Using Binary Tree Algorithms to Predict a Discharge Coefficient of the Type-A Piano Key Weir
DOI:
https://doi.org/10.31272/jeasd.3029Keywords:
Decision Tree, Discharge coefficient, Machine Learning, Piano Key Weir, Random ForestAbstract
Type-A PKW is a type of spillway with inlet and outlet keys connected in a zigzag. The survey shows that previous studies on the flow over PKW mainly focused on establishing empirical equations to calculate Cd. There is no study that meets all research needs. This shows that studies have not taken advantage of data from other studies. Meanwhile, regression Machine Learning (ML) algorithms can solve difficult problems in empirical studies. That is the ability to make maximum use of data sources, and it does not need constraints in predicting Cd values. Therefore, this study was carried out to develop a process to apply ML algorithms to the regression prediction of hydraulic characteristics (including 3 phases). In particular, the application of "Binary tree" algorithms (Decision Tree and Random Forest algorithms) is used to illustrate the study. In addition to determining the advantages of ML algorithms, a comparison was made between applying ML algorithms and using empirical equations to determine Cd values. The study shows that the Random Forest algorithm has better predicting efficiency with statistical indicators very close to the ideal point (R2 = 0.98, RMSE = 0.046, MAPE = 3.1% and MEA = 0.028).
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Copyright (c) 2025 Nguyen Minh Ngoc, Bui Hai Phong, Le Van Nghi, Tran Ngoc Thang, Nguyen Thanh Phong (Author)
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