Decomposition prediction and optimal ensemble strategy improve river dissolved oxygen prediction accuracy.

ABSTRACT: The accurate prediction of dissolved oxygen (DO) concentration in rivers is very important for the management of aquatic ecosystems, However, the hybrid model of ' modal decomposition + prediction ' for predicting the nonlinear change of dissolved oxygen in rivers is still insufficient. In this paper, a frequency division prediction framework based on the optimal ensemble of Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed. The dissolved oxygen sequence was decomposed into multiple components by CEEMDAN, and the long short-term memory network (LSTM), support vector regression (SVR) and multi-layer perceptron (MLP) models were constructed to predict each component independently. An innovative grid search algorithm with constraints is constructed,

SUBMITTER: Xie Y 

PROVIDER: S-EPMC12675495 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature