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Table 3 summarizes the performance results for the three BPNN model scenarios. R2 and Ens for the five gauging stations are relatively low (<0.46), and the RMSE errors are correspondingly large (>1.0 m) for scenario S1. These performance statistics, including the RMSE of 2.88 m at Hukou, indicate that the BPNN models of S1 failed to reproduce the observed time series of lake water levels. The simulation of lake water levels is clearly improved in scenarios S2 and S3 (Table 3). The values of R2 and Ens improve to >0.90, and RMSE errors decrease significantly to <1.0 m with the introduction of Yangtze River flows. In particular, the lake water-level simulation accuracy for the downstream gauging stations is significantly enhanced (Table 3). These results indicate that Yangtze River discharges play an important role in Poyang Lake water-level behavior, in support of the cross-correlation analysis (Figure 3). The decrease in RMSE values from the lake outlet to the most upstream gauging station, obtained for S1 models (see Table 3), provides evidence that the contribution of the Yangtze River (to lake water levels) reduces gradually in the upstream direction from the lake outlet, as expected.

Table 3

Performance evaluation of BPNN models

Location
BPNNPerformanceHukouXingziDuchangTangyinKangshan
S1 Training – R2 0.43 0.45 0.46 0.45 0.44 
Training – Ens 0.43 0.45 0.46 0.45 0.44 
Training – RMSE (m) 2.88 2.52 2.08 1.73 1.39 
Testing – R2 0.47 0.48 0.49 0.51 0.50 
Testing – Ens 0.39 0.36 0.37 0.42 0.39 
Testing – RMSE (m) 2.66 2.56 2.16 1.62 1.24 
S2 Training – R2 0.98 0.97 0.95 0.92 0.90 
Training – Ens 0.98 0.96 0.95 0.92 0.90 
Training – RMSE (m) 0.58 0.64 0.61 0.66 0.61 
Testing – R2 0.96 0.95 0.93 0.92 0.90 
Testing – Ens 0.96 0.92 0.87 0.86 0.81 
Testing – RMSE (m) 0.70 0.91 1.02 0.82 0.71 
S3 Training – R2 0.98 0.97 0.96 0.95 0.94 
Training – Ens 0.98 0.97 0.96 0.95 0.94 
Training – RMSE (m) 0.59 0.60 0.58 0.55 0.50 
Testing – R2 0.97 0.96 0.94 0.93 0.90 
Testing – Ens 0.93 0.90 0.86 0.87 0.83 
Testing – RMSE (m) 0.89 1.01 0.96 0.77 0.65 
Location
BPNNPerformanceHukouXingziDuchangTangyinKangshan
S1 Training – R2 0.43 0.45 0.46 0.45 0.44 
Training – Ens 0.43 0.45 0.46 0.45 0.44 
Training – RMSE (m) 2.88 2.52 2.08 1.73 1.39 
Testing – R2 0.47 0.48 0.49 0.51 0.50 
Testing – Ens 0.39 0.36 0.37 0.42 0.39 
Testing – RMSE (m) 2.66 2.56 2.16 1.62 1.24 
S2 Training – R2 0.98 0.97 0.95 0.92 0.90 
Training – Ens 0.98 0.96 0.95 0.92 0.90 
Training – RMSE (m) 0.58 0.64 0.61 0.66 0.61 
Testing – R2 0.96 0.95 0.93 0.92 0.90 
Testing – Ens 0.96 0.92 0.87 0.86 0.81 
Testing – RMSE (m) 0.70 0.91 1.02 0.82 0.71 
S3 Training – R2 0.98 0.97 0.96 0.95 0.94 
Training – Ens 0.98 0.97 0.96 0.95 0.94 
Training – RMSE (m) 0.59 0.60 0.58 0.55 0.50 
Testing – R2 0.97 0.96 0.94 0.93 0.90 
Testing – Ens 0.93 0.90 0.86 0.87 0.83 
Testing – RMSE (m) 0.89 1.01 0.96 0.77 0.65 

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