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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/86344
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dc.contributor.authorLi, X.-
dc.contributor.authorZecchin, A.-
dc.contributor.authorMaier, H.-
dc.date.issued2014-
dc.identifier.citationEnvironmental Modelling and Software, 2014; 59:162-186-
dc.identifier.issn1364-8152-
dc.identifier.issn1873-6726-
dc.identifier.urihttp://hdl.handle.net/2440/86344-
dc.description.abstractAbstract not available-
dc.description.statementofresponsibilityXuyuan Li, Aaron C. Zecchin, Holger R. Maier-
dc.language.isoen-
dc.publisherElsevier-
dc.rights© 2014 Elsevier Ltd. All rights reserved.-
dc.source.urihttp://dx.doi.org/10.1016/j.envsoft.2014.05.010-
dc.subjectGeneral regression neural networks; Smoothing parameter estimators; Artificial neural networks; Multi-layer perceptrons; Extreme and average events; Hydrology and water resources-
dc.titleSelection of smoothing parameter estimators for general regression neural networks - applications to hydrological and water resources modelling-
dc.typeJournal article-
dc.identifier.doi10.1016/j.envsoft.2014.05.010-
pubs.publication-statusPublished-
dc.identifier.orcidZecchin, A. [0000-0001-8908-7023]-
dc.identifier.orcidMaier, H. [0000-0002-0277-6887]-
Appears in Collections:Aurora harvest 2
Civil and Environmental Engineering publications

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