A fully unsupervised machine learning framework for algal bloom forecasting in inland waters using modis time series and climatic products

HIGHLIGHTS

• who: Pedro Henrique Ananias et al. from the Graduate Program in Natural Disasters, São Paulo State University (UNESP), National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), São José dos Campos, Brazil have published the research: A Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products, in the Journal: (JOURNAL) of 25/07/2022
• what: A representative example is the study conducted by Zhang et_al , in which a support vector machine (SVM)-based algorithm and Landsat-8/OLI images were used . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.