doi: 10.18178/ijesd.2018.9.10.1117
Evaluation of Artificial Neural Networks and Eddy Covariance Measurements for Modelling the CO<sub>2</sub> Flux Dynamics in the Acoculco Geothermal Caldera (Mexico)
E. Santoyo1
,
A. Acevedo-Anicasio2 , ,
- 1the Institute for Renewable Energy
- 2the PhD Programme of Computational
- 3the Institute of Geophysics (UNAM), CONACyT
Abstract
The aim of this research work is to report the CO<sub>2</sub> measurements of a short-term period carried out in the geothermal zone of Acoculco, Puebla (Mexico). CO<sub>2</sub> measurements were logged using a micrometeorological station Eddy Covariance. According to the complex geochemical phenomena involved in these measurements, artificial neural networks have been used for reproducing the CO<sub>2</sub> measurements, and to fill the gap issues during the monitoring period. This survey was also performed for the identification of CO<sub>2</sub> anomalies in the zone, and also to determine the natural emission baseline of CO<sub>2</sub> at the early exploration stage of this promissory geothermal system. Details of this evaluation study are outlined.
Keywords
- Enhanced geothermal systems
- environmental sustainability
- geothermal energy
- hot-dry rock
- soil-gas emission
How to Cite
E. Santoyo, A. Acevedo-Anicasio, D. Perez-Zarate, and M. Guevara, "Evaluation of Artificial Neural Networks and Eddy Covariance Measurements for Modelling the CO<sub>2</sub> Flux Dynamics in the Acoculco Geothermal Caldera (Mexico)," International Journal of Environmental Science and Development, vol. 9, no. 10, pp. 298-302, 2018. https://doi.org/10.18178/ijesd.2018.9.10.1117
Copyright & License
Copyright © 2018 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).