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Title Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs
Type JournalPaper
Keywords data-driven solver, error distribution, nonlinear weir, sensitivity analysis
Abstract As a remarkable parameter, the discharge coefficient (Cd) plays an important role in determining weirs’ passing capacity. In this research work, the support vector machine (SVM) and the gene expression programming (GEP) algorithms were assessed to predict Cd of piano key weir (PKW), rectangular labyrinth weir (RLW), and trapezoidal labyrinth weir (TLW) with gathered experimental data set. Using dimensional analysis, various combinations of hydraulic and geometric non-dimensional parameters were extracted to perform simulation. The superior model for the SVM and the GEP predictor for PKW, RLW, and TLW included H ð Þ o=P, N, W=P , H ð Þ o=P, W=P, N and H ð Þ o=P, Lc=W, A=W, Fr respectively. The results showed that both algorithms are potential in predicting discharge coefficient, but the coefficient of determination (RMSE, R2, Cd(DDR)max) illustrated the superiority of the GEP performance over the SVM. The results of the sensitivity analysis determined the highest effective parameters for PKW, RLW, and TLW in predicting discharge coefficients are Ho=P, Ho=P, and Fr respectively.
Researchers Ravi Prakash Tripathi (Fourth Researcher), venkut arun (Third Researcher), Mahdi Fuladipanah (Second Researcher), Mahdi Majedi-Asl (First Researcher)