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

• Published in: Environmental monitoring and assessment Vol. 193; no. 3; p. 156
• Main Authors: Yang, Yong, Sun, Huaiwei, Xue, Jie, Liu, Yi, Liu, Luguang, Yan, Dong, Gui, Dongwei
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2021; Springer Nature B.V
• Subjects: Algorithms; Arid regions; Arid zones; Atmospheric Protection/Air Quality Control/Air Pollution; Bayes Theorem; Bayesian analysis; Bayesian theory; Climate change; Climate models; Earth and Environmental Science; Ecology; Ecotoxicology; Energy balance; Energy balance models; Energy transfer; Environment; Environmental Management; Environmental Monitoring; Environmental science; Evapotranspiration; Evapotranspiration estimates; Evapotranspiration models; Hydrologic cycle; Hydrological cycle; Hydrology; In situ measurement; Landsat; Landsat satellites; Learning algorithms; Machine Learning; Mathematical models; Monitoring/Environmental Analysis; Probability theory; Remote sensing; Satellite data; Semi arid areas; Semiarid zones; Support Vector Machine; Support vector machines; Surface energy; Surface energy balance; Surface properties; Uncertainty; Surface energy balance; Bayesian model averaging (BMA); Evapotranspiration; Machine learning; Landsat
• DOI: 10.1007/s10661-021-08934-1
• ISSN: 0167-6369; 1573-2959

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.