Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework,Water

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Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework
Water ( IF 3.0 ) Pub Date : 2021-02-22 , DOI: 10.3390/w13040557
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.

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