Abstract
Accurate prediction of water quality indicators can effectively predict sudden water pollution events and reveal them to water users for reducing the impact of water quality pollution. Neural networks, e.g., Long Short-Term Memory (LSTM) and encoder–decoder, have been widely used to predict time series data. However, as the water quality data increases, it becomes unstable and highly nonlinear, and therefore, its accurate prediction becomes a big challenge. To solve it, this work proposes a hybrid prediction method called VBAED to predict the water quality time series. VBAED combines Variational mode decomposition (VMD), a Bidirectional input Attention mechanism, an Encoder with bidirectional LSTM (BiLSTM), and a Decoder with a bidirectional temporal attention mechanism and BiLSTM. The definition of VBAED is an Encoder–Decoder model that uses VMD as mode decomposition, combining BiLSTM with a bidirectional attention mechanism. Specifically, VBAED first adopts VMD to decompose historical data of a predicted factor, and its decomposed results are adopted as the input along with other features. Then, a bidirectional input attention mechanism is adopted to add weights to input features from both directions. VBAED adopts BiLSTM as an encoder to extract hidden features from input features. Finally, the predicted result is obtained by a BiLSTM decoder with a bidirectional temporal attention mechanism. Real-life data-based experiments demonstrate that VBAED obtains the best prediction results compared with other widely used methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.