A data-driven model for water quality prediction in Tai Lake, China, using secondary modal decomposition with multidimensional external features

• DOI: 10.1016/j.ejrh.2023.101435
• Publication Date: 2023-05-30T16:58:57Z
• Authors: Rui Tan; Zhaocai Wang; Tunhua Wu; Junhao Wu
• Keywords: Hyperparameter; Feature (linguistics); Mode (computer interface)

Tai Lake, the third largest freshwater lake in China, with a history of serious ecological pollution incidents. Lake water quality prediction techniques are essential to ensure an early emergency response capability for sustainable management. Herein, effective data-driven ensemble model was developed predicting dissolved oxygen (DO) based on meteorological factors, indicators and spatial information. First, variation mode decomposition (VMD) used decompose data into multiple modal components classify them feature terms self terms. The were combined relevant external features multivariate by convolutional neural network (CNN) bi-directional long short-term memory (BiLSTM) attention mechanism (AT), as well using whale optimization algorithm (WOA) optimize hyperparameters. form secondary model. Finally, groupings linearly summed obtain outcome. proposed has highest accuracy best effect 0.5 days period. This research also establishes stepwise temperature regulation mechanism, where output target DO content value is achieved changing magnitude combining it this model, thereby strengthening protection resources management fishery production.