Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework: Fid-Bau Portal

Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework

Hakan Başağaoğlu / Debaditya Chakraborty / James Winterle

Evapotranspiration is often expressed in terms of reference crop evapotranspiration ( $E T_{o}$ ), actual evapotranspiration ( $E T_{a}$ ), or surface water evaporation ( $E_{s w}$ ), and their reliable predictions are critical for groundwater, irrigation, and aquatic ecosystem management in semi-arid regions. We demonstrated that a newly developed probabilistic machine learning (ML) model, using a hybridized “boosting” framework, can simultaneously predict the daily $E T_{o}$ , $E_{s w}$ , & $E T_{a}$ from local hydroclimate data with high accuracy. The probabilistic approach exhibited great potential to overcome data uncertainties, in which $100 %$ of the $E T_{o}$ , $89.9 %$ of the $E_{s w}$ , and $93 %$ of the $E T_{a}$ test data at three watersheds were within the models’ $95 %$ prediction intervals. The modeling results revealed that the hybrid boosting framework can be used as a reliable computational tool to predict $E T_{o}$ while bypassing net solar radiation calculations, estimate $E_{s w}$ while overcoming uncertainties associated with pan evaporation & pan coefficients, and predict $E T_{a}$ while offsetting high capital & operational costs of EC towers. In addition, using the Shapley analysis built on a coalition game theory, we identified the order of importance and interactions between the hydroclimatic variables to enhance the models’ transparency and trustworthiness.