Prediction of discharge coefficient of side weir using adaptive neuro-fuzzy inference system

被引：14
作者：
Parsaie A. [1 ]
Haghiabi A.H. [1 ]
机构：
[1] Department of Water Engineering, Lorestan University, Khorramabad
来源：
Sustainable Water Resources Management | 2016年 / 2卷 / 3期
关键词：
ANFIS; Discharge coefficient; Optimal model structure; Principal component analysis; Side weir;
D O I：
10.1007/s40899-016-0055-6
中图分类号：
学科分类号：
摘要：
Predicting the discharge coefficient of the hydraulic structures is one of the main subjects related to the hydro-system management. Weirs are the common hydraulic structure widely used in the water engineering projects. Side weir is the common type of hydraulic structure used in water engineering projects. Principal component analysis of the affective parameters on the side weir discharge coefficient leads to develop optimal structure for the empirical formulas and artificial intelligent models. In this paper, the principal component analysis (PCA) technique was used to define the most important affective parameters on the discharge coefficient of side weir (Cd sw). The result of the PCA showed that the Froude number and ratio of the weir height to the upstream flow depth (P/h1) are the most influential parameters affecting the Cd sw. Developing the adaptive neuro-fuzzy inference system (ANFIS) based on the PCA result showed that the optimal ANFIS structure is related to consider the five and four Gaussian membership function for the Froude number and P/h1 parameters, respectively. The correlation coefficient of the ANFIS model during the training and testing stage was found to be 0.96 and 0.86 correspondingly. © 2016, Springer International Publishing Switzerland.
引用
收藏
页码：257 / 264
页数：7
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