Genetic programming-based ordinary Kriging for spatial interpolation of rainfall
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- @Article{vu29881,
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author = "Sajal Kumar Adhikary and Nitin Muttil and
Abdullah Yilmaz",
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title = "Genetic programming-based ordinary Kriging for spatial
interpolation of rainfall",
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journal = "Journal of Hydrologic Engineering",
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year = "2016",
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volume = "21",
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number = "2",
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month = feb,
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keywords = "genetic algorithms, genetic programming, rainfall
data, management of water resource systems, missing
values, programming",
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publisher = "American Society of Civil Engineers",
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URL = "https://vuir.vu.edu.au/29881/",
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URL = "https://ascelibrary.org/doi/10.1061/%28ASCE%29HE.1943-5584.0001300",
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DOI = "doi:10.1061/(ASCE)HE.1943-5584.0001300",
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abstract = "Rainfall data provide an essential input for most
hydrologic analyses and designs for effective
management of water resource systems. However, in
practice, missing values often occur in rainfall data
that can ultimately influence the results of hydrologic
analysis and design. Conventionally, stochastic
interpolation methods such as Kriging are the most
frequently used approach to estimate the missing
rainfall values where the variogram model that
represents spatial correlations among data points plays
a vital role and significantly impacts the performance
of the methods. In the past, the standard variogram
models in ordinary kriging were replaced with the
universal function approximator-based variogram models,
such as artificial neural networks (ANN). In the
current study, applicability of genetic programming
(GP) to derive the variogram model and use of this
GP-derived variogram model within ordinary kriging for
spatial interpolation was investigated. Developed
genetic programming-based ordinary kriging (GPOK) was
then applied for estimating the missing rainfall data
at a rain gauge station using the historical rainfall
data from 19 rain gauge stations in the Middle Yarra
River catchment of Victoria, Australia. The results
indicated that the proposed GPOK method outperformed
the traditional ordinary kriging as well as the
ANN-based ordinary kriging method for spatial
interpolation of rainfall. Moreover, the GP-derived
variogram model is shown to have advantages over the
standard and ANN-derived variogram models. Therefore,
the GP-derived variogram model seems to be a potential
alternative to variogram models applied in the past and
the proposed GPOK method is recommended as a viable
option for spatial interpolation.",
- }
Genetic Programming entries for
Sajal Kumar Adhikary
Nitin Muttil
Abdullah Gokhan Yilmaz
Citations