Based on the Conditional Restricted Boltzmann Machine - Deep Belief Network (CRBM-DBN), we select four patterns and compare their prediction effects for the significant wave height in the Gulf of Mexico (GoM). Historical datasets of all 12 buoys managed by the National Data Buoy Center are employed to train and construct models. Root-mean-square error (RMSE) and coefficient of efficiency (CE) between the observed and predicted wave heights are investigated. We find that for the short-term prediction (i.e., lead time≤12 h), the best results (RMSE<0.24 m and CE > 0.92) are achieved with the univariate significant wave height as the input in most cases of the whole gulf. When the lead time is equal to 24 h or 48 h, the multivariate pattern of “significant wave height + dominant wave direction + wind speed + wind direction” has the optimal effects (0.18 m < RMSE<0.40 m and 0.72 < CE < 0.93) in the vicinity of 26^。N oceans. The superiority is very obvious and gradually diminishes as the latitude increases to the north and decreases to the south. Regarding the wave height predictions in different oceans of GoM, the findings provide evidence that it may be contributed to select optimal prediction patterns and obtain the best applications.

基于条件受限玻尔兹曼机 - 深度信念网络 (CRBM-DBN)，我们选择了四种模式并比较了它们对墨西哥湾 (GoM) 显着波高的预测效果。使用国家数据浮标中心管理的所有 12 个浮标的历史数据集来训练和构建模型。研究了观测波高和预测波高之间的均方根误差 (RMSE) 和效率系数 (CE)。我们发现，对于短期预测（即提前期≤12 h），在整个海湾的大多数情况下，以单变量有效波高作为输入获得最佳结果（RMSE<0.24 m 和 CE > 0.92） . 当前置时间等于 24 小时或 48 小时时，^。 Ñ海洋。优势非常明显，随着纬度向北增加向南减少而逐渐减弱。关于GoM不同海洋的波高预测，研究结果提供了证据表明它可能有助于选择最佳预测模式并获得最佳应用。