Pre-training enhanced spatio-temporal graph neural network for predicting influent water quality and flow rate of wastewater treatment plant: Improvement of forecast accuracy and analysis of related factors

Wu, Xue; Chen, Ming; Zhu, Tengyi; Chen, Dou; Xiong, Jianglei

doi:10.1016/j.scitotenv.2024.175411

Science of The Total Environment

2024

DOI: 10.1016/j.scitotenv.2024.175411

|View full text |Cite

Pre-training enhanced spatio-temporal graph neural network for predicting influent water quality and flow rate of wastewater treatment plant: Improvement of forecast accuracy and analysis of related factors

Xue Wu,

Ming Chen,

Tengyi Zhu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Hybrid Model Combined Deep Neural Network and Beluga Whale Optimizer for China Urban Dissolved Oxygen Concentration Forecasting

Wang,

Ding,

Zhang

et al. 2024

Water

View full text Add to dashboard Cite

The dissolved oxygen concentration (DOC) is an important indicator of water quality. Accurate DOC predictions can provide a scientific basis for water environment management and pollution prevention. This study proposes a hybrid DOC forecasting framework combined with Variational Mode Decomposition (VMD), a convolutional neural network (CNN), a Gated Recurrent Unit (GRU), and the Beluga Whale Optimization (BWO) algorithm. Specifically, the original DOC sequences were decomposed using VMD. Then, CNN-GRU combined with an attention mechanism was utilized to extract the key features and local dependency of the decomposed sequences. Introducing the BWO algorithm solved the correction coefficients of the proposed system, with the aim of improving prediction accuracy. This study used 4-h monitoring China urban water quality data from November 2020 to November 2023. Taking Lianyungang as an example, the empirical findings exhibited noteworthy enhancements in performance metrics such as MSE, RMSE, MAE, and MAPE within the VMD-BWO-CNN-GRU-AM, with reductions of 0.2859, 0.3301, 0.2539, and 0.0406 compared to a GRU. These results affirmed the superior precision and diminished prediction errors of the proposed hybrid model, facilitating more precise DOC predictions. This proposed DOC forecasting system is pivotal for sustainably monitoring and regulating water quality, particularly in terms of addressing pollution concerns.

show abstract

A Hybrid Model Combined Deep Neural Network and Beluga Whale Optimizer for China Urban Dissolved Oxygen Concentration Forecasting

Wang,

Ding,

Zhang

et al. 2024

Water

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Pre-training enhanced spatio-temporal graph neural network for predicting influent water quality and flow rate of wastewater treatment plant: Improvement of forecast accuracy and analysis of related factors

Cited by 1 publication

References 36 publications

A Hybrid Model Combined Deep Neural Network and Beluga Whale Optimizer for China Urban Dissolved Oxygen Concentration Forecasting

A Hybrid Model Combined Deep Neural Network and Beluga Whale Optimizer for China Urban Dissolved Oxygen Concentration Forecasting

Contact Info

Product

Resources

About