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Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms

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A Correction to this article was published on 23 March 2021

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Abstract

Evapotranspiration (ET) is one of the most important components of global hydrologic cycle and has significant impacts on energy exchange and climate change. Numerous models have been developed to estimate ET so far; however, great uncertainties in models still require considerations. The aim of this study is to reduce model errors and uncertainties among multi-models to improve daily ET estimate. The Bayesian model averaging (BMA) method is used to assemble eight ET models to produce ET with Landsat 8 satellite data, including four surface energy balance models (i.e., SEBS, SEBAL, SEBI, and SSEB) and four machine learning algorithms (i.e., polymars, random forest, ridge regression, and support vector machine). Performances of each model and BMA method were validated through in situ measurements of semi-arid region. Results indicated that the BMA method outperformed all eight single models. The four most important models obtained by the BMA method were ranked by random forest, SVM, SEBS, and SEBAL. The BMA method coupled with machine learning can significantly improve the accuracy of daily ET estimate, reducing uncertainties among models, and taking different intrinsic benefits of empirically and physically based models to obtain a more reliable ET estimate.

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Funding

This work was partially supported by the National Natural Science Foundation of China under Grant 51879110, 52079055, and 52011530128, and by the Hubei Provincial Water Resources Key Scientific Research Project under Grant HBSLKY201907, and Special project of basic resources investigation of Ministry of Science and Technology under Grant 2019FY100205.

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Correspondence to Huaiwei Sun.

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Yang, Y., Sun, H., Xue, J. et al. Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms. Environ Monit Assess 193, 156 (2021). https://doi.org/10.1007/s10661-021-08934-1

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