Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/2381
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dc.contributor.authorOyebode, Oluwaseun Kunle-
dc.contributor.authorOtieno, Fredrick Alfred O.-
dc.contributor.authorAdeyemo, Josiah-
dc.date.accessioned2017-03-13T09:23:10Z-
dc.date.available2017-03-13T09:23:10Z-
dc.date.issued2014-
dc.identifier.citationOyebode, O., Otieno, F. and Adeyemo, J. 2014. Review of three data- driven modelling techniques for hydrological modelling and forecasting. Fresenius Environmental Bulletin 23(7):1443-1454.en_US
dc.identifier.issn1018-4619-
dc.identifier.urihttp://hdl.handle.net/10321/2381-
dc.description.abstractVarious modelling techniques have been proposed and applied for modelling and forecasting of hydrological sys-tems in different studies. These modelling techniques are majorly categorized into two namely, process-based and data-driven modelling techniques. While the process-based techniques provides detailed description of hydro-logical processes, data-driven techniques however de-scribe the behaviour of hydrological processes by taking into account only limited assumptions about the underly-ing physics of the system being modelled. Although, process-based techniques have been widely applied in numerous hydrological modelling studies, the application of data-driven modelling techniques on the other hand has not been fully embraced in the hydrological domain. This paper provides a comprehensive review of several stud-ies relating to three data-driven modelling techniques namely, K-Nearest Neighbours (K-NN), Model Trees (MTs) and Fuzzy Rule-Based Systems (FRBS). Modern trends with respect to their applications in hydrological model-ling and forecasting studies are also discussed. The struc-ture of this review encapsulates an introduction to each of the modelling techniques, their applications in hydrological modelling and forecasting, identification of areas of con-cern in their use, performance improvement methods, as well as summary of their advantages and disadvantages. The review aims to make a case for the application of data-driven modelling techniques by discussing the benefits em-bedded in its integration into water resources applications.en_US
dc.format.extent12 pen_US
dc.language.isoenen_US
dc.publisherPSPen_US
dc.relation.ispartofFresenius environmental bulletin-
dc.subjectData-driven modelsen_US
dc.subjectFuzzy rule-based systemsen_US
dc.subjectHydrological mod-elling and forecastingen_US
dc.subjectK-nearest neighboursen_US
dc.subjectModel treesen_US
dc.titleReview of three data- driven modelling techniques for hydrological modelling and forecastingen_US
dc.typeArticleen_US
dc.dut-rims.pubnumDUT-004957en_US
local.sdgSDG06-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextWith Fulltext-
Appears in Collections:Research Publications (Engineering and Built Environment)
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