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Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia

A genetic programming (GP)-based logistic regression method is proposed in the present study for the downscaling of extreme rainfall indices on the east coast of Peninsular Malaysia, which is considered one of the zones in Malaysia most vulnerable to climate change. A National Centre for Environment...

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Main Authors: Shahid, Shamsuddin, Harun, Sobri, Pour, Sahar Hadi
Format: Article
Language:English
Published: MDPI AG 2014
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/52970/1/SobriHarun2014_GeneticProgrammingfortheDownscalingofExtremeRainfall.pdf
http://eprints.utm.my/id/eprint/52970/
http://dx.doi.org/10.3390/atmos5040914
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spelling my.utm.529702018-07-19T07:22:29Z http://eprints.utm.my/id/eprint/52970/ Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia Shahid, Shamsuddin Harun, Sobri Pour, Sahar Hadi TA Engineering (General). Civil engineering (General) A genetic programming (GP)-based logistic regression method is proposed in the present study for the downscaling of extreme rainfall indices on the east coast of Peninsular Malaysia, which is considered one of the zones in Malaysia most vulnerable to climate change. A National Centre for Environmental Prediction reanalysis dataset at 42 grid points surrounding the study area was used to select the predictors. GP models were developed for the downscaling of three extreme rainfall indices: days with larger than or equal to the 90th percentile of rainfall during the north-east monsoon; consecutive wet days; and consecutive dry days in a year. Daily rainfall data for the time periods 1961-1990 and 1991-2000 were used for the calibration and validation of models, respectively. The results are compared with those obtained using the multilayer perceptron neural network (ANN) and linear regression-based statistical downscaling model (SDSM). It was found that models derived using GP can predict both annual and seasonal extreme rainfall indices more accurately compared to ANN and SDSM. MDPI AG 2014 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/52970/1/SobriHarun2014_GeneticProgrammingfortheDownscalingofExtremeRainfall.pdf Shahid, Shamsuddin and Harun, Sobri and Pour, Sahar Hadi (2014) Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia. Atmosphere, 5 (4). pp. 914-936. ISSN 2073-4433 http://dx.doi.org/10.3390/atmos5040914 DOI: 10.3390/atmos5040914
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Shahid, Shamsuddin
Harun, Sobri
Pour, Sahar Hadi
Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia
description A genetic programming (GP)-based logistic regression method is proposed in the present study for the downscaling of extreme rainfall indices on the east coast of Peninsular Malaysia, which is considered one of the zones in Malaysia most vulnerable to climate change. A National Centre for Environmental Prediction reanalysis dataset at 42 grid points surrounding the study area was used to select the predictors. GP models were developed for the downscaling of three extreme rainfall indices: days with larger than or equal to the 90th percentile of rainfall during the north-east monsoon; consecutive wet days; and consecutive dry days in a year. Daily rainfall data for the time periods 1961-1990 and 1991-2000 were used for the calibration and validation of models, respectively. The results are compared with those obtained using the multilayer perceptron neural network (ANN) and linear regression-based statistical downscaling model (SDSM). It was found that models derived using GP can predict both annual and seasonal extreme rainfall indices more accurately compared to ANN and SDSM.
format Article
author Shahid, Shamsuddin
Harun, Sobri
Pour, Sahar Hadi
author_facet Shahid, Shamsuddin
Harun, Sobri
Pour, Sahar Hadi
author_sort Shahid, Shamsuddin
title Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia
title_short Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia
title_full Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia
title_fullStr Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia
title_full_unstemmed Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia
title_sort genetic programming for the downscaling of extreme rainfall events on the east coast of peninsular malaysia
publisher MDPI AG
publishDate 2014
url http://eprints.utm.my/id/eprint/52970/1/SobriHarun2014_GeneticProgrammingfortheDownscalingofExtremeRainfall.pdf
http://eprints.utm.my/id/eprint/52970/
http://dx.doi.org/10.3390/atmos5040914
_version_ 1643653289704685568
score 13.211869

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