干旱区生态环境知识资源中心(Arid): Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches

Arid

DOI	10.2166/wcc.2021.221
	Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches
	Samantaray, Sandeep; Ghose, Dillip Kumar
通讯作者	Ghose, DK (corresponding author), Natl Inst Technol, Dept Civil Engn, Silchar, Assam, India.
来源期刊	JOURNAL OF WATER AND CLIMATE CHANGE
ISSN	2040-2244
EISSN	2408-9354
出版年	2022
英文摘要	Effective prediction of runoff is a substantial feature for the successful management of hydrological phenomena in arid regions. The present research findings reveal that a rainfall simulator (RS) can be a valuable instrument to estimate runoff as the intensity of rainfall is modifiable in the course of an experimental process, which turns out to be of great advantage. Rainfall-runoff process is a complex physical phenomenon caused by the effect of various parameters. In this research, a new hybrid technique integrating PSR (phase space reconstruction) with FFA (firefly algorithm) and SVM (support vector machine) has gained recognition in various modelling investigations in contrast to the principle of empirical risk minimization through ANN practices. Outcomes of SVM are contrasted against SVM-FFA and PSR-SVM-FFA models. The improvements in NSE (Nash-Sutcliffe), RMSE (Root Mean Square Error), and WI (Willmott's Index) by PSR-SVM-FFA over SVM models specify that the prediction accuracy of the hybrid model is better. The established PSR-SVM-FFA model generates preeminent WI values that range from 0.97 to 0.98, while the SVM and SVM-FFA models encompass 0.93-0.95 and 0.96-0.97, respectively. The proposed PSR-SVM-FFA model gives more accurate results and error limiting up to 2-3%.
英文关键词	PSR-SVM-FFA rainfall simulator runoff SVM SVM-FFA
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000711939000001
WOS关键词	PHASE-SPACE RECONSTRUCTION ; SUPPORT VECTOR MACHINES ; FIREFLY ALGORITHM ; NEURAL-NETWORKS ; STREAMFLOW ; MODELS ; WIND
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/367595
作者单位	[Samantaray, Sandeep; Ghose, Dillip Kumar] Natl Inst Technol, Dept Civil Engn, Silchar, Assam, India
推荐引用方式 GB/T 7714	Samantaray, Sandeep,Ghose, Dillip Kumar. Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches[J],2022.
APA	Samantaray, Sandeep,&Ghose, Dillip Kumar.(2022).Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches.JOURNAL OF WATER AND CLIMATE CHANGE.
MLA	Samantaray, Sandeep,et al."Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches".JOURNAL OF WATER AND CLIMATE CHANGE (2022).