Multi-step prediction of chlorophyll concentration based on Adaptive
  Graph-Temporal Convolutional Network with Series Decomposition
Ying Chen Xiao Li Hongbo Zhang Wenyang Song Chongxuan Xv
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.