Statistical downscaling of precipitation using machine learning techniques
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gp-bibliography.bib Revision:1.8098
- @Article{SACHINDRA:2018:AR,
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author = "D. A. Sachindra and K. Ahmed and
Md. Mamunur Rashid and S. Shahid and B. J. C. Perera",
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title = "Statistical downscaling of precipitation using machine
learning techniques",
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journal = "Atmospheric Research",
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volume = "212",
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pages = "240--258",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Statistical
downscaling, Machine learning, Precipitation,
Australia, Floods, Droughts",
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ISSN = "0169-8095",
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DOI = "doi:10.1016/j.atmosres.2018.05.022",
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URL = "http://www.sciencedirect.com/science/article/pii/S0169809517310141",
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abstract = "Statistical models were developed for downscaling
reanalysis data to monthly precipitation at 48
observation stations scattered across the Australian
State of Victoria belonging to wet, intermediate and
dry climate regimes. Downscaling models were calibrated
over the period 1950-1991 and validated over the period
1992-2014 for each calendar month, for each station,
using 4 machine learning techniques, (1) Genetic
Programming (GP), (2) Artificial Neural Networks
(ANNs), (3) Support Vector Machine (SVM), and (4)
Relevance Vector Machine (RVM). It was found that,
irrespective of the climate regime and the machine
learning technique, downscaling models tend to better
simulate the average (compared to other statistics) and
under-estimate the standard deviation and the maximum
of the observed precipitation. Also, irrespective of
the climate regime and the machine learning technique,
at the majority of stations downscaling models showed
an over-estimating trend of low to mid percentiles
(i.e. below the 50th percentile) of precipitation and
under-estimating trend of high percentiles of
precipitation (i.e. above the 90th percentile). The
over-estimating trend of low to mid percentiles of
precipitation was more pronounced at stations located
in dryer climate, irrespective of the machine learning
technique. Based on the results of this investigation
the use of RVM or ANN over SVM or GP for developing
downscaling models can be recommended for a study such
as flood prediction which involves the consideration of
high extremes of precipitation. Also, RVM can be
recommended over GP, ANN or SVM in developing
downscaling models for a study such as drought analysis
which involves the consideration of low extremes of
precipitation. Furthermore, it was found that
irrespective of the climate regime, the SVM and
RVM-based precipitation downscaling models showed the
best performance with the Polynomial kernel",
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keywords = "genetic algorithms, genetic programming, Statistical
downscaling, Machine learning, Precipitation,
Australia, Floods, Droughts",
- }
Genetic Programming entries for
D A Sachindra
K Ahmed
Mohammad Mamunur Rashid
Shamsuddin Shahid
Chris Perera
Citations