干旱区生态环境知识资源中心(Arid): Assessment of Sediment Load Concentration Using SVM, SVM-FFA and PSR-SVM-FFA in Arid Watershed, India: A Case Study

Arid

DOI	10.1007/s12205-020-1889-x
	Assessment of Sediment Load Concentration Using SVM, SVM-FFA and PSR-SVM-FFA in Arid Watershed, India: A Case Study
	Samantaray, Sandeep; Sahoo, Abinash; Ghose, Dillip K.
通讯作者	Samantaray, Sandeep
来源期刊	KSCE JOURNAL OF CIVIL ENGINEERING
ISSN	1226-7988
EISSN	1976-3808
出版年	2020
卷号	24 期号:6 页码:1944-1957
英文摘要	Improvement in area of artificial intelligence for predicting different hydrological phenomenon has shaped an enormous alteration in predictions. Knowledge on suspended sediment load (SSL) is vital in managing water resources problems and safe guard environment. Present study evaluated accurateness of five soft computing techniques, i.e. radial basis function network (RBFN), cascade forward back propagation neural network (CFBPNN), support vector machine (SVM), integration of support vector machine with firefly algorithm (SVM-FFA) and phase space reconstruction (PSR) with SVM-FFA (PSR-SVM-FFA) approaches to estimate daily SSL in Salebhata, Suktel, Lant gauge stations in western part of Odisha, India. Performance of selected models were evaluated on basis of performance criterion namely root mean square error (RMSE), Nash-Sutcliffe (NSE), Wilton index (WI) for choosing best fit model. Results acquired verified that application of various neural network methods in present field of study showed fine concurrence with observed SSL values. Comparison of estimation accuracies of different methods exemplified that PSR-SVM-FFA is very precise to estimate SSL when compared with other models. Result shows that Suktel gauge station, the best value of WI is 0.978 for PSR-SVM-FFA model, while it is 0.959, 0.923, 0.885, and 0.842 for SVM-FFA, SVM, CFBPNN, RBFN models in testing phase. Moreover, cumulative SSL data calculated by PSR-SVM-FFA method are closer to observed data as compared to other methods.
英文关键词	PSR-SVM-FFA River basin Sediment SVM SVM-FFA
类型	Article
语种	英语
国家	India
收录类别	SCI-E
WOS记录号	WOS:000528430200005
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; MULTILAYER PERCEPTRON ; PREDICTION ; ANN ; WAVELET ; MODEL ; TRANSPORT ; RIVER ; ALGORITHM ; YIELD
WOS类目	Engineering, Civil
WOS研究方向	Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315117
作者单位	NIT Silchar, Dept Civil Engn, Silchar 788010, Assam, India
推荐引用方式 GB/T 7714	Samantaray, Sandeep,Sahoo, Abinash,Ghose, Dillip K.. Assessment of Sediment Load Concentration Using SVM, SVM-FFA and PSR-SVM-FFA in Arid Watershed, India: A Case Study[J],2020,24(6):1944-1957.
APA	Samantaray, Sandeep,Sahoo, Abinash,&Ghose, Dillip K..(2020).Assessment of Sediment Load Concentration Using SVM, SVM-FFA and PSR-SVM-FFA in Arid Watershed, India: A Case Study.KSCE JOURNAL OF CIVIL ENGINEERING,24(6),1944-1957.
MLA	Samantaray, Sandeep,et al."Assessment of Sediment Load Concentration Using SVM, SVM-FFA and PSR-SVM-FFA in Arid Watershed, India: A Case Study".KSCE JOURNAL OF CIVIL ENGINEERING 24.6(2020):1944-1957.