Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches

doi:10.1007/s11629-018-5156-2

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > Journal of Mountain Science > Journal of Mountain Science-2020 > Vol17 No.2

	Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches
	Roushangar, Kiyoumars; Shahnazi, Saman
刊名	JOURNAL OF MOUNTAIN SCIENCE
	2020
卷号	17 期号:2 页码:480-491
关键词	Total sediment loads Support vector machine Gaussian process regression Kernel extreme learning machine Mountain Rivers
ISSN号	1672-6316
DOI	10.1007/s11629-018-5156-2
文献子类	Article
英文摘要	It is important to have a reasonable estimation of sediment transport rate with respect to its significant role in the planning and management of water resources projects. The complicate nature of sediment transport in gravel-bed rivers causes inaccuracies of empirical formulas in the prediction of this phenomenon. Artificial intelligences as alternative approaches can provide solutions to such complex problems. The present study aimed at investigating the capability of kernel-based approaches in predicting total sediment loads and identification of influential parameters of total sediment transport. For this purpose, Gaussian process regression (GPR), Support vector machine (SVM) and kernel extreme learning machine (KELM) are applied to enhance the prediction level of total sediment loads in 19 mountain gravel-bed streams and rivers located in the United States. Several parameters based on two scenarios are investigated and consecutive predicted results are compared with some well-known formulas. Scenario 1 considers only hydraulic characteristics and on the other side, the second scenario was formed using hydraulic and sediment properties. The obtained results reveal that using the parameters of hydraulic conditions as inputs gives a good estimation of total sediment loads. Furthermore, it was revealed that KELM method with input parameters of Froude number (Fr), ratio of average velocity (V) to shear velocity (U-*) and shields number (theta) yields a correlation coefficient (R) of 0.951, a Nash-Sutcliffe efficiency (NSE) of 0.903 and root mean squared error (RMSE) of 0.021 and indicates superior results compared with other methods. Performing sensitivity analysis showed that the ratio of average velocity to shear flow velocity and the Froude number are the most effective parameters in predicting total sediment loads of gravel-bed rivers.
电子版国际标准刊号	1993-0321
语种	英语
WOS记录号	WOS:000512107000017
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/46750]
专题	Journal of Mountain Science_Journal of Mountain Science-2020_Vol17 No.2
推荐引用方式 GB/T 7714	Roushangar, Kiyoumars,Shahnazi, Saman. Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches[J]. JOURNAL OF MOUNTAIN SCIENCE,2020,17(2):480-491.
APA	Roushangar, Kiyoumars,&Shahnazi, Saman.(2020).Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches.JOURNAL OF MOUNTAIN SCIENCE,17(2),480-491.
MLA	Roushangar, Kiyoumars,et al."Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches".JOURNAL OF MOUNTAIN SCIENCE 17.2(2020):480-491.