Downscaling Daily Temperature with Evolutionary Artificial Neural Networks

Min Shi

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Paper

Downscaling Daily Temperature with Evolutionary Artificial Neural Networks

Topics: Environmental Modeling; Neural Nets and Fuzzy Systems; Non-Linear Systems

In Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications SIMULTECH - Volume 1, 237-243, 2015 , Colmar, Alsace, France

Author: Min Shi

Affiliation: Norwegian Meteorological Institute, Norway

Keyword(s): Temperature, Downscaling, Artificial Neural Networks, Evolutionary Algorithms.

Related Ontology Subjects/Areas/Topics: Complex Systems Modeling and Simulation ; Environmental Modeling ; Formal Methods ; Neural Nets and Fuzzy Systems ; Non-Linear Systems ; Simulation and Modeling

Abstract: The spatial resolution of climate data generated by general circulation models (GCMs) is usually too coarse to present regional or local features and dynamics. State of the art research with Artificial Neural Networks (ANNs) for the downscaling of GCMs mainly uses back-propagation algorithm as a training approach. This paper applies another training approach of ANNs, Evolutionary Algorithm. The combined algorithm names neuroevolutionary (NE) algorithm. We investigate and evaluate the use of the NE algorithms in statistical downscaling by generating temperature estimates at interior points given information from a lattice of surrounding locations. The results of our experiments indicate that NE algorithms can be efficient alternative downscaling methods for daily temperatures.

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Paper citation in several formats:

Shi, M. (2015). Downscaling Daily Temperature with Evolutionary Artificial Neural Networks. In Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-120-5; ISSN 2184-2841, SciTePress, pages 237-243. DOI: 10.5220/0005507002370243

@conference{simultech15,
author={Min Shi.},
title={Downscaling Daily Temperature with Evolutionary Artificial Neural Networks},
booktitle={Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2015},
pages={237-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005507002370243},
isbn={978-989-758-120-5},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Downscaling Daily Temperature with Evolutionary Artificial Neural Networks
SN - 978-989-758-120-5
IS - 2184-2841
AU - Shi, M.
PY - 2015
SP - 237
EP - 243
DO - 10.5220/0005507002370243
PB - SciTePress