基于Stacking集成机器学习的波浪预报

采用多层感知器模型、随机森林模型为第一层子模型，极端树模型为第二层元模型，建立基于Stacking集成机器学习的波浪预报算法，并引入邻域平均法抑制在拐点处产生的数值震荡。以长江口外海2016年1—9月的风速和中国近海波高数据为数据源，利用机器学习风速与有效波高之间的关系，将2016年10—11月的风速、波高数据用于预报结果的对比分析，预报前45 d R²拟合优度达到0.97以上，平均误差最大值为0.08 m，平均相对误差最大值为0.05，预报结果与波浪谱模型结果趋势一致，准确度较高；预报结果后15 d误差增长较快，这与训练集数据中寒潮浪占比较少有关。

Wave forecasting algorithm with stacking ensemble machine learning method

A wave forecasting algorithm is established based on the stacking ensemble machine learning method. The algorithm includes two sublayers. The multi-layer perceptron model and the random forest model are used in the first sublayer, and the extreme randomized tree model is used in the second sublayer. To suppress the numerical oscillation of the predicted results, the neighborhood averaging method is introduced into the algorithm. Toestablish relations between the wind speed and the significant wave height, the wind speed and China wave(CWAVE)data from January to September 2016 in the offshore area of the Yangtze River Estuary are used. By using the wind data from October to November 2016, the corresponding significant wave height is predicted by the wave forecasting algorithm. The results show that the values of R² between the predicted wave height and CWAVE data are larger than 0. 97 in the 45 days from 1 October, the mean error is less than 0. 08 m, and the mean relative error is less than 0. 05. The trends of the variations of the significant wave height can be accurately predicted by the algorithm. However, the error increases in the last 15 day, which is due to lack of cold wave data in the training set.