Massive　amount　of　water　level　data　has　been　collected　by　using　Internet　of　Things　(IoT)　techniques　in　the　Yangtze　River　and　other　rivers.　In　this　paper,　utilizing　these　data　to　construct　deep　neural　network　models　for　water　level　prediction　is　focused.　To　achieve　higher　accuracy,　both　the　factors　of　time　and　locations　of　data　collection　sensors　are　considered　to　perform　prediction.　And　the　network　structures　of　gated　recurrent　unit　(GRU)　and　convolutional　neural　network　(CNN)　are　combined　to　build　a　CNN-GRU　model　in　which　the　GRU　part　learns　the　changing　trend　of　water　level,　and　the　CNN　part　learns　the　spatial　correlation　among　water　level　data　observed　from　adjacent　water　stations.　The　CNN-GRU　model　that　using　data　from　multiple　locations　to　predict　the　water　level　of　the　middle　location　has　higher　accuracy　than　the　model　only　based　on　GRU　and　other　state-of-the-art　methods　including　autoregressive　integrated　moving　average　model　(ARIMA),　wavelet-based　artificial　neural　network　(WANN)　and　long-short　term　memory　model　(LSTM),　because　of　its　ability　to　decrease　the　affections　of　abnormal　value　and　data　randomness　of　a　single　water　station　to　some　extent.　The　results　are　verified　on　an　experiment　dataset　that　including　30-year　observed　data　of　water　level　at　several　collection　stations　in　the　Yangtze　River.　For　forecasting　the　8-o＇clock　water　levels　of　future　5　days,　accuracy　of　the　CNN-GRU　model　is　better　than　that　of　ARIMA,　WANN　and　LSTM　models　with　three　evaluation　factors　including　Nash-Sutcliffe　efficiency　coefficient　(NSE),　average　relative　error　(MRE)　and　root　mean　square　error　(RMSE).