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The parameters C and γ of SVM were optimized by GA. Performance statistics of the GA-SVM models for PMF-56 ET0 for the training and testing periods are given in Table 2. It was found that the difference between the values of the statistical indices of the training and validation set did not vary substantially.

Table 2

Optimal SVM parameters with GA and the performance statistics of the GA-SVM models during the training and testing periods

Parameter
Training periods
Testing periods
ModelInputCγrRMSE mm/dayMAE mm/dayrRMSE mm/dayMAE mm/day
GA-SVM1 9.64 0.940 0.422 0.294 0.948 0.424 0.311 
GA-SVM2 3.01 1.32 0.959 0.353 0.247 0.972 0.314 0.241 
GA-SVM3 4.13 8.81 0.975 0.273 0.151 0.990 0.201 0.147 
GA-SVM4 15.11 0.53 0.948 0.390 0.287 0.955 0.396 0.298 
GA-SVM5 1.68 1.24 0.963 0.331 0.234 0.971 0.316 0.241 
GA-SVM6 0.69 2.61 0.977 0.263 0.143 0.993 0.163 0.124 
GA-SVM7 3.88 8.07 0.985 0.213 0.113 0.991 0.175 0.132 
GA-SVM8 29.13 0.27 0.980 0.249 0.137 0.995 0.138 0.106 
Parameter
Training periods
Testing periods
ModelInputCγrRMSE mm/dayMAE mm/dayrRMSE mm/dayMAE mm/day
GA-SVM1 9.64 0.940 0.422 0.294 0.948 0.424 0.311 
GA-SVM2 3.01 1.32 0.959 0.353 0.247 0.972 0.314 0.241 
GA-SVM3 4.13 8.81 0.975 0.273 0.151 0.990 0.201 0.147 
GA-SVM4 15.11 0.53 0.948 0.390 0.287 0.955 0.396 0.298 
GA-SVM5 1.68 1.24 0.963 0.331 0.234 0.971 0.316 0.241 
GA-SVM6 0.69 2.61 0.977 0.263 0.143 0.993 0.163 0.124 
GA-SVM7 3.88 8.07 0.985 0.213 0.113 0.991 0.175 0.132 
GA-SVM8 29.13 0.27 0.980 0.249 0.137 0.995 0.138 0.106 

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