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Table 7

Results of ensemble models for scenario 3

Piezometer numberEnsemble modelModel structureaDC
RMSEb
CalibrationVerificationCalibrationVerification
207 Simple averaging – 0.983 0.885 0.030 0.036 
Weighted averaging 0.2555, 0.2578, 0.2563, 0.2303 0.983 0.886 0.030 0.036 
Neural averaging 4-3-1 0.985 0.900 0.028 0.033 
217 Simple averaging – 0.961 0.767 0.052 0.102 
Weighted averaging 0.2575, 0.2644, 0.2603, 0.2177 0.962 0.768 0.051 0.102 
Neural averaging 4-4-1 0.955 0.821 0.056 0.089 
Piezometer numberEnsemble modelModel structureaDC
RMSEb
CalibrationVerificationCalibrationVerification
207 Simple averaging – 0.983 0.885 0.030 0.036 
Weighted averaging 0.2555, 0.2578, 0.2563, 0.2303 0.983 0.886 0.030 0.036 
Neural averaging 4-3-1 0.985 0.900 0.028 0.033 
217 Simple averaging – 0.961 0.767 0.052 0.102 
Weighted averaging 0.2575, 0.2644, 0.2603, 0.2177 0.962 0.768 0.051 0.102 
Neural averaging 4-4-1 0.955 0.821 0.056 0.089 

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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