An integrated long-short term memory algorithm for predicting polar westerlies wave height

Abstract

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. Besides, the paper also discusses the challenges for long term and high-value prediction which needs to be optimized in the future work.

Significant wave height forecasting using WRF-CLSF model in Taiwan strait

“…In recent years, Ali and Prasad (2019), Deka and Prahlada (2012), Duan et al (2016) and Ni and Ma (2020) combined machine learning method with a decomposition of sea wave observations to predict significant wave height. For example, Ali and Prasad (2019) combined extreme learning machine with empirical value decomposition to forecast the future height.…”

“…Deka and Prahlada (2012) combined wavelet decomposition with the neural network for prediction work. Ni and Ma (2020) used the principal component analysis to predict the wave height in the polar region. Many studies using wave decomposition show that different factors will affect the future wave height trend.…”

“…In recent years, researchers have applied the LSTM model (libin et al, 2018), and GRU model (Wang et al, 2021) to study the prediction in the coastal waters of Taiwan Strait. And, Ni and Ma (2020) also used the LSTM model to study the prediction in polar regions. From the above analysis, it shows that the LSTM model is a feasible approach for the prediction work.…”

Bidirectional Modeling of Surface Winds and Significant Wave Heights in the Caribbean Sea

“…Widely used in oceanographic studies, the long short-term memory (LSTM) recurrent neural network has been applied primarily to forecast significant wave height. For example, Ni and Ma [22] used LSTM and Principal Component Analysis (PCA)-identified parameters to predict wave height from four buoys in the polar westerlies. Pushpam and Enigo used LSTM trained on three years of buoy data to perform 3, 6, 12, and 24 h significant wave height predictions [23].…”

CLTS-Net: A More Accurate and Universal Method for the Long-Term Prediction of Significant Wave Height

“…However, the above method can only be applied to forecasts in a relatively short period of time under normal conditions, while the forecasts under extreme conditions are not ideal. In addition, with the increase in the number of inputs and the increase in complexity, the accuracy of the ANN may drop sharply because the model cannot extract enough features [23].…”