Simulation of Temperature and Precipitation under the Climate Change Scenarios
Keyword(s):
This study aims to discuss the potentials of machine learning methods such as artificial neural network (ANN), least squares support vector machine (LSSVM), and relevance vector machine (RVM) in downscaling of simulations of a general circulation model (GCM) for monthly temperature and precipitation of the Demirkopru Dam located in the Aegean region of Turkey. The predictors are obtained from ERA-Interim re-analysis data. The best performed downscaling model is integrated into European Centre Hamburg Model (ECHAM5) with A2 future scenario. The results are then discussed to assess the probable climate change effects on temperature and precipitation.
2014 ◽
Vol 17
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pp. 108-122
1998 ◽
Vol 22
(3)
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pp. 350-374
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2008 ◽
Vol 12
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pp. 449-463
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2017 ◽
Vol 9
(3)
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pp. 421-433
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