Prediction Method for Ocean Wave Height Based on Stacking Ensemble Learning Model

Abstract

Wave heights are important factors affecting the safety of maritime navigation. This study proposed a stacking ensemble learning method to improve the prediction accuracy of wave heights. We analyzed the correlation between wave heights and other oceanic hydrological features, according to eleven features, such as measurement time, horizontal velocity, temperature, and pressure, as the model inputs. A fusion model consisting of two layers was established according to the principle of stacking ensemble learning. The first layer used the extreme gradient boosting algorithm, a light gradient boosting machine, random forest, and adaptive boosting to determine the deep relations between the wave heights and the input features. The second layer used a linear regression model to fit the relation between the first layer outputs and the actual wave heights, using the data from the four models of the first layer. The fusion model was trained based on the 5-fold cross-verification algorithm. This paper used real data to test the performances of the proposed fusion model, and the results showed that the mean absolute error and the mean squared error of the fusion model were at least 35.79% and 50.52% better than those of the four models.

Real-Time Underwater Acoustic Homing Weapon Target Recognition Based on a Stacking Technique of Ensemble Learning

“…Therefore, stacking ensemble learning is well suited for applications in scenarios with high real-time requirements. Currently, the stacking ensemble technique has been applied in various fields, including rainfall prediction [18], ocean wave height prediction [19], rock mass classification [20], and damage prediction [21].…”