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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...

Täydet tiedot

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Bibliografiset tiedot
Julkaisussa:Environ Sci Technol
Päätekijät: Requia, Weeberb J., Di, Qian, Silvern, Rachel, Kelly, James T., Koutrakis, Petros, Mickley, Loretta J., Sulprizio, Melissa P., Amini, Heresh, Shi, Liuhua, Schwartz, Joel
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: 2020
Aiheet:
Linkit: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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