتم‌های هوشمند به دلیل توانایی زیاد در کشف رابطه‌های دقیق پیچیده‌ی مخفی بین پارامترهای مستقل موثر و پارامتر وابسته و همچنین صرفه‌جویی مالی و زمانی، جایگاه بسیار ارزشمندی بین پژوهشگران پیدا کرده‌اند. در این پژوهش عملکرد الگوریتم‌های پشتیبان بردار ماشین(SVM) ، برنامه‌ریزی بیان ژن(GEP) ، نرم‌افزار (QNET) و شبکه هوش مصنوعی (ANN) در پیش‌بینی ضریب دبی سرریزهای غیرخطی تعداد 318 سری داده برای سناریو اول و سناریو دوم شامل تعداد 363 سری داده و سناریو سوم شامل ادغام داده‌ها (مجموع سناریو‌ی اول و دوم) که شامل 681 سری داده می‌باشند. تفاوت سناریو اول و دوم در فرم تاج سرریز ربع‌دایره‌ای و نیم‌دایره‌ای می-باشد. پارامترهای هندسی و هیدرولیکی مورد استفاده در این پژوهش شامل نسبت بار آبی کل(H_T/p) ، بزرگ نمایی (L_C/W)، زاویه دیواره سیکل(α) و ضریب دبی (Cd) می‌باشند. نتایج هوش مصنوعی نشان داد که تر کیب پارامترهای(Cd, H_T/p, α, L_C/W) در الگوریتم‌هایQNET ، ANN،GEP و SVM در مرحله‌ی آموزش مربوط به سناریو برتر با شاخصه‌های ارزیابی به‌ترتیب برابراست با (9960/0=(R2، (0080/0=(RMSE، (9961/0=(DC، (9980/0=(R2، (0057/0=(RMSE، (9980/0=(DC، (9837/0=(R2، (0207/0=(RMSE، (9838/0=(DC و (9902/0=(R2، (0186/0=(RMSE، (9830/0=(DC می‌باشد. که در مقایسه با دیگر ترکیب‌ها منجر به بهینه‌ترین خروجی شده است که نشان دهنده دقت بسیار مطلوب به‌ترتیب در هر چهار روش عبارت است از ANN، QNET، SVM و GEP در پیش‌بینی ضریب دبی سرریز غیرخطی است. نتایج آنالیز حساسیت نشان داد که پارامتر موثر در تعیین ضریب دبی سرریز غیرخطی در تمامی روش‌ها پارامتر نسبت بار آبی کل (H_T/p) می‌باشد. مقایسه نتایج این تحقیق با سایر محققین نشان می‌دهد که شاخصه‌های ارزیابی برای تمامی روش‌های تحقیق حاضر نسبت به سایر محققین نسبتا بهتر می‌باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Comparison of The Hydraulic Efficiency of labyrinth Weirs with a Quarter and Semi-Circular Crest Shape Using Neural Networks (QNET, SVM, GEP, ANN)

نویسندگان [English]

  • Mahdi Majedi Asl 1
  • tohid omidpour alavian 2
  • Mehdi Kouhdaragh 3
1 Associate Professor
2 M.SC., University of Maragheh
3 Civil Engineering Department, Engineering Faculty, Malekan Branch, Islamic Azad University, Malekan, Iran
چکیده [English]

While having economic advantages, non-linear weirs have more passing flow capacity than linear weirs. These weirs have higher discharge efficiency with less free height upstream compared to linear weirs by increasing the length of the crown at a certain width. Intelligent algorithms have found a valuable place among researchers due to their great ability to discover complex and hidden relationships between effective independent parameters and dependent parameters, as well as saving money and time. In this research, the performance of support vector machine (SVM), gene expression programming (GEP), software (QNET) and artificial intelligence network (ANN) in predicting the discharge coefficient of non-linear Weirs of 318 data series for the first scenario And the second scenario includes the number of 363 data series and the third scenario includes data integration (the sum of the first and second scenario) which includes 681 data series. The difference between the first and second scenarios is in the shape of the quarter-circle and semi-circle weir crown. The geome tric and hydraulic lines used in this research include total water load ratio (H_T/p), magnification) L_C/W), cycle wall angle (α) and discharge coefficient (Cd). The results of artificial intelligence showed that the combinations (Cd, H_T/p, α, L_C/W) in QNET, ANN, GEP, SVM algorithms in the training stage related to the superior scenario are equal to the evaluation indicators respectively (R2=0.9960), (RMSE=0.0080), (DC=0.9961), (R2=0.9980), (RMSE=0.0057), (DC=0.9980), (R2=0.9837), (RMSE=0.0207), (DC=0.9838) and (R2=0.9902), (RMSE=0.0186), (DC=0.9830). Which has led to the most optimal output compared to other combinations, which indicates a very favorable accuracy in all four methods, namely ANN, QNET, SVM and GEP in predicting the weir discharge coefficient is non-linear. The results of the sensitivity analysis showed that the effective parameter in determining the nonlinear weir discharge coefficient in all methods is the total water load ratio parameter (H_T/p).

کلیدواژه‌ها [English]

  • Sensitivity Analysis
  • Non-Linear Weirs
  • Neural Networks
  • Discharge Coefficient

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