A combined monthly precipitation prediction method based on CEEMD and improved LSTM.

ABSTRACT: With the continuous decline of water resources due to population growth and rapid economic development, precipitation prediction plays an important role in the rational allocation of global water resources. To address the non-linearity and non-stationarity of monthly precipitation, a combined prediction method based on complementary ensemble empirical mode decomposition (CEEMD) and a modified long short-term memory (LSTM) neural network was proposed. Firstly, the CEEMD method was used to decompose the monthly precipitation series into a set of relatively stationary sub-sequence components, which can better reflect the local characteristics of the sequence and further understand the nonlinear dynamic characteristics of the sequence. Then, improved LSTM neural networks were employed to predi

SUBMITTER: Jiang X 

PROVIDER: S-EPMC10343162 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature