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Table 5

Results of ensemble models for scenario 1

Piezometer numberEnsemble modelModel structureaDC
RMSEb
CalibrationVerificationCalibrationVerification
207 Simple averaging – 0.942 0.892 0.055 0.035 
Weighted averaging 0.2530, 0.2577, 0.2494, 0.2309 0.942 0.892 0.055 0.035 
Neural averaging 4-7-1 0.943 0.892 0.054 0.035 
217 Simple averaging – 0.882 0.716 0.091 0.113 
Weighted averaging 0.2464, 0.2550, 0.2602, 0.2385 0.882 0.715 0.090 0.113 
Neural averaging 4-5-1 0.841 0.757 0.105 0.104 
Piezometer numberEnsemble modelModel structureaDC
RMSEb
CalibrationVerificationCalibrationVerification
207 Simple averaging – 0.942 0.892 0.055 0.035 
Weighted averaging 0.2530, 0.2577, 0.2494, 0.2309 0.942 0.892 0.055 0.035 
Neural averaging 4-7-1 0.943 0.892 0.054 0.035 
217 Simple averaging – 0.882 0.716 0.091 0.113 
Weighted averaging 0.2464, 0.2550, 0.2602, 0.2385 0.882 0.715 0.090 0.113 
Neural averaging 4-5-1 0.841 0.757 0.105 0.104 

aNumbering of a,b,c,d in weighted averaging structure denotes to weights of FFNN, ANFIS, SVR, ARIMA models. Numbering of a-b-c in structure of neural averaging represents the number of input layer, hidden layer and output layer neurons.

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

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