Predicting discharge coefficient of weir–orifice in closed conduit using a neuro-fuzzy model improved by multi-phase PSOGSA

被引：0
作者：
Rana Muhammad Adnan
Payam Khosravinia
Ozgur Kisi
Mohammad Reza Nikpour
Hong-Liang Dai
Mazyar Osmani
Seyyede Aniseh Ghazaei
机构：
[1] Guangzhou University,College of Architecture and Urban Planning
[2] University of Kurdistan,Department of Water Sciences and Engineering, Faculty of Agriculture
[3] Luebeck University of Applied Sciences,Department of Civil Engineering
[4] Ilia State University,Civil Engineering Department
[5] University of Mohaghegh Ardabili,Department of Water Engineering, Faculty of Agriculture and Natural Resources
[6] Guangzhou University,School of Economics and Statistics
[7] K. N. Toosi University of Technology,Faculty of Civil Engineering
来源：
Applied Water Science | 2024年 / 14卷
关键词：
Combined weir–orifices; Dimensionless experimental discharge; Modeling; Particle swarm optimization; Gravity search algorithm; Neuro-fuzzy system;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This study investigates the viability of a strong algorithm (PSOGSA) merging particle swarm optimization (PSO) and gravity search algorithm (GSA) in tuning adaptive neuro-fuzzy system (ANFIS) parameters for modeling dimensionless experimental discharge of combined weir–orifices. The results are compared with the standard ANFIS and two hybrid models ANFIS tuned with PSO and GSA. The models are assessed by applying several dimensionless input parameters, consisting h/D (the ratio of upstream water depth to channel diameter), W/D (the ratio of orifice opening height to channel diameter), H/D (the ratio of plate height to channel diameter) and using comparison indices such as root-mean-square error and mean absolute error. The outcomes reveal that the new ANFIS-PSOGSA method provides superior accuracy in modeling dimensionless experimental discharge over the ANFIS-PSO, ANFIS-GSA and standard ANFIS method. Among the input parameters, the h/D was found to be the most effective input on modeling dimensionless experimental discharge while involving the H/D parameter deteriorated the models’ performances. The relative root-mean-square error differences between ANFIS-PSOGSA and ANFIS are found as 50% and 68.29% for pipe A and B, respectively. By implementing the ANFIS-PSOGSA, the accuracy of ANFIS-PSO and ANFIS-GSA is also improved in modeling dimensionless experimental discharge by 45.71% and 29.63% in pipe A and by 63.89% and 45.83% in pipe B with respect to root-mean-square error.
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