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 ( ), actual evapotranspiration ( ), or surface water evaporation ( ), 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 , , & from local hydroclimate data with high accuracy. The probabilistic approach exhibited great potential to overcome data uncertainties, in which of the , of the , and of the test data at three watersheds were within the models’ prediction intervals. The modeling results revealed that the hybrid boosting framework can be used as a reliable computational tool to predict while bypassing net solar radiation calculations, estimate while overcoming uncertainties associated with pan evaporation & pan coefficients, and predict 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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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 StyleBaşağaoğlu, 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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