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The parameters C and γ of SVM were optimized by grid algorithm and performance statistics of the GA-SVM models for PMF-56 ET0 for the testing period are given in Table 3.

Table 3

Optimal SVM parameters with grid algorithm and the performance statistics of the SVM models during the training and testing periods

Parameter
Training periods
Testing periods
ModelInputCγrRMSE mm/dayMAE mm/dayrRMSE mm/dayMAE mm/day
SVM1 0.57 48.50 0.943 0.412 0.282 0.947 0.433 0.321 
SVM2 0.57 5.28 0.961 0.342 0.237 0.969 0.329 0.251 
SVM3 0.33 16 0.975 0.275 0.153 0.989 0.206 0.150 
SVM4 5.73 0.953 0.374 0.268 0.951 0.404 0.308 
SVM5 0.57 5.29 0.967 0.313 0.216 0.965 0.347 0.262 
SVM6 9.19 0.980 0.245 0.124 0.992 0.179 0.129 
SVM7 16 0.987 0.201 0.106 0.988 0.211 0.153 
SVM8 0.58 9.19 0.986 0.205 0.104 0.994 0.148 0.114 
Parameter
Training periods
Testing periods
ModelInputCγrRMSE mm/dayMAE mm/dayrRMSE mm/dayMAE mm/day
SVM1 0.57 48.50 0.943 0.412 0.282 0.947 0.433 0.321 
SVM2 0.57 5.28 0.961 0.342 0.237 0.969 0.329 0.251 
SVM3 0.33 16 0.975 0.275 0.153 0.989 0.206 0.150 
SVM4 5.73 0.953 0.374 0.268 0.951 0.404 0.308 
SVM5 0.57 5.29 0.967 0.313 0.216 0.965 0.347 0.262 
SVM6 9.19 0.980 0.245 0.124 0.992 0.179 0.129 
SVM7 16 0.987 0.201 0.106 0.988 0.211 0.153 
SVM8 0.58 9.19 0.986 0.205 0.104 0.994 0.148 0.114 

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