Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

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buir.contributor.authorArashloo, Shervin Rahimzadeh
dc.citation.epage125014-1en_US
dc.citation.spage125014-12en_US
dc.citation.volumeNumber587en_US
dc.contributor.authorSafari, M. J. S.en_US
dc.contributor.authorArashloo, Shervin Rahimzadehen_US
dc.contributor.authorMehr, A. D.en_US
dc.date.accessioned2021-02-25T06:28:08Z
dc.date.available2021-02-25T06:28:08Z
dc.date.issued2020
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractIn this study, Regression in the Reproducing Kernel Hilbert Space (RRKHS) technique which is a non-linear regression approach formulated in the reproducing kernel Hilbert space (RRKHS) is applied for rainfall-runoff (R-R) modeling for the first time. The RRKHS approach is commonly applied when the data to be modeled is highly non-linear, and consequently, the common linear approaches fail to provide satisfactory performance. The calibration and verification processes of the RRKHS for one- and multi-day ahead forecasting R-R models were demonstrated using daily rainfall and streamflow measurement from a mountainous catchment located in the Black Sea region, Turkey. The efficacy of the new approach in each forecasting scenario was compared with those of other benchmarks, namely radial basis function artificial neural network and multivariate adaptive regression splines. The results illustrate the superiority of the RRKHS approach to its counterparts in terms of different performance indices. The range of relative peak error (PE) is found as 0.009–0.299 for the best scenario of the RRKHS model, which illustrates the high accuracy of RRKHS in peak streamflow estimation. The superior performance of the RRKHS model may be attributed to its formulation in a very high (possibly infinite) dimensional space which facilitates a more accurate regression analysis. Based on the promising results of the current study, it is expected that the proposed approach would be applied to other similar environmental modeling problems.en_US
dc.description.provenanceSubmitted by Onur Emek (onur.emek@bilkent.edu.tr) on 2021-02-25T06:28:08Z No. of bitstreams: 1 Rainfall-runoff_modeling_through_regression_in_the_reproducing_kernel_Hilbert_space_algorithm.pdf: 1478583 bytes, checksum: 4de47103c90fc01892d98492fdaac510 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-02-25T06:28:08Z (GMT). No. of bitstreams: 1 Rainfall-runoff_modeling_through_regression_in_the_reproducing_kernel_Hilbert_space_algorithm.pdf: 1478583 bytes, checksum: 4de47103c90fc01892d98492fdaac510 (MD5) Previous issue date: 2020en
dc.embargo.release2022-08-01
dc.identifier.doi10.1016/j.jhydrol.2020.125014en_US
dc.identifier.issn0022-1694
dc.identifier.urihttp://hdl.handle.net/11693/75572
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttps://dx.doi.org/10.1016/j.jhydrol.2020.125014en_US
dc.source.titleJournal of Hydrologyen_US
dc.subjectRainfall-runoff modelingen_US
dc.subjectRegression in the reproducing kernel Hilbert spaceen_US
dc.subjectRadial basis functionen_US
dc.subjectMultivariate adaptive regression splinesen_US
dc.titleRainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithmen_US
dc.typeArticleen_US

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