Simulation of Temperature and Precipitation under the Climate Change Scenarios: Integration of a GCM and Machine Learning Approaches

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Author(s): Umut Okkan (Balikesir University, Turkey)and Gul Inan (Middle East Technical University, Turkey)
Copyright: 2017
Pages: 27
Source title: Artificial Intelligence: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-1759-7.ch043

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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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 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.

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Simulation of Temperature and Precipitation under the Climate Change Scenarios: Integration of a GCM and Machine Learning Approaches

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