GCN-Seq2Seq: A Spatio-Temporal feature-fused model for surface water quality prediction

Abstract

Aiming at the complex dependence of water quality data in space and time, we propose a GCN-Seq2Seq model for surface water quality prediction. The model uses Graph Convolutional Network (GCN) to capture the spatial feature of water quality monitoring sites, uses the sequence to sequence (Seq2Seq) model constructed by GRU to extract the temporal feature of the water quality data sequence, and predicts multi-step water quality time series. Experiments were carried out with data from 6 water quality monitoring stations in the Huangshui River and surrounding areas in Xining City, Qinghai Province from November 2020 to June 2021, and compared with the baseline model. experimental results show that the proposed model can effectively improve the accuracy of multi-step prediction of surface water quality.