Prediction of significant wave height in hurricane area of the Atlantic Ocean using the Bi-LSTM with attention model

Abstract

The Bi-LSTM with attention (BLA) model is proposed to predict wave height in the hurricane area of the Atlantic Ocean. Four data features (wave height, wind speed, wind direction, and wave direction) collected at five buoy stations are selected as model inputs, while future wave height is specified as model output. Predictions are obtained for 1-h, 3-h, 6-h, and 12-h lead times. Two evaluation metrics are introduced to evaluate the accuracy and stability of the model. Influencing factors such as input–output ratio and input feature combination are studied. For the input–output ratio, it is found that the shorter lead time requires a larger ratio, with a ratio of 9:1 required for 1-h lead time prediction, with 3:1 for 3-h prediction, 2:1 for 6-h prediction, and 1:1 for 12-h prediction. For the input feature combination, the combination of the wave height, the wind speed, and the coupling of the wind direction and wave direction via the cosine function holds the best prediction performance in 1-h and 3-h lead time prediction. And for 6-h and 12-h forecasts, a combination of wave height and wind speed is best. By comparing overall evaluation metrics and extreme wave height prediction results of the BLA model with those of the Bi-LSTM, LSTM, and LSTM with attention models, we notice that the BLA model has the best and most stable prediction performance.