基于灰狼优化支持向量回归的船舶航迹预测

Ship trajectory prediction based on grey wolf optimization support vector regression

为提高船舶航迹预测精度,利用灰狼优化(grey wolf optimization,GWO)算法求出支持向量回归(support vector regression,SVR)模型的最优参数,构建基于GWO-SVR的船舶航迹预测模型。选取福建漳州古雷港水域某船航迹的AIS数据。将该模型与其他模型的预测结果进行比较。实验结果表明,与SVR、灰狼优化最小二乘支持向量机和粒子群优化最小二乘支持向量机等3种模型相比,GWO-SVR模型的预测精度分别提升2.61%、10.93%和0.22%,预测误差分别降低0.022、0.303和0.172。本文方法提高了船舶航迹预测精度,可为海事监管人员及时作出正确决策和保障航行安全提供支持。

In order to improve the accuracy of ship trajectory prediction,the optimal parameters of the support vector regression(SVR)model are found by the grey wolf optimization(GWO)algorithm,and a ship trajectory prediction model based on GWO-SVR is constructed.The AIS data of trajectory of a ship of Gulei Port in Zhangzhou of Fujian province are selected.The model prediction result is compared with prediction results by other models.Experimental results show that,the prediction accuracy of GWO-SVR model is improved by 2.61%,10.93%and 0.22%,and the prediction error is reduced by 0.022,0.303 and 0.172,respectively,compared with SVR,the GWO-least squares support vector machine and the particle swarm optimization least squares support vector machine.The method in the paper improves the accuracy of ship trajectory prediction and can provide support for maritime supervisors to make correct decisions and ensure navigation safety in time.