Prediction Model of Side Weir Discharge Capacity Based on LS-SVM

被引：0
|
作者：
Li G. [1 ]
Shen G. [1 ]
Li S. [1 ]
Lu Q. [2 ]
机构：
[1] State Key Laboratory of Ecological Water Resources in Northwest Arid Area, Xi’an University of Technology, Xi’an
[2] Powerchina Huadong Engineering Corporation Limited, Hangzhou
来源：
Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering | 2023年 / 31卷 / 04期
关键词：
artificial intelligence; discharge coefficient; kernel function; LS-SVM; machine learning; rectangular side weir;
D O I：
10.16058/j.issn.1005-0930.2023.04.004
中图分类号：
学科分类号：
摘要：
In order to obtain the discharge coefficient (Cd) of the rectangular side weir accurately and efficiently, this paper designed the rectangular side weir model experiment to obtain the experimental values of Cd for six different discharge conditions.The least squares support vector machine (LS-SVM) model with different kernel functions was developed using MATLAB, and the dimensionless parameters affecting Cd were used as model inputs and Cd as model outputs.It is shown that the LS-SVM model can be used to predict the Cd of rectangular side weirs.The mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) are 0. 005, 0. 005 and 0. 966 respectively, which indicates that the model has better performance, higher accuracy and more accurate prediction.An intelligent model for predicting the discharge capacity of side weirs is proposed in this paper, and the influence of different dimensionless parameters on the model is discussed to verify the applicability of the model, which provides a reference basis for similar hydraulic engineering and also provides new ideas for solving complex hydraulics problems. © 2023 Editorial Board of Journal of Basic Science and. All rights reserved.
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页码：843 / 851
页数：8
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