TY - GEN
T1 - Multi-Scale Convolutional LSTM Network for Sewage Flow Prediction
AU - He, Yanlin
AU - Teng, Xiuyun
AU - Xu, Yuan
AU - Zhu, Qunxiong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Predicting sewage flow is of great significance in urban sewage treatment and management. However, traditional prediction methods often overlook information at different time scales, failing to capture the complex relationships among these pieces of information. To address this issue, this paper proposes a multi-scale convolutional long short-term memory (LSTM) network. The convolutional layers are used to capture data patterns at different time scales, while the LSTM network is utilized to better capture the long-term dependencies and short-term fluctuations between sequences. Experimental results demonstrate that compared to traditional LSTM networks, the proposed method achieves higher accuracy and stability in sewage flow prediction.
AB - Predicting sewage flow is of great significance in urban sewage treatment and management. However, traditional prediction methods often overlook information at different time scales, failing to capture the complex relationships among these pieces of information. To address this issue, this paper proposes a multi-scale convolutional long short-term memory (LSTM) network. The convolutional layers are used to capture data patterns at different time scales, while the LSTM network is utilized to better capture the long-term dependencies and short-term fluctuations between sequences. Experimental results demonstrate that compared to traditional LSTM networks, the proposed method achieves higher accuracy and stability in sewage flow prediction.
KW - LSTM
KW - Multi-scale features
KW - Multivariate information
KW - Sewage flow prediction
UR - http://www.scopus.com/inward/record.url?scp=85202440853&partnerID=8YFLogxK
U2 - 10.1109/DDCLS61622.2024.10606563
DO - 10.1109/DDCLS61622.2024.10606563
M3 - Conference contribution
AN - SCOPUS:85202440853
T3 - Proceedings of 2024 IEEE 13th Data Driven Control and Learning Systems Conference, DDCLS 2024
SP - 2164
EP - 2169
BT - Proceedings of 2024 IEEE 13th Data Driven Control and Learning Systems Conference, DDCLS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024
Y2 - 17 May 2024 through 19 May 2024
ER -