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Table 14

Error comparison of LSTM and other four models

BasinModelNo. of inputNSE
RMSE
1 h2 h3 h4 h5 h6 h1 h2 h3 h4 h5 h6 h
Qinhuai LSTM 54 0.969 0.954 0.926 0.897 0.854 0.821 0.025 0.026 0.028 0.030 0.033 0.034 
SVM 0.954 0.921 0.897 0.845 0.81 0.795 0.025 0.027 0.036 0.038 0.04 0.042 
BP 0.897 0.821 0.781 0.726 0.684 0.623 0.03 0.031 0.033 0.039 0.045 0.047 
WaveNet 0.787 0.694 0.672 0.624 0.599 0.546 0.038 0.04 0.042 0.046 0.049 0.055 
CNN 0.722 0.685 0.645 0.578 0.543 0.497 0.045 0.053 0.057 0.078 0.085 0.088 
Tunxi LSTM 72 0.956 0.942 0.931 0.894 0.864 0.850 0.012 0.021 0.027 0.032 0.037 0.042 
SVM 0.875 0.811 0.790 0.740 0.725 0.711 0.016 0.024 0.036 0.038 0.04 0.045 
BP 0.847 0.800 0.780 0.730 0.713 0.692 0.018 0.027 0.04 0.042 0.042 0.05 
WaveNet 0.816 0.762 0.748 0.714 0.695 0.647 0.029 0.04 0.046 0.051 0.053 0.057 
CNN 0.654 0.542 0.498 0.421 0.398 0.342 0.059 0.077 0.092 0.103 0.126 0.167 
BasinModelNo. of inputNSE
RMSE
1 h2 h3 h4 h5 h6 h1 h2 h3 h4 h5 h6 h
Qinhuai LSTM 54 0.969 0.954 0.926 0.897 0.854 0.821 0.025 0.026 0.028 0.030 0.033 0.034 
SVM 0.954 0.921 0.897 0.845 0.81 0.795 0.025 0.027 0.036 0.038 0.04 0.042 
BP 0.897 0.821 0.781 0.726 0.684 0.623 0.03 0.031 0.033 0.039 0.045 0.047 
WaveNet 0.787 0.694 0.672 0.624 0.599 0.546 0.038 0.04 0.042 0.046 0.049 0.055 
CNN 0.722 0.685 0.645 0.578 0.543 0.497 0.045 0.053 0.057 0.078 0.085 0.088 
Tunxi LSTM 72 0.956 0.942 0.931 0.894 0.864 0.850 0.012 0.021 0.027 0.032 0.037 0.042 
SVM 0.875 0.811 0.790 0.740 0.725 0.711 0.016 0.024 0.036 0.038 0.04 0.045 
BP 0.847 0.800 0.780 0.730 0.713 0.692 0.018 0.027 0.04 0.042 0.042 0.05 
WaveNet 0.816 0.762 0.748 0.714 0.695 0.647 0.029 0.04 0.046 0.051 0.053 0.057 
CNN 0.654 0.542 0.498 0.421 0.398 0.342 0.059 0.077 0.092 0.103 0.126 0.167 
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