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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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Bibliografski detalji
Izdano u:Environ Sci Technol
Glavni autori: Requia, Weeberb J., Di, Qian, Silvern, Rachel, Kelly, James T., Koutrakis, Petros, Mickley, Loretta J., Sulprizio, Melissa P., Amini, Heresh, Shi, Liuhua, Schwartz, Joel
Format: Artigo
Jezik:Inglês
Izdano: 2020
Teme:
Online pristup: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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