Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction

Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction

Accurate reference evapotranspiration (ET<sub>o</sub>) prediction is important for water resource management, particularly in arid regions where water availability is highly variable. However, the nonlinear and non-stationary characteristics of ET<sub>o</sub> time series pose...

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Bibliographic Details
Main Authors: Yunfei Chen, Zuyu Liu, Ting Long, Xiuhua Liu, Yaowei Gao, Sibo Wang
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Atmosphere
Subjects:
reference crop evapotranspiration
hybrid forecasting model
decomposition algorithm
deep learning
arid regions
Online Access:https://www.mdpi.com/2073-4433/16/5/535
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Summary:Accurate reference evapotranspiration (ET<sub>o</sub>) prediction is important for water resource management, particularly in arid regions where water availability is highly variable. However, the nonlinear and non-stationary characteristics of ET<sub>o</sub> time series pose challenges for conventional prediction models. Given this, in this study we evaluate eight decomposition-hybrid models that integrate various decomposition techniques with a long short-term memory (LSTM) network to enhance short-term (5-day, 7-day, and 10-day) ET<sub>o</sub> forecasting. Using a 40-year dataset from a meteorological station, we employ the Penman-Monteith equation to calculate ET<sub>o</sub> and systematically compare model performance. Results show that VMD-LSTM and EWT-LSTM achieve the highest accuracy in the testing set (R<sup>2</sup> = 0.983 and 0.992, respectively) but exhibit reduced robustness in the prediction phase due to excessive high-frequency components. In contrast, EMD-LSTM and ESMD-LSTM demonstrate superior predictive stability, with no significant differences from actual values (<i>p</i> > 0.05). These findings underscore the importance of selecting appropriate decomposition methods to balance high-frequency information and predictive accuracy, offering insights for improving ET<sub>o</sub> forecasting in arid regions.
ISSN:2073-4433

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