Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP)
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| Title: | Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP) |
|---|---|
| Authors: | Hashmi, Muhammad Z. mhas074@aucklanduni.ac.nz, Shamseldin, Asaad Y. a.shamseldin@auckland.ac.nz, Melville, Bruce W. b.melville@auckland.ac.nz |
| Source: | Environmental Modelling & Software. Dec2011, Vol. 26 Issue 12, p1639-1646. 8p. |
| Subjects: | Watersheds, Genetic programming, Precipitation (Chemistry), Quantitative research, Hydrology, Climate change, Regression analysis, Artificial neural networks |
| Geographic Terms: | New Zealand |
| Abstract: | Abstract: Investigation of hydrological impacts of climate change at the regional scale requires the use of a downscaling technique. Significant progress has already been made in the development of new statistical downscaling techniques. Statistical downscaling techniques involve the development of relationships between the large scale climatic parameters and local variables. When the local parameter is precipitation, these relationships are often very complex and may not be handled efficiently using linear regression. For this reason, a number of non-linear regression techniques and the use of Artificial Neural Networks (ANNs) was introduced. But due to the complexity and issues related to finding a global solution using ANN-based techniques, the Genetic Programming (GP) based techniques have surfaced as a potential better alternative. Compared to ANNs, GP based techniques can provide simpler and more efficient solutions but they have been rarely used for precipitation downscaling. This paper presents the results of statistical downscaling of precipitation data from the Clutha Watershed in New Zealand using a non-linear regression model developed by the authors using Gene Expression Programming (GEP), a variant of GP. The results show that GEP-based downscaling models can offer very simple and efficient solutions in the case of precipitation downscaling. [Copyright &y& Elsevier] |
| Copyright of Environmental Modelling & Software is the property of Elsevier B.V. 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 67326162 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP) – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hashmi%2C+Muhammad+Z%2E%22">Hashmi, Muhammad Z.</searchLink><i> mhas074@aucklanduni.ac.nz</i><br /><searchLink fieldCode="AR" term="%22Shamseldin%2C+Asaad+Y%2E%22">Shamseldin, Asaad Y.</searchLink><i> a.shamseldin@auckland.ac.nz</i><br /><searchLink fieldCode="AR" term="%22Melville%2C+Bruce+W%2E%22">Melville, Bruce W.</searchLink><i> b.melville@auckland.ac.nz</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Modelling+%26+Software%22">Environmental Modelling & Software</searchLink>. Dec2011, Vol. 26 Issue 12, p1639-1646. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+programming%22">Genetic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Precipitation+%28Chemistry%29%22">Precipitation (Chemistry)</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrology%22">Hydrology</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22New+Zealand%22">New Zealand</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: Investigation of hydrological impacts of climate change at the regional scale requires the use of a downscaling technique. Significant progress has already been made in the development of new statistical downscaling techniques. Statistical downscaling techniques involve the development of relationships between the large scale climatic parameters and local variables. When the local parameter is precipitation, these relationships are often very complex and may not be handled efficiently using linear regression. For this reason, a number of non-linear regression techniques and the use of Artificial Neural Networks (ANNs) was introduced. But due to the complexity and issues related to finding a global solution using ANN-based techniques, the Genetic Programming (GP) based techniques have surfaced as a potential better alternative. Compared to ANNs, GP based techniques can provide simpler and more efficient solutions but they have been rarely used for precipitation downscaling. This paper presents the results of statistical downscaling of precipitation data from the Clutha Watershed in New Zealand using a non-linear regression model developed by the authors using Gene Expression Programming (GEP), a variant of GP. The results show that GEP-based downscaling models can offer very simple and efficient solutions in the case of precipitation downscaling. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environmental Modelling & Software is the property of Elsevier B.V. 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: 10.1016/j.envsoft.2011.07.007 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1639 Subjects: – SubjectFull: Watersheds Type: general – SubjectFull: Genetic programming Type: general – SubjectFull: Precipitation (Chemistry) Type: general – SubjectFull: Quantitative research Type: general – SubjectFull: Hydrology Type: general – SubjectFull: Climate change Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: New Zealand Type: general Titles: – TitleFull: Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP) Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hashmi, Muhammad Z. – PersonEntity: Name: NameFull: Shamseldin, Asaad Y. – PersonEntity: Name: NameFull: Melville, Bruce W. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 13648152 Numbering: – Type: volume Value: 26 – Type: issue Value: 12 Titles: – TitleFull: Environmental Modelling & Software Type: main |
| ResultId | 1 |
