A wavelet based approach for combining the outputs of different rainfall-runoff models.
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| Title: | A wavelet based approach for combining the outputs of different rainfall-runoff models. |
|---|---|
| Authors: | Shoaib, Muhammad1 msho127@aucklanduni.ac.nz, Shamseldin, Asaad Y.2 a.shamseldin@auckland.ac.nz, Khan, Sher2 skha121@aucklanduni.ac.nz, Khan, Mudasser Muneer1 mudasser.muneer@gmail.com, Khan, Zahid Mahmood1 zahidmk@bzu.edu.pk, Melville, Bruce W.2 b.melville@auckland.ac.nz |
| Source: | Stochastic Environmental Research & Risk Assessment. Jan2018, Vol. 32 Issue 1, p155-168. 14p. |
| Subjects: | Rainfall frequencies, Mathematical models of hydrodynamics, Runoff, Runoff analysis, Stochastic analysis, Wavelets (Mathematics), Hydrologic models |
| Abstract: | The rainfall-runoff modelling being a stochastic process in nature is dependent on various climatological variables and catchment characteristics and therefore numerous hydrological models have been developed to simulate this complex process. One approach to modelling this complex non-linear rainfall-runoff process is to combine the outputs of various models to get more accurate and reliable results. This multi-model combination approach relies on the fact that various models capture different features of the data, and hence combination of these features would yield better result. This study for the first time presented a novel wavelet based combination approach for estimating combined runoff The simulated daily output (Runoff) of five selected conventional rainfall-runoff models from seven different catchments located in different parts of the world was used in current study for estimating combined runoff for each time period. Five selected rainfall-runoff models used in this study included four data driven models, namely, the simple linear model, the linear perturbation model, the linearly varying variable gain factor model, the constrained linear systems with a single threshold and one conceptual model, namely, the soil moisture accounting and routing model. The multilayer perceptron neural network method was used to develop combined wavelet coupled models to evaluate the effect of wavelet transformation (WT). The performance of the developed wavelet coupled combination models was compared with their counterpart simple combination models developed without WT. It was concluded that the presented wavelet coupled combination approach outperformed the existing approaches of combining different models without applying input WT. The study also recommended that different models in a combination approach should be selected on the basis of their individual performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 127393953 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A wavelet based approach for combining the outputs of different rainfall-runoff models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shoaib%2C+Muhammad%22">Shoaib, Muhammad</searchLink><relatesTo>1</relatesTo><i> msho127@aucklanduni.ac.nz</i><br /><searchLink fieldCode="AR" term="%22Shamseldin%2C+Asaad+Y%2E%22">Shamseldin, Asaad Y.</searchLink><relatesTo>2</relatesTo><i> a.shamseldin@auckland.ac.nz</i><br /><searchLink fieldCode="AR" term="%22Khan%2C+Sher%22">Khan, Sher</searchLink><relatesTo>2</relatesTo><i> skha121@aucklanduni.ac.nz</i><br /><searchLink fieldCode="AR" term="%22Khan%2C+Mudasser+Muneer%22">Khan, Mudasser Muneer</searchLink><relatesTo>1</relatesTo><i> mudasser.muneer@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Khan%2C+Zahid+Mahmood%22">Khan, Zahid Mahmood</searchLink><relatesTo>1</relatesTo><i> zahidmk@bzu.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Melville%2C+Bruce+W%2E%22">Melville, Bruce W.</searchLink><relatesTo>2</relatesTo><i> b.melville@auckland.ac.nz</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Stochastic+Environmental+Research+%26+Risk+Assessment%22">Stochastic Environmental Research & Risk Assessment</searchLink>. Jan2018, Vol. 32 Issue 1, p155-168. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Rainfall+frequencies%22">Rainfall frequencies</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models+of+hydrodynamics%22">Mathematical models of hydrodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Runoff%22">Runoff</searchLink><br /><searchLink fieldCode="DE" term="%22Runoff+analysis%22">Runoff analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+analysis%22">Stochastic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelets+%28Mathematics%29%22">Wavelets (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrologic+models%22">Hydrologic models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The rainfall-runoff modelling being a stochastic process in nature is dependent on various climatological variables and catchment characteristics and therefore numerous hydrological models have been developed to simulate this complex process. One approach to modelling this complex non-linear rainfall-runoff process is to combine the outputs of various models to get more accurate and reliable results. This multi-model combination approach relies on the fact that various models capture different features of the data, and hence combination of these features would yield better result. This study for the first time presented a novel wavelet based combination approach for estimating combined runoff The simulated daily output (Runoff) of five selected conventional rainfall-runoff models from seven different catchments located in different parts of the world was used in current study for estimating combined runoff for each time period. Five selected rainfall-runoff models used in this study included four data driven models, namely, the simple linear model, the linear perturbation model, the linearly varying variable gain factor model, the constrained linear systems with a single threshold and one conceptual model, namely, the soil moisture accounting and routing model. The multilayer perceptron neural network method was used to develop combined wavelet coupled models to evaluate the effect of wavelet transformation (WT). The performance of the developed wavelet coupled combination models was compared with their counterpart simple combination models developed without WT. It was concluded that the presented wavelet coupled combination approach outperformed the existing approaches of combining different models without applying input WT. The study also recommended that different models in a combination approach should be selected on the basis of their individual performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00477-016-1364-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 155 Subjects: – SubjectFull: Rainfall frequencies Type: general – SubjectFull: Mathematical models of hydrodynamics Type: general – SubjectFull: Runoff Type: general – SubjectFull: Runoff analysis Type: general – SubjectFull: Stochastic analysis Type: general – SubjectFull: Wavelets (Mathematics) Type: general – SubjectFull: Hydrologic models Type: general Titles: – TitleFull: A wavelet based approach for combining the outputs of different rainfall-runoff models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shoaib, Muhammad – PersonEntity: Name: NameFull: Shamseldin, Asaad Y. – PersonEntity: Name: NameFull: Khan, Sher – PersonEntity: Name: NameFull: Khan, Mudasser Muneer – PersonEntity: Name: NameFull: Khan, Zahid Mahmood – PersonEntity: Name: NameFull: Melville, Bruce W. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 14363240 Numbering: – Type: volume Value: 32 – Type: issue Value: 1 Titles: – TitleFull: Stochastic Environmental Research & Risk Assessment Type: main |
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