An Improved Attention-based LSTM for Multi-Step Dissolved Oxygen Prediction in Water Environment
Document Type
Conference Proceeding
Publication Date
10-30-2020
Abstract
The prediction of accurate water quality has great significance to the sustainable management of water resources and pollution prevention. Due to the complexity of water environment, it is difficult to do so. Traditional prediction methods are mainly linear methods. Their prediction accuracy is limited since they fail to reflect nonlinear characteristics in water quality data. To achieve much higher accuracy, this work proposes to combines a Savitzky-Golay filter with Attention-based Long Short-Term Memory to perform a multi-step prediction of water quality. The proposed model uses a Savitzky-Golay filter for smoothing sequences to reduce noise interference. The adoption of an attention mechanism can extract effective information from complex, long, and temporal dependence. Experimental results demonstrate that the proposed method outperforms other state-of-the-art peers.
Identifier
85096351478 (Scopus)
ISBN
[9781728168531]
Publication Title
2020 IEEE International Conference on Networking Sensing and Control Icnsc 2020
External Full Text Location
https://doi.org/10.1109/ICNSC48988.2020.9238097
Grant
61703011
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Bi, Jing; Lin, Yongze; Dong, Quanxi; Yuan, Haitao; and Zhou, Meng Chu, "An Improved Attention-based LSTM for Multi-Step Dissolved Oxygen Prediction in Water Environment" (2020). Faculty Publications. 4896.
https://digitalcommons.njit.edu/fac_pubs/4896
