A study on water quality prediction by a hybrid dual channel CNN-LSTM model with attention mechanism

Yibei Liu; Peishun Liu; Xuefang Wang; Xueqing Zhang; Zifei Qin

doi:10.1117/12.2614212

10 November 2021 A study on water quality prediction by a hybrid dual channel CNN-LSTM model with attention mechanism

Yibei Liu, Peishun Liu, Xuefang Wang, Xueqing Zhang, Zifei Qin

Author Affiliations +

Proceedings Volume 12050, International Conference on Smart Transportation and City Engineering 2021; 1205035 (2021) https://doi.org/10.1117/12.2614212
Event: 2021 International Conference on Smart Transportation and City Engineering, 2021, Chongqing, China

Abstract

Water resources are the primary condition to maintain the ecological balance and sustainable development of the earth. Accurate prediction of water quality has high socio-economic value and ecological environmental protection value. However, it is difficult to achieve accurate prediction of river water quality data due to the characteristics of time series, seasonality, nonlinearity and excessive influencing factors. According to the characteristics of water quality data, this paper proposes a water quality prediction method based on attention mechanism of dual channel convolutional neural network ( CNN ) and long short-term memory ( LSTM ). Firstly, the river water quality data are cleaned and inputted into two parallel convolutional neural networks ( CNN ) for feature extraction. Then after the fusion level, the data are sent to the long short-term memory network ( LSTM ) for model training. Finally, the attention mechanism is used to optimize the model. The model combines powerful feature extraction ability of CNN , long-term memory ability of LSTM and the ability to highlight key features of attention mechanism ,achieving accurate prediction of river water quality data. Finally, based on the water quality data of the Guangli River, the results show that the Mean Absolute Error ( MAE ) of the proposed method is 2.04, and the Root Mean Square Guangli River Error ( RMSE ) is 2.77.

Citation Download Citation

Yibei Liu, Peishun Liu, Xuefang Wang, Xueqing Zhang, and Zifei Qin "A study on water quality prediction by a hybrid dual channel CNN-LSTM model with attention mechanism", Proc. SPIE 12050, International Conference on Smart Transportation and City Engineering 2021, 1205035 (10 November 2021); https://doi.org/10.1117/12.2614212

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Convolution

Feature extraction

Convolutional neural networks

Performance modeling

Oxygen

Data processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years