Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition

• URL: http://arxiv.org/abs/2309.07187v1
• Date: Wed, 13 Sep 2023 02:15:02 GMT
• Title: Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition
• Authors: Ying Chen, Xiao Li, Hongbo Zhang, Wenyang Song and Chongxuan Xv
• Abstract summary: This paper proposes a time-series decomposition adaptive graph-time convolutional network ( AGTCNSD) prediction model. Based on the graph convolutional neural network, the water quality parameter data is modeled, and a parameter embedding matrix is defined. The validity of the model is verified by the water quality data of the coastal city Beihai.
• Score: 11.090455139282883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chlorophyll concentration can well reflect the nutritional status and algal blooms of water bodies, and is an important indicator for evaluating water quality. The prediction of chlorophyll concentration change trend is of great significance to environmental protection and aquaculture. However, there is a complex and indistinguishable nonlinear relationship between many factors affecting chlorophyll concentration. In order to effectively mine the nonlinear features contained in the data. This paper proposes a time-series decomposition adaptive graph-time convolutional network ( AGTCNSD ) prediction model. Firstly, the original sequence is decomposed into trend component and periodic component by moving average method. Secondly, based on the graph convolutional neural network, the water quality parameter data is modeled, and a parameter embedding matrix is defined. The idea of matrix decomposition is used to assign weight parameters to each node. The adaptive graph convolution learns the relationship between different water quality parameters, updates the state information of each parameter, and improves the learning ability of the update relationship between nodes. Finally, time dependence is captured by time convolution to achieve multi-step prediction of chlorophyll concentration. The validity of the model is verified by the water quality data of the coastal city Beihai. The results show that the prediction effect of this method is better than other methods. It can be used as a scientific resource for environmental management decision-making.

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