A hybrid ensemble machine learning model for discharge coefficient prediction of side orifices with different shapes-Reference-Cited by-同舟云学术

A hybrid ensemble machine learning model for discharge coefficient prediction of side orifices with different shapes

Published:2023-06 Issue: Volume:91 Page:102372
ISSN:0955-5986
Container-title:Flow Measurement and Instrumentation
language:en
Short-container-title:Flow Measurement and Instrumentation

Author:

Deng Yangyu,Zhang Di,Zhang Dong,Wu Jian,Liu Yakun

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Science Applications,Instrumentation,Modeling and Simulation

Reference44 articles.

1. Simulation of discharge coefficient of triangular lateral orifices using an evolutionary design of generalized structure group method of data handling;Gerami Moghadam;Iran J. Sci. Technol.-Trans. Mech. Eng.,2022

2. Application of NARX neural network model for discharge prediction through lateral orifices;Ezzeldin;Alex. Eng. J.,2018

3. Rectangular lateral orifices in open channels;Ramamurthy;J. Environ. Eng.,1986

4. Flow through side slots;Gill;J. Environ. Eng.,1987

5. Analysis of rectangular side sluice gate;Swamee;J. Irrigat. Drain. Eng.,1993

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