TY - GEN
T1 - An Improved Attention-based LSTM for Multi-Step Dissolved Oxygen Prediction in Water Environment
AU - Bi, Jing
AU - Lin, Yongze
AU - Dong, Quanxi
AU - Yuan, Haitao
AU - Zhou, Meng Chu
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/30
Y1 - 2020/10/30
N2 - 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.
AB - 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.
KW - Attention
KW - Long Short-Term Memory (LSTM)
KW - Savitzky-Golay filter
KW - dissolved oxygen prediction
KW - encoder-decoder architecture
KW - water environment
UR - http://www.scopus.com/inward/record.url?scp=85096351478&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096351478&partnerID=8YFLogxK
U2 - 10.1109/ICNSC48988.2020.9238097
DO - 10.1109/ICNSC48988.2020.9238097
M3 - Conference contribution
AN - SCOPUS:85096351478
T3 - 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
BT - 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
Y2 - 30 October 2020 through 2 November 2020
ER -