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An ensemble learning approach for estimating high spatiotemporal resolution of ground-level ozone in the contiguous United States
In this paper we integrated multiple types of predictor variables and three types of machine learners (neural network, random forest, and gradient boosting) into a geographically weighted ensemble model to estimate daily maximum 8-hr O(3) with high resolution over both space (at 1 km × 1 km grid cel...
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| Publicat a: | Environ Sci Technol |
|---|---|
| Autors principals: | , , , , , , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2020
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7498146/ https://ncbi.nlm.nih.gov/pubmed/32808786 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1021/acs.est.0c01791 |
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