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Table 8

Results of neural ensemble models in comparison of individual AI models for verification step

Piezometer numberModelDC
RMSEa
Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3
207 FFNN 0.698 0.803 0.621 0.036 0.029 0.042 
ANFIS 0.702 0.521 0.266 0.036 0.045 0.056 
SVR 0.613 0.682 0.561 0.041 0.037 0.043 
Neural averaging 0.716 0.866 0.688 0.035 0.024 0.042 
217 FFNN 0.392 0.632 0.601 0.116 0.090 0.105 
ANFIS 0.403 0.339 0.354 0.115 0.121 0.120 
SVR 0.420 0.312 0.435 0.135 0.124 0.122 
Neural averaging 0.516 0.687 0.692 0.104 0.084 0.088 
Piezometer numberModelDC
RMSEa
Scenario 1Scenario 2Scenario 3Scenario 1Scenario 2Scenario 3
207 FFNN 0.698 0.803 0.621 0.036 0.029 0.042 
ANFIS 0.702 0.521 0.266 0.036 0.045 0.056 
SVR 0.613 0.682 0.561 0.041 0.037 0.043 
Neural averaging 0.716 0.866 0.688 0.035 0.024 0.042 
217 FFNN 0.392 0.632 0.601 0.116 0.090 0.105 
ANFIS 0.403 0.339 0.354 0.115 0.121 0.120 
SVR 0.420 0.312 0.435 0.135 0.124 0.122 
Neural averaging 0.516 0.687 0.692 0.104 0.084 0.088 

aSince all data are normalized, the RMSE has no dimension.

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