بررسی میزان افت انرژی جریان در سرریزهای زیگزاگی با استفاده از روش‌های مبتنی بر محاسبات نرم

نوع مقاله : مقاله کامل پژوهشی

نویسندگان

دانشکده مهندسی عمران، دانشگاه تبریز

چکیده

هدف از پژوهش حاضر بررسی میزان افت انرژی نسبی (EDR) در سرریزهای کنگره‌ای با پلان مثلثی و ذوزنقه‌ای در ابعاد مختلف با استفاده از مدل‌ ماشین بردار پشتیبان (SVM)، الگوریتم جنگل تصادفی (RF) و روش شبکه عصبی مصنوعی (ANN) است. از مجموعه داده‌های آزمایشگاهی 70% برای مرحله آموزش و 30% برای مرحله آزمون مورد استفاده قرار گرفتند. در مدل SVM، نتایج کرنل‌های مختلف نشان داد که کرنل تابع پایه شعاعی (RBF) نتایج بهتری در پیش‌بینی افت انرژی نسبی سرریز کنگره‌ای در مقایسه با کرنل‌های چندجمله‌ای (Polynomial)، خطی (Linear) و سیگموئید (Sigmoid) دارد. نتایج شاخص‌های آماری ضریب همبستگی (R)، میانگین درصد خطای نسبی (Mean RE%)، خطای جذر میانگین مربعات (RMSE) و شاخص کلینگ گوپتا (KGE) برای مدل SVM-RBF در مرحله آزمون به‌ترتیب 907/0، 38/1%، 0153/0 و 744/0 است. در روش ANN شبکه چند لایه پرسپترون (MLP) نتایج دقیق‌تری در مقایسه با شبکه RBF دارد. نتایج شاخص‌های فوق در مرحله آزمون برای روش ANN-MLP به‌ترتیب 969/0، 73/0%، 007/0 و 968/0 است. همچنین این نتایج برای مدل RF به‌ترتیب 878/0، 78/1%، 0192/0 و 362/0 است. بررسی نتایج نشان داد که روش ANN عملکرد مطلوبی نسبت به سایر مدل‌های SVM و RF دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigating the Rate of Flow Energy Loss in Zigzag Weirs Using Methods Based on Soft Computing

نویسندگان [English]

  • Hamidreza Abbaszadeh
  • Reza Tarinejad
Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
چکیده [English]

The purpose of this research is to investigate the amount of relative energy loss (EDR) in zigzag weirs with triangular and trapezoidal plans in different dimensions using Support Vector Machine (SVM) model, Random Forest (RF) algorithm, and Artificial Neural Network (ANN) method. 70% of the experimental data sets were used for the training phase and 30% for the test phase. In the SVM model, the results of different kernels showed that the Radial Basis Function (RBF) kernel has better results in predicting the relative energy loss of zigzag weirs compared to the Polynomial, Linear, and Sigmoid kernels. The results of statistical indicators of correlation coefficient (R), percentage Mean Relative Error (Mean RE%), Root Mean Square Error (RMSE), and Kling Gupta Efficiency (KGE) for the SVM-RBF model in the test phase are 0.907, 1.38%, 0.0153, and 0.744, respectively. In the ANN method, the Multi-Layer Perceptron (MLP) network has more accurate results compared to the RBF network. The results of the above indicators in the test phase for the ANN-MLP method are 0.969, 0.73%, 0.007, and 0.968, respectively. In addition, these results for the RF model are 0.878, 1.78%, 0.0192, and 0.362, respectively. Examining the results showed that the ANN method performs better than other SVM and RF models.

کلیدواژه‌ها [English]

  • Zigzag weir
  • Energy loss
  • Artificial Neural Network
  • Support Vector Machine
  • Random Forest algorithm
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