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dc.contributor.authorDuggal, Meera
dc.contributor.authorHammerling, Dorit
dc.date2021
dc.date.accessioned2023-11-21T23:42:46Z
dc.date.available2023-11-21T23:42:46Z
dc.identifier.urihttps://hdl.handle.net/11124/178560
dc.identifier.urihttps://doi.org/10.25676/11124/178560
dc.description.abstractClimate indices can measure the variability in the climate such as changes in sea surface temperature and wind. With this knowledge a predictive CO model was developed (Buchholz et al., 2018). This model uses climate indices to predict future CO emissions which are directly linked to large burn events that will occur in the southern hemisphere. We use four different climate indices in our model: the El Nino Southern Oscillation in the central tropical Pacific region 3.4, Indian Ocean Dipole/Dipole Mode Index (IOD/DMI), Tropical South Atlantic Index (TSA), and the Antartic [sic] Oscillation Index (AAO).
dc.format.mediumarticles
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartofReuleaux 2021
dc.rightsCreative Commons CC-BY License or the Creative Commons CC-BY-NC License.
dc.subjectgenetic algorithms
dc.subjectcarbon monoxide modeling
dc.titleOptimizing genetic algorithm parameters for atmospheric carbon monoxide modeling
dc.typeText
dc.publisher.originalColorado School of Mines. Reuleaux Undergraduate Research Magazine


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