气候变化领域集成服务门户(CCPortal): Modeling velocity distributions in small streams using different neuro-fuzzy and neural computing techniques

CCPortal

DOI	10.2166/wcc.2019.103
	Modeling velocity distributions in small streams using different neuro-fuzzy and neural computing techniques
	Genc O.; Kisi O.; Ardiclioglu M.
发表日期	2020
ISSN	20402244
起始页码	390
结束页码	401
卷号	11 期号:2
英文摘要	Accurate estimation of velocity distribution in open channels or streams (especially in turbulent flow conditions) is very important and its measurement is very difficult because of spatio-temporal variation in velocity vectors. In the present study, velocity distribution in streams was estimated by two different artificial neural networks (ANN), ANN with conjugate gradient (ANN-CG) and ANN with Levenberg–Marquardt (ANN-LM), and two different adaptive neuro-fuzzy inference systems (ANFIS), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC). The performance of the proposed models was compared with the multiple-linear regression (MLR) model. The comparison results revealed that the ANN-CG, ANN-LM, ANFIS-GP, and ANFIS-SC models performed better than the MLR model in estimating velocity distribution. Among the soft computing methods, the ANFIS-GP was observed to be better than the ANN-CG, ANN-LM, and ANFIS-SC models. The root mean square errors (RMSE) and mean absolute errors (MAE) of the MLR model were reduced by 69% and 72%, respectively, using the ANFIS-GP model to estimate velocity distribution in the test period. © IWA Publishing 2020.
英文关键词	ANFIS; ANN; Modeling; Velocity distribution
语种	英语
scopus关键词	Fuzzy neural networks; Fuzzy systems; Linear regression; Mean square error; Soft computing; Velocity; Velocity distribution; Accurate estimation; Adaptive neuro-fuzzy inference system; Mean absolute error; Multiple linear regression models; Root mean square errors; Soft computing methods; Spatio-temporal variation; Subtractive clustering; Fuzzy inference; artificial neural network; comparative study; flow modeling; flow velocity; numerical model; regression analysis; spatiotemporal analysis; streamflow; streamwater
来源期刊	Journal of Water and Climate Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/147940
作者单位	Department of Civil Engineering, Meliksah University, Kayseri, Turkey; Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, 0162, Georgia; Department of Civil Engineering, Erciyes University, Kayseri, Turkey
推荐引用方式 GB/T 7714	Genc O.,Kisi O.,Ardiclioglu M.. Modeling velocity distributions in small streams using different neuro-fuzzy and neural computing techniques[J],2020,11(2).
APA	Genc O.,Kisi O.,&Ardiclioglu M..(2020).Modeling velocity distributions in small streams using different neuro-fuzzy and neural computing techniques.Journal of Water and Climate Change,11(2).
MLA	Genc O.,et al."Modeling velocity distributions in small streams using different neuro-fuzzy and neural computing techniques".Journal of Water and Climate Change 11.2(2020).