Neural Networks

Evaluation of prediction and forecasting models for evapotranspiration of agricultural lands in the Midwest US
Random Forest (RF) and recurrent neural network models such as LSTM predict field-scale ET more accurately than process-based models. Vapor pressure and crop coefficients are key predictors for irrigated crops. ET forecasting for non-irrigated crop requires enhanced vegetation index. Short-term (3-day) forecasts have lower uncertainty, higher accuracy using RF.
Ammara Talib, Ankur R.Desai, Jingyi Huang, Tim J.Griffis, David E.Reed
PDF DOI
Evaluation of prediction and forecasting models for evapotranspiration of agricultural lands in the Midwest US