Water Quality Prediction Model Based on GAT-BILSTM-Res
For the dependence of water quality data in the time dimension and the dependence of water quality monitoring stations in the spatial dimension,this paper is based on the actual monitoring of historical water quality data in the Tianjin section of the Haihe River basin.It designs a method to effectively extract spatio-temporal characteristics,and proposes a spatio-temporal water quality model(GAT-BILSTM-Res)that combines graph attention network(GAT),bi-directional long and short-term memory network(Bi-LSTM)and residual block(ResBlock).The model first captures the topological relationship between water quality monitoring stations through GAT and establishes a spatial correlation model;at the same time,the dynamic changes in water quality monitoring data are captured through Bi-LSTM,and the temporal correlation is modeled.Then the spatio-temporal features are fused and input into the residual block.Finally,the prediction results are output by using the fully connected layer.The experimental results show that the model is able to achieve a performance improvement of 6.6%～25.2%compared with the baseline model.
water quality predictiongraph attention networkbi-directional long and short-term memory networkresidual block
NETL
NSTL
万方数据
维普
