A study of five types of ANN-based approaches to predict discharge coefficient of combined weir-gate
Article Type:
Research/Original Article (دارای رتبه معتبر)
Since many years ago, flow measurement has become a fundamental issue in hydraulic engineering. One of the conventional methods of flow measurement is the use of combined structures. In this regard, using a combined structure, including a gate and a weir, is one of the approaches that has attracted the attention of researchers in this field. Therefore, in this research, five different methods based on artificial neural networks were used to predict the discharge coefficient. The networks architecture includes an input layer with four neurons, a hidden layer with seven neurons, and an output layer with one neuron. be mentioned that the number of neurons within the hidden layer is set to 4 only for the recurrent network. For the hidden layer, the logarithmic sigmoid activation function was used. Also, the linear activation function was used for the output layer. Finally, the results showed that the Levenberg-Marquardt (LM) algorithm performs better than the other methods. The convergence speed of this algorithm, which also uses the second derivative, is much higher than others. In this case, the coefficient of determination (R^2) for the training and the test stage was equal to 0.92616 and 0.94079, respectively. In addition to, the first type of rough model with the gradient descent training algorithm also had an acceptable performance and was placed in second place. Also, the sensitivity analysis on the dimensionless parameters affecting this issue showed that the H⁄d, y⁄d, b⁄B, and b⁄d parameters have maximum to minimum effect on the model results, respectively.
Journal of Hydraulic Structures, Volume:8 Issue: 4, Winter 2022
73 to 92
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یک‌ساله به مبلغ 990,000ريال می‌توانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
  • حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران می‌شود.
  • پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانه‌های چاپی و دیجیتال را به کاربر نمی‌دهد.
In order to view content subscription is required

Personal subscription
Subscribe magiran.com for 50 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!