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Table 2

Results of single models for scenario 1

Piezometer numberModelModel structureaDC
RMSEb
CalibrationVerificationCalibrationVerification
207 FFNN 3-8-1 0.929 0.887 0.061 0.036 
ANFIS Gaussian-3 0.946 0.876 0.053 0.037 
SVR 0.333, 0.01, 15 0.915 0.883 0.067 0.038 
ARIMA (5,2,4) 0.880 0.764 0.074 0.056 
217 FFNN 3-5-1 0.827 0.756 0.110 0.104 
ANFIS Trapezoidal-2 0.856 0.690 0.100 0.118 
SVR 0.333, 0.1, 30 0.873 0.667 0.094 0.121 
ARIMA (5,2,4) 0.800 0.571 0.117 0.136 
Piezometer numberModelModel structureaDC
RMSEb
CalibrationVerificationCalibrationVerification
207 FFNN 3-8-1 0.929 0.887 0.061 0.036 
ANFIS Gaussian-3 0.946 0.876 0.053 0.037 
SVR 0.333, 0.01, 15 0.915 0.883 0.067 0.038 
ARIMA (5,2,4) 0.880 0.764 0.074 0.056 
217 FFNN 3-5-1 0.827 0.756 0.110 0.104 
ANFIS Trapezoidal-2 0.856 0.690 0.100 0.118 
SVR 0.333, 0.1, 30 0.873 0.667 0.094 0.121 
ARIMA (5,2,4) 0.800 0.571 0.117 0.136 

aNumbering of a-b-c in structure of neural network represents the number of input layer, hidden layer and output layer neurons. In ANFIS, structure MF-a refers to used membership function and number of membership functions. Numbering of a, b, c in SVR structure denotes to γ, ɛ, c and numbering of (a, b, c) in ARIMA structure refers to orders of autoregressive and moving average components and the order of differencing, respectively.

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

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