Forecasting

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.

A new framework based on a machine learning data-driven network to estimate and forecast evapotranspiration in agricultural areas was proposed.

We can better quantify evapotranspiration (ET) to improve our understanding of the Central Sands water cycle and improve irrigation demand forecasting.