Evaluation of the Performance of Classical and Artificial Intelligence Approaches in Prediction of Critical Submergence of Horizontal Intakes in Open Channel Flows

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ROUSHANGAR K.; GHASEMPOUR R.

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Journal: WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE) Year:2018 | Volume:28 | Issue:1 Page(s): 69-82

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Title

Evaluation of the Performance of Classical and Artificial Intelligence Approaches in Prediction of Critical Submergence of Horizontal Intakes in Open Channel Flows

Author(s)

ROUSHANGAR K. | GHASEMPOUR R. | Issue Writer Certificate

Keywords

ANFISQ2

Classical formulaQ2

Critical submergenceQ2

GEPQ2

Horizontal intakesQ2

Abstract

Horizontal intakes are of the most common structures for water withdrawal from open channels such as rivers, lakes and dam reservoirs. One of the hydraulic phenomena that mainly occurs during the water withdrawal process of the channels is the formation of vortex and air bubbles that can cause many problems for hydro-mechanical facilities of intakes. Insufficient height of water above the intake pipes (submergence depth) is the major cause of the vortex formation on Horizontal intakes. Due to the importance of this phenomenon, many models have been developed to estimate the Critical submergence depth. However, due to the uncertainties of the vortex formation near the intake, the obtained results often do not show a desired accuracy. In this study, using three experimental data series, the performance of artificial intelligence techniques (adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), gene expression programming (GEP)) and classical models were investigated for predicting the Critical submergence depth of Horizontal intakes with different bottom clearances in open channel flows. The results indicated that in estimating the Critical submergence depth, the artificial intelligence techniques are more accurate than the classical models and a good agreement could be seen between the observed and predicted values. The best result for the test series was obtained for C=di/2 state (di and C were intake diameter and bottom clearance, respectively) using SVM method with the values of R=0. 988, DC=0. 976 and RMSE=0. 191. According to the results of sensitivity analysis, it was observed that the relative velocity and Weber number in intake pipe were the most and the least significant parameters in the estimation of Critical submergence depth, respectively.

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APA: Copy

ROUSHANGAR, K., & GHASEMPOUR, R.. (2018). Evaluation of the Performance of Classical and Artificial Intelligence Approaches in Prediction of Critical Submergence of Horizontal Intakes in Open Channel Flows. WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE), 28(1 ), 69-82. SID. https://sid.ir/paper/147705/en

Vancouver: Copy

ROUSHANGAR K., GHASEMPOUR R.. Evaluation of the Performance of Classical and Artificial Intelligence Approaches in Prediction of Critical Submergence of Horizontal Intakes in Open Channel Flows. WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE)[Internet]. 2018;28(1 ):69-82. Available from: https://sid.ir/paper/147705/en

IEEE: Copy

K. ROUSHANGAR, and R. GHASEMPOUR, “Evaluation of the Performance of Classical and Artificial Intelligence Approaches in Prediction of Critical Submergence of Horizontal Intakes in Open Channel Flows,” WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE), vol. 28, no. 1 , pp. 69–82, 2018, [Online]. Available: https://sid.ir/paper/147705/en

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