WT-DSE-LSTM: A hybrid model for the multivariate prediction of dissolved oxygen

Abstract

Dissolved oxygen (DO) is a critical indicator of water quality in freshwater lake ecosystems. To address the issues of difficulty in prediction of DO, a hybrid model (WT-DSE-LSTM) combined with the wavelet transform algorithm, the dual-squeeze-and-excitation module, and the long short-term memory network is proposed in this paper. The DSE module captures the long-term dependencies and enhances feature weights through the attention mechanism. The MAE, RMSE, and R² of DO prediction with the proposed model is 0.011, 0.015, and 0.9746, respectively. Furthermore, compared with the state-of-the-art models, the MAE, RMSE of the proposed one can be decreased by 94.09 % and 95.64 % and the R² of that can be increased by 50.49 %. The DSE module has demonstrated its potential to enhance multivariate time series prediction, which is of great significance for environmental protection and disaster reduction.