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Table 6

Performance of three models for flood forecasting at lead times , , , and in the testing stage

ModelIndexLead time
XAJ NSE 0.921 0.881 0.824 0.761 
KGE 0.897 0.887 0.796 0.776 
0.960 0.940 0.909 0.886 
RE (%) 4.976 5.765 4.681 −3.802 
RMSE (m3/s) 57.854 88.896 118.680 144.159 
LSTM NSE 0.979 0.936 0.850 0.749 
KGE 0.909 0.858 0.773 0.765 
0.990 0.968 0.925 0.867 
RE (%) 6.055 1.774 7.943 1.244 
RMSE (m3/s) 35.180 65.207 119.528 150.627 
XAJ-LSTM NSE 0.981 0.943 0.895 0.813 
KGE 0.930 0.918 0.823 0.830 
0.991 0.971 0.947 0.902 
RE (%) 4.664 3.792 4.094 0.663 
RMSE (m3/s) 33.137 61.288 91.680 129.997 
ModelIndexLead time
XAJ NSE 0.921 0.881 0.824 0.761 
KGE 0.897 0.887 0.796 0.776 
0.960 0.940 0.909 0.886 
RE (%) 4.976 5.765 4.681 −3.802 
RMSE (m3/s) 57.854 88.896 118.680 144.159 
LSTM NSE 0.979 0.936 0.850 0.749 
KGE 0.909 0.858 0.773 0.765 
0.990 0.968 0.925 0.867 
RE (%) 6.055 1.774 7.943 1.244 
RMSE (m3/s) 35.180 65.207 119.528 150.627 
XAJ-LSTM NSE 0.981 0.943 0.895 0.813 
KGE 0.930 0.918 0.823 0.830 
0.991 0.971 0.947 0.902 
RE (%) 4.664 3.792 4.094 0.663 
RMSE (m3/s) 33.137 61.288 91.680 129.997 
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