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dc.contributor.authorWang, Den
dc.contributor.authorBorthwick, AGen
dc.contributor.authorHe, Hen
dc.contributor.authorWang, Yen
dc.contributor.authorZhu, Jen
dc.contributor.authorLu, Yen
dc.contributor.authorXu, Pen
dc.contributor.authorZeng, Xen
dc.contributor.authorWu, Jen
dc.contributor.authorWang, Len
dc.contributor.authorZou, Xen
dc.contributor.authorLiu, Jen
dc.contributor.authorZou, Yen
dc.contributor.authorHe, Ren
dc.date.accessioned2021-08-22T16:35:28Z
dc.date.available2021-08-22T16:35:28Z
dc.date.issued2018-01en
dc.identifier.urihttp://hdl.handle.net/10026.1/17701
dc.description.abstract

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

en
dc.format.extent269 - 281en
dc.languageengen
dc.language.isoengen
dc.subjectData-driven modelen
dc.subjectForecastingen
dc.subjectHydro-meteorological seriesen
dc.subjectRank-Set Pair Analysisen
dc.subjectWavelet de-noisingen
dc.subjectChinaen
dc.subjectDroughtsen
dc.subjectFloodsen
dc.subjectForecastingen
dc.subjectRiversen
dc.subjectWavelet Analysisen
dc.titleA hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series.en
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/29032311en
plymouth.volume160en
plymouth.publication-statusPublisheden
plymouth.journalEnviron Resen
dc.identifier.doi10.1016/j.envres.2017.09.033en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.placeNetherlandsen
dcterms.dateAccepted2017-09-29en
dc.identifier.eissn1096-0953en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1016/j.envres.2017.09.033en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-01en
rioxxterms.typeJournal Article/Reviewen


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