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Table 7

Comparison of performance indicators

Prediction methodsRMSE (108 m3)MAE (108 m3)MAPE (%)RRMSESSE (108m3)TICIA
Proposed approach 0.1686 0.4625 0.5013 0.0052 1.3645 0.7079 0.0026 0.9075 
ARMA 0.3935 0.9650 1.0524 0.0122 7.4340 0.5914 0.0061 0.8230 
SVM 0.3614 0.8939 0.9703 0.0111 6.2677 0.3417 0.0055 0.8062 
LSSVM 0.3123 0.7340 0.8011 0.0097 4.6819 0.4123 0.0048 0.8656 
BP neural network 0.3088 0.6899 0.7516 0.0095 4.5768 0.3202 0.0047 0.8728 
Elman neural network 0.3610 0.9413 1.0215 0.0111 6.2553 0.3391 0.0055 0.6954 
Prediction methodsRMSE (108 m3)MAE (108 m3)MAPE (%)RRMSESSE (108m3)TICIA
Proposed approach 0.1686 0.4625 0.5013 0.0052 1.3645 0.7079 0.0026 0.9075 
ARMA 0.3935 0.9650 1.0524 0.0122 7.4340 0.5914 0.0061 0.8230 
SVM 0.3614 0.8939 0.9703 0.0111 6.2677 0.3417 0.0055 0.8062 
LSSVM 0.3123 0.7340 0.8011 0.0097 4.6819 0.4123 0.0048 0.8656 
BP neural network 0.3088 0.6899 0.7516 0.0095 4.5768 0.3202 0.0047 0.8728 
Elman neural network 0.3610 0.9413 1.0215 0.0111 6.2553 0.3391 0.0055 0.6954 
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