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

•Regression in the Reproducing Kernel Hilbert Space (RRKHS) is implemented.•The RRKHS is introduced to model rainfall-runoff process for the first time.•The pseudo-code algorithmic of the RRKHS is presented.•The RRKHS is found superior to its alternatives. In this study, Regression in the Reproducin...

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Vydané v:Journal of hydrology (Amsterdam) Ročník 587; s. 125014
Hlavní autori: Safari, Mir Jafar Sadegh, Rahimzadeh Arashloo, Shervin, Danandeh Mehr, Ali
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.08.2020
Predmet:
algorithms
Black Sea
calibration
mountains
Multivariate adaptive regression splines
neural networks
Radial basis function
rain
Rainfall-runoff modeling
regression analysis
Regression in the reproducing kernel Hilbert space
runoff
stream flow
watersheds
Black Sea
Radial basis function
Multivariate adaptive regression splines
Rainfall-runoff modeling
Regression in the reproducing kernel Hilbert space
ISSN:0022-1694, 1879-2707
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Shrnutí:•Regression in the Reproducing Kernel Hilbert Space (RRKHS) is implemented.•The RRKHS is introduced to model rainfall-runoff process for the first time.•The pseudo-code algorithmic of the RRKHS is presented.•The RRKHS is found superior to its alternatives. In 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.
Bibliografia:ObjectType-Article-1
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ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2020.125014