edu.ucar.opensky::technotes:587 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T23:11:04.340302 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7df6vng Optimizing Genetic Algorithm Parameters for Atmospheric Carbon Monoxide Modeling 2021-06-01T00:00:00Z publication https://n2t.org/ark:/85065/d7df6vng https://doi.org/10.5065/h45f-c987 Duggal, M. author Daniels, W. author Hammerling, D. author Buchholz, Rebecca author UCAR/NCAR - Library publisher A main source of atmospheric carbon monoxide (CO) variability in the Southern Hemisphere is large burn events, making CO a useful proxy for fires. Therefore, predictive CO models over fire regions can help countries prepare for unusually large fire seasons. Fires are related to the climate through fuel dryness and availability, both of which respond to variability in the climate. Climate indices are metrics that summarize climate variability through changes in sea surface temperature and wind. In previous work, we developed a multiple linear regression model that uses these climate indices to predict atmospheric CO and created the R package regClimateChem to perform variable selection. This package offers three different variable selection techniques: stepwise selection, a genetic algorithm, and an exhaustive search. The exhaustive search always finds the best possible model but is computationally expensive. Stepwise selection runs quickly and is scalable but often fails to find the best model. We implement a genetic algorithm as a potential compromise between computational expense and model accuracy. As a stochastic variable selection technique, the genetic algorithm has many parameters that affect the stopping criterion, frequency of the model modification techniques, and population size. Here we present a parameter optimization study for the genetic algorithm, seeking to balance computational expense and model quality. When considering models with four covariates, we find that the optimized genetic algorithm parameters result in a runtime reduction of 11.8% and only compromise 0.3% accuracy compared to the default settings. We then consider models with five covariates using a high-performance computing system. For models with five covariates, we find that the optimized population size becomes close to the total number of models, meaning it behaves similarly to the exhaustive method UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Carbon monoxide Atmospheric carbon monoxide Biomass burning Statistical applications theme U.S. National Aeronautics and Space Administration Global Change Master Directory GCMD 2021-09-17 revision 11.2 NASA Global Change Master Directory originator Valids List: Science and Services Keywords Copyright Author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation