%A Jia-hui XU,Jing-chang WANG,Ling CHEN,Yong WU %T Surface water quality prediction model based on graph neural network %0 Journal Article %D 2021 %J Journal of ZheJiang University (Engineering Science) %R 10.3785/j.issn.1008-973X.2021.04.001 %P 601-607 %V 55 %N 4 %U {https://www.zjujournals.com/eng/CN/abstract/article_41822.shtml} %8 2021-04-05 %X

A surface water quality prediction model based on graph neural network (GNN) was proposed to solve the problem that water quality data has complex dependencies in both temporal and spatial dimensions. GNN was utilized to model the complex spatial dependencies of monitoring stations, and long short-term memory (LSTM) was used to model the complex temporal dependencies of historical water quality sequences. Then the encoded vector was input into the decoder to get the water quality prediction output. The experimental results show that the model can achieve 23.3%, 26.6% and 14.8% performance improvements compared with time series analysis methods, general regression methods and existing deep learning methods.