Cargando…
Ensemble streamflow forecasting based on variational mode decomposition and long short term memory
Reliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to sampling, noise and long historical changes...
Autores principales: | Sun, Xiaomei, Zhang, Haiou, Wang, Jian, Shi, Chendi, Hua, Dongwen, Li, Juan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8752851/ https://www.ncbi.nlm.nih.gov/pubmed/35017569 http://dx.doi.org/10.1038/s41598-021-03725-7 |
Ejemplares similares
-
Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow
por: Chen, Quanchao, et al.
Publicado: (2019) -
Correction: Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow
por: Chen, Quanchao, et al.
Publicado: (2020) -
Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm
por: Di Nunno, Fabio, et al.
Publicado: (2023) -
IoT and Ensemble Long-Short-Term-Memory-Based Evapotranspiration Forecasting for Riyadh
por: Nauman, Muhammad Asif, et al.
Publicado: (2023) -
A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition
por: Zhang, Xike, et al.
Publicado: (2018)