An Integrated Long short Term Memory Algorithm for Predicting Polar Westerlies Wave Height

The improved knowledge of wave height and period conditions has considerably influenced on ocean navigation, marine fishery and engineering, especially in the polar regions. The methods of predicting ocean wave height which involve field measurements, numerical simulation, physical models and analytical solutions have been gradually developed with intelligent functions. Despite numerical wave models being dominant for recent decades, wave forecasting is still facing many challenges such as small region forecasting and large amounts of data needed. This paper presents a novel deep learning algorithm, namely Long Short Term Memory (LSTM), incorporating with Principal Component Analysis (PCA) to predict the wave height by using data from four wave buoys as deployed in the polar westerlies for two and half months. The PCA method is used to extract principal components from a set of input signals while LSTM is adopted to avoid long term independences during the forecasting. The novelty of this paper is to investigate an artificial intelligence (AI) based model in the field of time sequence forecasting in order to determine the performance of wave conditions by using AI technology. The result from this integrated method demonstrates that the LSTM model has the potential to better predict wave height in the polar condition based on time-space domain information. The PCA is proved essential for selection of input signals and for correlation analysis. For comparison, different data-driven models are applied and the results also show the purposed model achieves the highest scores in terms of R-squared value and root mean square error. Besides, the paper also discusses the challenges for long term and high-value prediction which needs to be optimized in the future work