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Table 6

Results of ensemble models for scenario 2

Piezometer numberEnsemble modelModel structureaDC
RMSEb
CalibrationVerificationCalibrationVerification
207 Simple averaging – 0.973 0.935 0.037 0.027 
Weighted averaging 0.2572, 0.2559, 0.2536, 0.2333 0.973 0.935 0.037 0.027 
Neural averaging 4-2-1 0.974 0.949 0.036 0.024 
217 Simple averaging – 0.955 0.788 0.056 0.097 
Weighted averaging 0.2538, 0.2613, 0.2620, 0.2230 0.955 0.788 0.056 0.098 
Neural averaging 4-7-1 0.948 0.809 0.060 0.093 
Piezometer numberEnsemble modelModel structureaDC
RMSEb
CalibrationVerificationCalibrationVerification
207 Simple averaging – 0.973 0.935 0.037 0.027 
Weighted averaging 0.2572, 0.2559, 0.2536, 0.2333 0.973 0.935 0.037 0.027 
Neural averaging 4-2-1 0.974 0.949 0.036 0.024 
217 Simple averaging – 0.955 0.788 0.056 0.097 
Weighted averaging 0.2538, 0.2613, 0.2620, 0.2230 0.955 0.788 0.056 0.098 
Neural averaging 4-7-1 0.948 0.809 0.060 0.093 

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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