Self-adaptive extreme learning machine-based prediction of roller length of hydraulic jump on rough bed

ABSTRACT

In this study, the roller length of hydraulic jumps occurring on rough beds is modeled using the Self-Adaptive Extreme Learning Machine (SAELM) method. For this purpose, the parameters influencing the roller length are specified and four different SAELM models are developed based on them. A superior model is also established by analyzing the modeling results. For the superior model, the statistical values of the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient are calculated to be 1.720, 6.369 and 0.969, respectively. Also, the results of the SAELM superior model are compared with the Multilayer Perceptron Neural Network (MLPNN) and Support Vector Machine (SVM) methods. The analysis of the SVM, MLPNN and SVM models results reveals the effectiveness of the SAELM model. In this study, the uncertainty analysis of the SAELM, MLPNN and SVM models is also performed and the prediction error interval of 95% for the SAELM model is obtained which varies from −0.112 to +0.134.

KEYWORDS:

• Artificial intelligence
• hydraulic jump
• roller length
• self-adaptive extreme learning machine (SAELM)
• supercritical flow

Disclosure statement

No potential conflict of interest was reported by the authors.