File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis

TitleRainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis
Authors
KeywordsArtificial Neural Network
Modular Model
Prediction
Rainfall And Runoff
Singular Spectrum Analysis
Issue Date2011
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jhydrol
Citation
Journal Of Hydrology, 2011, v. 399 n. 3-4, p. 394-409 How to Cite?
AbstractAccurately modeling rainfall-runoff (R-R) transform remains a challenging task despite that a wide range of modeling techniques, either knowledge-driven or data-driven, have been developed in the past several decades. Amongst data-driven models, artificial neural network (ANN)-based R-R models have received great attentions in hydrology community owing to their capability to reproduce the highly nonlinear nature of the relationship between hydrological variables. However, a lagged prediction effect often appears in the ANN modeling process. This paper attempts to eliminate the lag effect from two aspects: modular artificial neural network (MANN) and data preprocessing by singular spectrum analysis (SSA). Two watersheds from China are explored with daily collected data. Results show that MANN does not exhibit significant advantages over ANN. However, it is demonstrated that SSA can considerably improve the performance of prediction model and eliminate the lag effect. Moreover, ANN or MANN with antecedent runoff only as model input is also developed and compared with the ANN (or MANN) R-R model. At all three prediction horizons, the latter outperforms the former regardless of being coupled with/without SSA. It is recommended from the present study that the ANN R-R model coupled with SSA is more promisings. © 2011 Elsevier B.V.
Persistent Identifierhttp://hdl.handle.net/10722/168781
ISSN
2015 Impact Factor: 3.043
2015 SCImago Journal Rankings: 1.743
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWu, CLen_US
dc.contributor.authorChau, KWen_US
dc.date.accessioned2012-10-08T03:32:22Z-
dc.date.available2012-10-08T03:32:22Z-
dc.date.issued2011en_US
dc.identifier.citationJournal Of Hydrology, 2011, v. 399 n. 3-4, p. 394-409en_US
dc.identifier.issn0022-1694en_US
dc.identifier.urihttp://hdl.handle.net/10722/168781-
dc.description.abstractAccurately modeling rainfall-runoff (R-R) transform remains a challenging task despite that a wide range of modeling techniques, either knowledge-driven or data-driven, have been developed in the past several decades. Amongst data-driven models, artificial neural network (ANN)-based R-R models have received great attentions in hydrology community owing to their capability to reproduce the highly nonlinear nature of the relationship between hydrological variables. However, a lagged prediction effect often appears in the ANN modeling process. This paper attempts to eliminate the lag effect from two aspects: modular artificial neural network (MANN) and data preprocessing by singular spectrum analysis (SSA). Two watersheds from China are explored with daily collected data. Results show that MANN does not exhibit significant advantages over ANN. However, it is demonstrated that SSA can considerably improve the performance of prediction model and eliminate the lag effect. Moreover, ANN or MANN with antecedent runoff only as model input is also developed and compared with the ANN (or MANN) R-R model. At all three prediction horizons, the latter outperforms the former regardless of being coupled with/without SSA. It is recommended from the present study that the ANN R-R model coupled with SSA is more promisings. © 2011 Elsevier B.V.en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jhydrolen_US
dc.relation.ispartofJournal of Hydrologyen_US
dc.subjectArtificial Neural Networken_US
dc.subjectModular Modelen_US
dc.subjectPredictionen_US
dc.subjectRainfall And Runoffen_US
dc.subjectSingular Spectrum Analysisen_US
dc.titleRainfall-runoff modeling using artificial neural network coupled with singular spectrum analysisen_US
dc.typeArticleen_US
dc.identifier.emailChau, KW:hrrbckw@hkucc.hku.hken_US
dc.identifier.authorityChau, KW=rp00993en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.jhydrol.2011.01.017en_US
dc.identifier.scopuseid_2-s2.0-79952006341en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952006341&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume399en_US
dc.identifier.issue3-4en_US
dc.identifier.spage394en_US
dc.identifier.epage409en_US
dc.identifier.isiWOS:000288828500023-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridWu, CL=8301716400en_US
dc.identifier.scopusauthoridChau, KW=24830082500en_US
dc.identifier.citeulike8742436-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats