The performance statistics of the model are shown in Table 1, where Ct, Qt and Lt denote the t-day salinity, runoff and high-tide level, respectively. As seen in Table 1, the number of influence factors increases from experiments 1 to 4. In addition, the GA-SVM model prediction accuracy improved for experiments 1 to 4 based on the R2, ENS and RMSE values. However, the prediction accuracy of the GA-SVM model decreased after adding the t-3 day salinity, t-3 day runoff and t-2 day high-tide level in experiment 5. Experiment 4 provides the best prediction accuracies based on the R2, ENS and RMSE.

Table 1

Experimental analysis of the salinity prediction results influenced by different combinations of factors

Evaluation index
IDInfluence factorsR2ENSRMSE(μS/cm)
Ct-1,Qt-2,Lt-1 0.49 0.53 193
Ct-1, Qt-1, Lt 0.60 0.62 174
Ct-1, Qt-1, Qt-2, Lt, Lt-1 0.76 0.78 142
Ct-1, Ct-2, Qt-1, Qt-2, Lt, Lt-1 0.83 0.84 126
Ct-1, Ct-2, Ct-3, Qt-1, Qt-2, Qt-3, Lt, Lt-1, Lt-2 0.78 0.80 138
Evaluation index
IDInfluence factorsR2ENSRMSE(μS/cm)
Ct-1,Qt-2,Lt-1 0.49 0.53 193
Ct-1, Qt-1, Lt 0.60 0.62 174
Ct-1, Qt-1, Qt-2, Lt, Lt-1 0.76 0.78 142
Ct-1, Ct-2, Qt-1, Qt-2, Lt, Lt-1 0.83 0.84 126
Ct-1, Ct-2, Ct-3, Qt-1, Qt-2, Qt-3, Lt, Lt-1, Lt-2 0.78 0.80 138

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