چکیده مقاله
Accurate prediction of flow discharge in non standard and unconventional flume geometries is vital for water resources management and hydraulic design This study develops a new machine learning framework to estimate the dimensionless discharge coefficient Q in flumes with converging triangular walls Firstly, we considered six dimensionless geometric and flow parameters Then, correlation analysis identified four dominant predictors, which were used as model inputs Two ensemble learning algorithms, Random Forest RF and Extreme Gradient Boosting XGBoost , were applied to a comprehensive experimental dataset We used multiple performance metrics, including the Pearson correlation coefficient, coefficient of determination R² , mean squared error MSE , root mean squared error RMSE , fourth root of mean quadrupled error R4MS4E , and mean absolute error MAE , to evaluate both models Both models achieved high accuracies In the test phase, XGBoost achieved an RMSE of 0 0140 and an R² value of 0 9872 Selecting dimensionless parameters as inputs and the output ensures the generalizability of the models across various flume and flow scales Results have concluded the high effectiveness of both models for modeling complex hydraulic behavior in water resources engineering
کلیدواژهها
نویسندگان
Mohammad Parvaneh
Graduate Student, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran
Gholam Reza Rakhshandehroo
Professor, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran
Nasser Talebbeydokhti
Professor, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran
شیوه ارجاع
Parvaneh, Mohammad and Rakhshandehroo, Gholam Reza and Talebbeydokhti, Nasser,1404,Estimating Discharge Capacity of Flumes with Converging Triangular Walls Using Machine Learning,14th International Congress on Civil Engineering,Tehran
ارائهشده در
مجموعه مقالات چهاردهمین کنگره بین المللی مهندسی عمران29 مهر 1404 · تهران