Application of soft computing techniques to estimate the scouring depth formed by crossing jets. (11th April 2023)

• Record Type: Journal Article
• Authors: Mirzaee, Reza; Mohammadi, Mirali; Mousavi, Sayed-Farhad; Bagherzadeh, Mohammad; Hosseini, Khosrow
• Is Part Of: Water science and technology. Volume 87:Number 8(2023)
• Page Start: 1853
• Page End: 1865
• Publication Date: 2023-04-11
• Subjects: crossing jet -- MLP -- plunge pool -- scouring -- tailwater; Water -- Pollution; Sewage -- Purification; Water quality management; Periodicals; 628.168
• DOI: 10.2166/wst.2023.108
• Languages: English
• ISSNs: 0273-1223

Abstract

Abstract: The scouring depth caused by the water jet outputs from a dam is one of the crucial parameters for design purposes. Due to the importance of the subject, several laboratory studies have been conducted to understand this subject. Nevertheless, using soft computing techniques is a new attitude for modeling and predicting the natural process parameters. Herein, the types of soft computing techniques for estimating the scouring depth of a plunge pool caused by the symmetrical crossing jets have been explored. The parameters involved in the scouring phenomenon are densimetric Froude number, tailwater depth, vertical jet angle, horizontal crossing angles, and the distance between the crossing points of two jets and the water level. The prediction results show that the Multi-Layer Perceptron (MLP) model gives the best performance among the other models tested here. The Pearson correlation coefficient, root mean square error, and normalized root mean square error for the MLP model were 0.9527, 0.9039, and 19.36% for the test phase, respectively. Furthermore, based on the sensitivity analysis, the parameters, for instance, tailwater depth and vertical jet angle have the highest and lowest effects for predicting the scouring depth of a plunge pool, respectively. HIGHLIGHTS: Predicting the scour depth of a plunge pool. Using different methods of artificial intelligence like Multi-Layer Perceptron (MLP) model, Radial Basis Function (RBF) network, Random Forest Algorithm (RFA), and Multivariate Adaptive Regression Splines (MARS) model. Performing sensitivity analysis based on the parameters of the present study.