Water　quality　prediction　is　of　great　significance　for　water　environmental　protection　and　management.　Traditional　water　quality　prediction　methods　are　mainly　based　on　linear　models,　and　they　fail　to　extract　nonlinear　relationships.　Recurrent　neural　network-based　ones　have　shortcomings　such　as　being　difficult　for　long-term　prediction　and　unable　to　capture　spatial　dependencies.　To　address　these　issues,　this　work　proposes　an　improved　spatio-temporal　prediction　model　called　GATWNet,　which　combines　a　-G　raph　AT　tention　network　with　a　W　aveNet　model　based　on　dilated　causal　convolution　to　predict　water　quality　at　multiple　sites　over　a　future　time　period.　GATWNet　jointly　captures　the　spatial　information　of　river　networks　and　the　temporal　information　of　each　water　quality　monitoring　sensor.　Two　real-world　datasets-based　experimental　results　demonstrate　the　proposed　GATWNet　achieves　higher　prediction　accuracy　than　several　baseline　models.　　©　2022　IEEE.