A multivariate streamflow forecasting model by integrating improved complete ensemble empirical mode decomposition with additive noise, sample entropy, Gini index and sequence-to-sequence approaches

标题
A multivariate streamflow forecasting model by integrating improved complete ensemble empirical mode decomposition with additive noise, sample entropy, Gini index and sequence-to-sequence approaches
作者
关键词
Streamflow time series, ICEEMDAN, Seq2Seq, Encode-decode, LSTM, Deep learning, RNN, Hybrid model
出版物
JOURNAL OF HYDROLOGY
Volume 603, Issue -, Pages 126831
出版商
Elsevier BV
发表日期
2021-08-15
DOI
10.1016/j.jhydrol.2021.126831

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