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

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 thi...