Spatio-temporal　prediction　has　a　wide　range　of　applications　in　many　fields,　e.g.,　air　pollution,　weather　forecasting,　and　traffic　forecasting.　Water　quality　prediction　is　also　one　of　spatio-temporal　prediction　tasks.　However,　it　faces　the　following　challenges:　1)　Water　quality　in　river　networks　has　complex　spatial　dependencies;　2)　There　are　complex　nonlinear　relations　in　water　quality　time　series;　and　3)　It　is　difficult　to　realize　long-term　forecasting.　To　address　these　challenges,　this　work　proposes　a　spatio-temporal　prediction　model　called　a　Graph　Attention-based　Spatio-Temporal　(GAST)　neural　network.　GAST　investigates　spatial　and　temporal　dependencies　of　water　quality　time　series.　First,　we　introduce　a　temporal　attention　mechanism　to　capture　time　series　dependencies,　which　can　effectively　handle　nonlinear　relationships　in　time　series.　Second,　we　adopt　a　spatial　attention　mechanism　to　extract　spatial　dependencies　of　river　networks　and　fuse　temporal　features　of　spatial　nodes.　Third,　we　adopt　a　temporal　convolution　residual　mechanism　based　on　the　spatio-temporal　fusion,　which　improves　the　accuracy　of　long-term　series　prediction.　This　work　adopts　two　real-world　datasets　to　evaluate　the　proposed　GAST　and　experiments　demonstrate　that　GAST　outperforms　several　state-of-the-art　methods　in　terms　of　prediction　accuracy.　　©　2022　IEEE.