Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Chen, Honglei;Yang, Junbo;Fu, Xiaohua;Zheng, Qingxing;Song, Xinyu;...

通讯作者：

Zhiming Liu<&wdkj&>Xiaohua Fu

作者机构：

[Wang, He; Zheng, Qingxing; Song, Xinyu; Liang, Yingqi; Fu, Xiaohua; Yin, Hailong; Wang, Jiacheng; Yang, Junbo; Yang, Xinxin; Jiang, Jie; Chen, Honglei] Cent South Univ Forestry & Technol, Ecol Environm Management & Assessment Ctr, Changsha 410004, Peoples R China.

[Wang, He; Zheng, Qingxing; Song, Xinyu; Liang, Yingqi; Fu, Xiaohua; Yin, Hailong; Wang, Jiacheng; Yang, Junbo; Yang, Xinxin; Jiang, Jie; Chen, Honglei] Cent South Univ Forestry & Technol, Sch Environm Sci & Engn, Changsha 410004, Peoples R China.

[Fu, Zeding] Changsha Univ Sci & Technol, Sch Hydraul & Environm Engn, Changsha 410114, Peoples R China.

[Liu, Zhiming] Eastern New Mexico Univ, Dept Biol, Portales, NM 88130 USA.

通讯机构：

[Zhiming Liu] A

[Xiaohua Fu] E

Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China<&wdkj&>Authors to whom correspondence should be addressed.<&wdkj&>School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China<&wdkj&>Authors to whom correspondence should be addressed.<&wdkj&>Department of Biology, Eastern New Mexico University, Portales, NM 88130, USA

语种：

英文

关键词：

water quality prediction;time series;attention mechanism;long short-term memory (LSTM)

期刊：

Sustainability

ISSN：

2071-1050

年：

2022

卷：

期：

页码：

13231-

DOI：

10.3390/su142013231

基金类别：

Conceptualization, H.C. and J.Y.; Methodology, X.F.; Software, H.C.; Validation, Z.F.; Formal analysis, X.S.; Investigation, J.W.; Resources, Y.L.; Data curation, H.Y.; Writing—original draft preparation, H.C.; Writing—review and editing, Z.L.; Visualization, Q.Z.; supervision, J.J.; Project administration, H.W.; Funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript. This work was supported by the Key R&D Program of Hunan Provincial Science and Technology Department (2019SK2191).

机构署名：

本校为第一机构

院系归属：

环境科学与工程学院

摘要：

Prediction of water quality is a critical aspect of water pollution control and prevention. The trend of water quality can be predicted using historical data collected from water quality monitoring and management of water environment. The present study aims to develop a long short-term memory (LSTM) network and its attention-based (AT-LSTM) model to achieve the prediction of water quality in the Burnett River of Australia. The models developed in this study introduced an attention mechanism after feature extraction of water quality data in the section of Burnett River considering the effect of...

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