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Open AccessArticle

Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework

1
Edwards Aquifer Authority, San Antonio, TX 78215, USA
2
Department of Construction Science, University of Texas at San Antonio, San Antonio, TX 78207, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Renato Morbidelli
Water 2021, 13(4), 557; https://doi.org/10.3390/w13040557
Received: 7 January 2021 / Revised: 10 February 2021 / Accepted: 11 February 2021 / Published: 22 February 2021
(This article belongs to the Section Hydrology and Hydrogeology)
Evapotranspiration is often expressed in terms of reference crop evapotranspiration (ETo), actual evapotranspiration (ETa), or surface water evaporation (Esw), 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 ETo, Esw, & ETa from local hydroclimate data with high accuracy. The probabilistic approach exhibited great potential to overcome data uncertainties, in which 100% of the ETo, 89.9% of the Esw, and 93% of the ETa 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 ETo while bypassing net solar radiation calculations, estimate Esw while overcoming uncertainties associated with pan evaporation & pan coefficients, and predict ETa 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. View Full-Text
Keywords: evapotranspiration; machine learning; probabilistic model; shapley analysis evapotranspiration; machine learning; probabilistic model; shapley analysis
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MDPI and ACS Style

Başağaoğlu, H.; Chakraborty, D.; Winterle, J. Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework. Water 2021, 13, 557. https://doi.org/10.3390/w13040557

AMA Style

Başağaoğlu H, Chakraborty D, Winterle J. Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework. Water. 2021; 13(4):557. https://doi.org/10.3390/w13040557

Chicago/Turabian Style

Başağaoğlu, Hakan; Chakraborty, Debaditya; Winterle, James. 2021. "Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework" Water 13, no. 4: 557. https://doi.org/10.3390/w13040557

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