نوع مقاله : مقاله عمران
نویسندگان
1 گروه مهندسی آب /دانشکده عمران/دانشگاه تبریز/تبریز/ایران
2 دانشیار مهندسی عمران- هیدرولیک و مکانیک مهندسی رودخانه، دانشکده فنی و مهندسی، دانشگاه ارومیه.
3 دانشجوی کارشناسی ارشد مهندسی عمران- سازه، دانشکده فنی، دانشگاه ارومیه، ایران
4 استادیار گروه مهندسی عمران، دانشکده فنی، دانشگاه صنعتی بورسا، ترکیه
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Prediction of flow discharge coefficient, Cd, for a sluice gate under free and submerged flow conditions is one of the essential issues in hydraulics. In recent years, various semi-empirical equations have been developed in order to predict Cd for a sluice gate that application of those formulas under submerged flow conditions suffered from large errors. The aim of the present research is to use Gaussian Process Regression (GPR) and Support Vector Machine (SVM) used in soft computing techniques, so that estimating Cd in submerged flow conditions and comparing the results with quasi-experimental methods are of interest, herein. For this purpose, an experimental dataset comprised of 122 data points were used to feed the methods utilized. Different combinations of dimensionless parameters were then prepared and the performance of the afore mentioned methods were assessed. The results showed that SVM with input parameters of 𝑦𝑡⁄𝑤, 𝑦0⁄𝑤, 1/𝐹𝑟2 and S by the values of Root Mean Square Error (RMSE=0.017), correlation coefficient (R=0.97) and Nash-Sutcliffe Equivalent (NSE=0.95) had a better performance than GPR and other semi-empirical approaches, indeed.
کلیدواژهها [English]