Global Surface Temperature Model using Coupled Sugeno Type Fuzzy Inference Systems and Neural Network Optimization

Bernardo Bastien-Olvera, Carlos Gay-Garcia

2015

Abstract

In this research, a model that projects the mean global temperature as a function of anthropogenic carbon emissions was generated with two fuzzy inference systems, sugeno type. We propose that the climatic system is energetically balanced, and the albedo, solar constant and atmospheric transparency are all constants. Nevertheless, we assume that the surface temperature varies when the CO2 concentration changes and depends on the system temperature itself. The second assertion states that any change in atmospheric CO2 concentration depends on anthropogenic carbon emissions and the system actual concentration. The fuzzy inference systems were optimized using artificial neural networks that adjust the parameters according to a different data base that the one that was used to create the initial system. So that, we assure to find the hidden patterns and avoid overfitting. The principal results of this work are the temperature projections under IPCC scenarios and the discovering of the historical data hidden patterns.

Paper Citation

in Harvard Style

Bastien-Olvera B. and Gay-Garcia C. (2015). Global Surface Temperature Model using Coupled Sugeno Type Fuzzy Inference Systems and Neural Network Optimization . In Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: MSCCES, (SIMULTECH 2015) ISBN 978-989-758-120-5, pages 519-525

in Bibtex Style

@conference{mscces15,
author={Bernardo Bastien-Olvera and Carlos Gay-Garcia},
title={Global Surface Temperature Model using Coupled Sugeno Type Fuzzy Inference Systems and Neural Network Optimization},
booktitle={Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: MSCCES, (SIMULTECH 2015)},
year={2015},
pages={519-525},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={978-989-758-120-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: MSCCES, (SIMULTECH 2015)
TI - Global Surface Temperature Model using Coupled Sugeno Type Fuzzy Inference Systems and Neural Network Optimization
SN - 978-989-758-120-5
AU - Bastien-Olvera B.
AU - Gay-Garcia C.
PY - 2015
SP - 519
EP - 525
DO -