This paper proposes a machine learning method based on the Extra Trees (ET) algorithm for forecasting Significant Wave Heights in oceanic waters. To derive multiple features from the CDIP buoys, which make point measurements, we first nowcast various parameters and then forecast them at 30-min intervals. The proposed algorithm has Scatter Index (SI), Bias, Correlation Coefficient, Root Mean Squared Error (RMSE) of 0.130, -0.002, 0.97, and 0.14, respectively, for one day ahead prediction and 0.110, -0.001, 0.98, and 0.122, respectively, for 14-day ahead prediction on the testing dataset. While other state-of-the-art methods can only forecast up to 120 hours ahead, we extend it further to 14 days. This 14-day limit is not the forecasting limit, but it arises due to our experiment's setup. Our proposed setup includes spectral features, hv-block cross-validation, and stringent QC criteria. The proposed algorithm performs significantly better than the state-of-the-art methods commonly used for significant wave height forecasting for one-day ahead prediction. Moreover, the improved performance of the proposed machine learning method compared to the numerical methods, shows that this performance can be extended to even longer time periods allowing for early prediction of significant wave heights in oceanic waters.
翻译:本文建议了一种基于预测大洋水域重大波高的“额外树”算法的机器学习方法。为了从具有点测量的CDIP浮标中得出多种特征,我们先是先播各种参数,然后每隔30分钟预报。提议的算法有散射指数(SI)、比亚斯、相交系数、根正正方差错误(RMSE)分别为0.130、0.002、0.97和0.14,分别用于提前一天预测和0.110、0.001、0.98和0.122,分别用于测试数据集的提前14天预测。虽然其他最先进的方法只能预测到前面120小时,但我们将它进一步扩展至14天。这14天的算法不是预测极限,而是由于我们实验的设置而产生。我们提议的设置包括光谱特征、hv区交叉校准和严格的QC标准。提议的算法比通常用于提前一天进行重大浪高预报的状态方法要好得多。此外,对于远一天预测来说,更先进的其他最先进方法只能预测120小时,我们只能将它延长到14天。这14天。这14天的14天的14天的限制不是预测期限,但这14天的限期,但这14天的限期是由于我们的实验性极限,但是由于我们的实验而产生。我们提议的海洋早期预测方法的更长时间的改进了,因此,因此可以使远的测算法可以使远的测算法的测得更长的年的测得。