Evaluation of energy dissipation on stepped spillway using evolutionary computing

• Published in: Applied water science 2019-08, Vol.9 (6), p.1-7, Article 144
• Main Authors: Parsaie, Abbas, Haghiabi, Amir Hamzeh
• Format: Article
• Language: English
• Subjects: Algorithms; Aquatic Pollution; Comparative Law; Computational fluid dynamics; Drop number; Earth and Environmental Science; Earth Sciences; Energy dissipation; Energy exchange; Evaluation; Evolutionary algorithms; Hydrogeology; Industrial and Production Engineering; International & Foreign Law; M5 algorithm; Multilayer perceptrons; Nanotechnology; Neural networks; Original Article; Private International Law; Root-mean-square errors; Soft computing; Spillways; Testing; Training; Transfer functions; Waste Water Technology; Water and Energy; Water Industry/Water Technologies; Water Management; Water Pollution Control
• ISSN: 2190-5487; 2190-5495
• DOI: 10.1007/s13201-019-1019-4

In this study, using the M5 algorithm and multilayer perceptron neural network (MLPNN), the capability of stepped spillways regarding energy dissipation (ED) was approximated. For this purpose, relevant data was collected from valid sources. The study of the developed model based on the M5 algorithm showed that the Drop and Froude numbers play important roles in modeling and approximating the ED. The error indices of M5 algorithm in training were R 2  = 0.99 and RMSE = 2.48 and in testing were R 2  = 0.99 and RMSE = 2.23. The study of developed MLPNN revealed that this model has one hidden layer which includes five neurons. Among the tested transfer functions, the great efficiency was related to the Tansing function. The error indices of MLPNN in training were R 2  = 0.97 and RMSE = 3.73 and in testing stages were R 2  = 0.97 and RMSE = 3.98. Evaluation of the results of both applied methods shows that the accuracy of the MLPNN is a bit less than the M5 algorithm.