Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation
Abstract The sustainability of artificial sand-binding vegetation is determined by the water balance between evapotranspiration (ET) and precipitation in desert regions. Consequently, accurately estimating ET is a critical prerequisite for determing the types and spatial distribution of artificial v...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-25208-z |
| _version_ | 1811315516842704896 |
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| author | Tonglin Fu Xinrong Li |
| author_facet | Tonglin Fu Xinrong Li |
| author_sort | Tonglin Fu |
| collection | DOAJ |
| description | Abstract The sustainability of artificial sand-binding vegetation is determined by the water balance between evapotranspiration (ET) and precipitation in desert regions. Consequently, accurately estimating ET is a critical prerequisite for determing the types and spatial distribution of artificial vegetation in different sandy areas. For this purpose, a novel hybrid estimation model was proposed to estimate monthly ET by coupling the deep learning long short term memory (LSTM) with variational mode decomposition (VMD) and whale optimization algorithm (WOA) (i.e., VMD-WOA-LSTM) to estimate the monthly ET in the southeast margins of Tengger Desert. The superiority of LSTM was selected due to its capability of automatically extracting the nonlinear and nonstationary features from sequential data, WOA was employed to optimize the hyperparameters of LSTM, and VMD was used to extract the intrinsic traits of ET time series. The estimating results of VMD-WOA-LSTM has been compared with actual ET and estimation of other hybrid models in terms of standard performance metrics. The results reveale that VMD-WOA-LSTM provide more accurate and reliable estimating results than that of LSTM, the support vector machine (SVM), and the variants of those models. Therefore, VMD-WOA-LSTM could be recommended as an essential auxiliary method to estimate ET in desert regions. |
| first_indexed | 2024-04-13T11:32:43Z |
| format | Article |
| id | doaj.art-2c6ea7cab15d4cfd80c53659cebcd83c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T11:32:43Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-2c6ea7cab15d4cfd80c53659cebcd83c2022-12-22T02:48:32ZengNature PortfolioScientific Reports2045-23222022-12-011211910.1038/s41598-022-25208-zHybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimationTonglin Fu0Xinrong Li1School of Mathematics and Statistics, Longdong UniversityShapotou Desert Research and Experiment Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of SciencesAbstract The sustainability of artificial sand-binding vegetation is determined by the water balance between evapotranspiration (ET) and precipitation in desert regions. Consequently, accurately estimating ET is a critical prerequisite for determing the types and spatial distribution of artificial vegetation in different sandy areas. For this purpose, a novel hybrid estimation model was proposed to estimate monthly ET by coupling the deep learning long short term memory (LSTM) with variational mode decomposition (VMD) and whale optimization algorithm (WOA) (i.e., VMD-WOA-LSTM) to estimate the monthly ET in the southeast margins of Tengger Desert. The superiority of LSTM was selected due to its capability of automatically extracting the nonlinear and nonstationary features from sequential data, WOA was employed to optimize the hyperparameters of LSTM, and VMD was used to extract the intrinsic traits of ET time series. The estimating results of VMD-WOA-LSTM has been compared with actual ET and estimation of other hybrid models in terms of standard performance metrics. The results reveale that VMD-WOA-LSTM provide more accurate and reliable estimating results than that of LSTM, the support vector machine (SVM), and the variants of those models. Therefore, VMD-WOA-LSTM could be recommended as an essential auxiliary method to estimate ET in desert regions.https://doi.org/10.1038/s41598-022-25208-z |
| spellingShingle | Tonglin Fu Xinrong Li Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation Scientific Reports |
| title | Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation |
| title_full | Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation |
| title_fullStr | Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation |
| title_full_unstemmed | Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation |
| title_short | Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation |
| title_sort | hybrid the long short term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation |
| url | https://doi.org/10.1038/s41598-022-25208-z |
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