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Article: Rainfall data simulation by hidden Markov model and discrete wavelet transformation

TitleRainfall data simulation by hidden Markov model and discrete wavelet transformation
Authors
KeywordsDaily rainfall
Discrete wavelet transformation
Expectation-maximization algorithm
False nearest neighbours
Hidden Markov model
Phase space reconstruction
Issue Date2009
PublisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/00477/index.htm
Citation
Stochastic Environmental Research And Risk Assessment, 2009, v. 23 n. 7, p. 863-877 How to Cite?
AbstractIn many regions, monthly (or bimonthly) rainfall data can be considered as deterministic while daily rainfall data may be treated as random. As a result, deterministic models may not sufficiently fit the daily data because of the strong stochastic nature, while stochastic models may also not reliably fit into daily rainfall time series because of the deterministic nature at the large scale (i.e. coarse scale). Although there are different approaches for simulating daily rainfall, mixing of deterministic and stochastic models (towards possible representation of both deterministic and stochastic properties) has not hitherto been proposed. An attempt is made in this study to simulate daily rainfall data by utilizing discrete wavelet transformation and hidden Markov model. We use a deterministic model to obtain large-scale data, and a stochastic model to simulate the wavelet tree coefficients. The simulated daily rainfall is obtained by inverse transformation. We then compare the accumulated simulated and accumulated observed data from the Chao Phraya Basin in Thailand. Because of the stochastic nature at the small scale, the simulated daily rainfall on a point to point comparison show deviations with the observed data. However the accumulated simulated data do show some level of agreement with the observed data. © Springer-Verlag 2008.
Persistent Identifierhttp://hdl.handle.net/10722/75568
ISSN
2015 Impact Factor: 2.237
2015 SCImago Journal Rankings: 1.065
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorJayawardena, AWen_HK
dc.contributor.authorXu, PCen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T07:12:27Z-
dc.date.available2010-09-06T07:12:27Z-
dc.date.issued2009en_HK
dc.identifier.citationStochastic Environmental Research And Risk Assessment, 2009, v. 23 n. 7, p. 863-877en_HK
dc.identifier.issn1436-3240en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75568-
dc.description.abstractIn many regions, monthly (or bimonthly) rainfall data can be considered as deterministic while daily rainfall data may be treated as random. As a result, deterministic models may not sufficiently fit the daily data because of the strong stochastic nature, while stochastic models may also not reliably fit into daily rainfall time series because of the deterministic nature at the large scale (i.e. coarse scale). Although there are different approaches for simulating daily rainfall, mixing of deterministic and stochastic models (towards possible representation of both deterministic and stochastic properties) has not hitherto been proposed. An attempt is made in this study to simulate daily rainfall data by utilizing discrete wavelet transformation and hidden Markov model. We use a deterministic model to obtain large-scale data, and a stochastic model to simulate the wavelet tree coefficients. The simulated daily rainfall is obtained by inverse transformation. We then compare the accumulated simulated and accumulated observed data from the Chao Phraya Basin in Thailand. Because of the stochastic nature at the small scale, the simulated daily rainfall on a point to point comparison show deviations with the observed data. However the accumulated simulated data do show some level of agreement with the observed data. © Springer-Verlag 2008.en_HK
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/00477/index.htmen_HK
dc.relation.ispartofStochastic Environmental Research and Risk Assessmenten_HK
dc.rightsThe original publication is available at www.springerlink.com-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectDaily rainfallen_HK
dc.subjectDiscrete wavelet transformationen_HK
dc.subjectExpectation-maximization algorithmen_HK
dc.subjectFalse nearest neighboursen_HK
dc.subjectHidden Markov modelen_HK
dc.subjectPhase space reconstructionen_HK
dc.titleRainfall data simulation by hidden Markov model and discrete wavelet transformationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1436-3240&volume=23&issue=7&spage=863&epage=877&date=2009&atitle=Rainfall+data+simulation+by+hidden+Markov+Model+and+discrete+wavelet+transfomation-
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1007/s00477-008-0264-0en_HK
dc.identifier.scopuseid_2-s2.0-70349757027en_HK
dc.identifier.hkuros170555-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70349757027&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume23en_HK
dc.identifier.issue7en_HK
dc.identifier.spage863en_HK
dc.identifier.epage877en_HK
dc.identifier.eissn1436-3259-
dc.identifier.isiWOS:000270291800002-
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridJayawardena, AW=7005049253en_HK
dc.identifier.scopusauthoridXu, PC=8440784800en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.citeulike3632857-

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