论文标题
一种深度学习的方法，可以使用长期的短期记忆来预测明显的波高度
A deep learning approach to predict significant wave height using long short-term memory
论文作者
论文摘要
我们提出了一个框架，可以使用长期的短期记忆算法（LSTM）预测西南大西洋上的显着波高，并通过ECMWF（欧洲中心预测中心）实施的Copernicus气候数据存储（CDS）可用的ERE5数据库进行培训，并与Buoy数据一起训练。这些预测是对巴西海岸的七个不同地点进行的预测，那里有浮标数据，范围从浅水到深水。实验是在选定位置使用的独家历史系列进行的，并研究了其他变量作为训练的输入的影响。结果表明，与重新分析相比
We present a framework for forecasting significant wave height on the Southwestern Atlantic Ocean using the long short-term memory algorithm (LSTM), trained with the ERA5 database available through Copernicus Climate Data Store (CDS) implemented by ECMWF (European Center for Medium Range Forecast) and also with buoy data. The predictions are made for seven different locations in the Brazilian coast, where buoy data are available, ranging from shallow to deep water. Experiments are conducted using exclusively historical series at the selected locations and the influence of other variables as inputs for training is investigated. The results shows that a data-driven methodology can be used as a surrogate to the computational expensive physical models, with the best accuracy near $95\%$, compared to reanalysis
