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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MDPI AG
2025-04-01
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| Series: | Atmosphere |
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| author | Yunfei Chen Zuyu Liu Ting Long Xiuhua Liu Yaowei Gao Sibo Wang |
| author_facet | Yunfei Chen Zuyu Liu Ting Long Xiuhua Liu Yaowei Gao Sibo Wang |
| author_sort | Yunfei Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6a325f9622ab4f8bbb4df9e79fa8147f |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-6a325f9622ab4f8bbb4df9e79fa8147f2025-08-20T01:56:20ZengMDPI AGAtmosphere2073-44332025-04-0116553510.3390/atmos16050535Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration PredictionYunfei Chen0Zuyu Liu1Ting Long2Xiuhua Liu3Yaowei Gao4Sibo Wang5School of Water and Environment, Chang’an University, Xi’an 710054, ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, ChinaSchool of Water and Environment, Chang’an University, Xi’an 710054, ChinaAccurate 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.https://www.mdpi.com/2073-4433/16/5/535reference crop evapotranspirationhybrid forecasting modeldecomposition algorithmdeep learningarid regions |
| spellingShingle | Yunfei Chen Zuyu Liu Ting Long Xiuhua Liu Yaowei Gao Sibo Wang Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction Atmosphere reference crop evapotranspiration hybrid forecasting model decomposition algorithm deep learning arid regions |
| title | Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction |
| title_full | Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction |
| title_fullStr | Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction |
| title_full_unstemmed | Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction |
| title_short | Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction |
| title_sort | evaluation of eight decomposition hybrid models for short term daily reference evapotranspiration prediction |
| topic | reference crop evapotranspiration hybrid forecasting model decomposition algorithm deep learning arid regions |
| url | https://www.mdpi.com/2073-4433/16/5/535 |
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