Weather Data Imputation

  • Faculty: Lav Khot (WSU), Prasad Tadepalli (OSU), Weng-Keen Wong (OSU), Alan Fern (OSU) 
  • Senior Collaborators: Paola Pesantez-Cabrera (WSU, Data Scientist), Bryan Curtis (WSU AWN Data Scientist), Sean Hill (WSU AWN System Analyst) 
  • Graduate students: Mohammad Rafid Ul Islam (OSU PhD in AI)
  • Undergraduate students: Robert Barragan (HU, CS)

  • Develop and implement weather station correlation methodology for site-specific usage in case of missing data from a preferred station.
  • Develop robust weather data imputation methods and evaluate them on AgWeatherNet. 
  • Develop and implement site-specific weather nowcasting/forecasting for 
  • improved decision support through ported models.

  • Weather station correlation methodology can help in running AI-based agricultural decision support models on alternate station data in case of missing data
  • The neural-network-based imputation methods can outperform classical statistical  approaches
  • The higher accuracy, station-specific forecasts will allow growers to improve anticipation of weather events and AI model predicted crop loss scenarios at their specific location and better plan on appropriate mitigation strategies. 

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