Combined forecasting model of ocean current based on improved variational mode decomposition and least square support vector machine

Accurate and reliable forecasting of ocean current is important for the study of ocean activity. Aiming at the nonstationary and nonlinear characteristics of ocean current, a combined forecasting model of ocean current based on improved variational mode decomposition and least square support vector machine is proposed. Aiming at the problem that decomposition level and penalty factor of variational mode decomposition (VMD) must be set manually, VMD based on love evolution algorithm (LEA) is proposed, named LVMD. Aiming at the problem of solid randomness in the selection of penalty coefficient and kernel penalty parameter in least squares support vector machine (LSSVM), LSSVM based on LEA is proposed, named LEA-LSSVM. First, use LVMD to decompose ocean current data, and obtain intrinsic mode functions (IMFs). Then, use LEA-LSSVM to forecast IMFs. Finally, superimpose forecasting results of IMFs to obtain the final forecasting result. Ocean current data from January to February, 2022 in National Ocean Science Data Center are selected as experimental data, and seven comparison models are selected to compare with the proposed forecasting model. The results show that RMSE, MAE, MAPE and R² are 0.6326, 0.4881, 0.0111 and 0.9990, respectively. The indexes of the proposed forecasting model are all better than those of the comparison model. It proves that the proposed forecasting model has higher prediction accuracy and provides a new method for ocean current forecasting.