Simulation of temperature and precipitation under the climate change scenarios: Integration of a GCM and machine learning approaches

Umut Okkan; Gul Inan

doi:10.4018/978-1-5225-1759-7.ch043

Simulation of temperature and precipitation under the climate change scenarios: Integration of a GCM and machine learning approaches

Umut Okkan^*, Gul Inan

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Citation (Scopus)

Abstract

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 ERAInterim 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.

Original language	English
Title of host publication	Artificial Intelligence
Subtitle of host publication	Concepts, Methodologies, Tools, and Applications
Publisher	IGI Global
Pages	1065-1091
Number of pages	27
Volume	2
ISBN (Electronic)	9781522517603
ISBN (Print)	1522517596, 9781522517597
DOIs	https://doi.org/10.4018/978-1-5225-1759-7.ch043
Publication status	Published - 12 Dec 2016
Externally published	Yes

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© 2017 by IGI Global. All rights reserved.

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10.4018/978-1-5225-1759-7.ch043

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Simulation of temperature and precipitation under the climate change scenarios: Integration of a GCM and machine learning approaches

Abstract

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