Preliminary communication

https://doi.org/10.17559/TV-20230519000647

Water Quality Prediction Method Based on OVMD and Spatio-Temporal Dependence

Haitao Meng ; School of Mathematics and Information Technology, Hebei Agricultural Data Intelligent Perception and Application Technology Innovation Center, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China
Jinling Song ; School of Mathematics and Information Technology, Hebei Agricultural Data Intelligent Perception and Application Technology Innovation Center, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China; Hebei Key Laboratory of Ocean Dynamics, Resources and Environments, Qinhuangdao 066004, China *
Liming Huang ; School of Business Administration, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China
Yijin Zhu ; Research Institute of International Chinese Language Education, Beijing Language and Culture University, Beijing 100083, China
Meining Zhu ; School of Mathematics and Information Technology, Hebei Agricultural Data Intelligent Perception and Application Technology, Innovation Center, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China
Jingwu Zhang ; School of Mathematics and Information Technology, Hebei Agricultural Data Intelligent Perception and Application Technology, Innovation Center, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China

* Corresponding author.

Abstract

Water quality changes at one monitoring spot are not only related to local historical data but also spatially to the water quality of the adjacent spots. Additionally, the non-linear and non-stationary nature of water quality data has a significant impact on prediction results. To improve the accuracy of water quality prediction models, a comprehensive water quality prediction model has been initially established that takes into account both data complexity and spatio-temporal dependencies. The Optimal Variational Mode Decomposition (OVMD) technology is used to effectively decompose water quality data into several simple and stable time series, highlighting short-term and long-term features and enhancing the model's learning ability. The component sequence and spot adjacency matrix are used as the input of Graph Convolutional Network (GCN) to extract the spatial characteristics of the data, and the spatio-temporal dependencies of water quality data at different spots are obtained by combining GCN into the neurons of Gated Recurrent Unit (GRU). The attention model is added to automatically adjust the importance of each time node to further improve the accuracy of the training model and obtain a multi-step prediction output that more closely aligns with the characteristics of water quality change. The proposed model has been validated with real monitoring data for ammonia nitrogen (NH3-N) and total phosphorus (TP), and the results show that the proposed model is better than ARIMA, GRU and GCN+GRU models in terms of prediction results and it shows obvious advantages in the benchmark comparison experiment, which can provide reliable evidence for water pollution source traceability or early warning.

Keywords

attention model; GCN; GRU; optimal variational modal decomposition; water quality

Hrčak ID:

312913

URI

https://hrcak.srce.hr/312913

Publication date:

31.12.2023.

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