Application of random forest and icon models combined with weather forecasts to predict soil temperature and content in greenhouse

HIGHLIGHTS

• who: HYDRUS- and colleagues from the Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan have published the research work: Application of Random Forest and ICON Models Combined with Weather Forecasts to Predict Soil Temperature and Content in Greenhouse, in the Journal: Water 2020, 12, do x fortunately of 20/09/2018
• what: The authors proposed a new framework that combines weather forecast data numerical models and machine learning methods to simulate and predict the soil temperature and volumetric water content in a greenhouse. This approach provides a framework that can potentially learn best . . .
  

   

  

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