Forecasting West Nile Virus With Graph Neural Networks: Harnessing Spatial Dependence in Irregularly...
Forecasting West Nile Virus With Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data
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United States: John Wiley & Sons, Inc
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English
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United States: John Wiley & Sons, Inc
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Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the...
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Forecasting West Nile Virus With Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data
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TN_cdi_doaj_primary_oai_doaj_org_article_416c7103f43645f881e0b51b5238e15b
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_416c7103f43645f881e0b51b5238e15b
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ISSN
2471-1403
E-ISSN
2471-1403
DOI
10.1029/2023GH000784
