A Deep Learning Approach for Wave Forecasting Based on a Spatially Correlated Wind Feature, with a Case Study in the Java Sea, Indonesia

Didit Adytia,Sri Redjeki Pudjaprasetya,Deni Saepudin,Semeidi Husrin,Ardhasena Sopaheluwakan

doi:10.3390/fluids7010039

Abstract

For safety and survival at sea and on the shore, wave predictions are essential for marine-related activities, such as harbor operations, naval navigation, and other coastal and offshore activities. In general, wave height predictions rely heavily on numerical simulations. The computational cost of such a simulation can be very high (and it can be time-consuming), especially when considering a complex coastal area, since these simulations require high-resolution grids. This study utilized a deep learning technique called bidirectional long short-term memory (BiLSTM) for wave forecasting to save computing time and to produce accurate predictions. The deep learning method was trained using wave data obtained by a continuous numerical wave simulation using the SWAN wave model over a 20-year period with ECMWF ERA-5 wind data. We utilized highly spatially correlated wind as input for the deep learning method to select the best feature for wave forecasting. We chose an area with a complex geometry as the study case, an area in Indonesia’s Java Sea. We also compared the results of wave prediction using BiLSTM with those of other methods, i.e., LSTM, support vector regression (SVR), and a generalized regression neural network (GRNN). The forecasting results using the BiLSTM were the best, with a correlation coefficient of 0.96 and an RMSE value of 0.06.

Highlights

Shipping and other marine-related operations are highly dependent on weather conditions
For the Generalized Regression Neural Network (GRNN), we found that a spreading parameter of s = 1 yielded the best performance
Results and Discussion we investigate the results of prediction using four machine learning methods, i.e., Support Vector Regression (SVR), the Generalized Regression Neural Network (GRNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), with two types of configuration

Summary

Introduction

Shipping and other marine-related operations are highly dependent on weather conditions. A range of technologies, methodologies, and historical data are used to forecast future weather conditions [1]. The height of the sea waves is a critical meteorological condition. The length of a wave height forecast is often determined by the significance of the activity [2]. High sea waves might endanger a ship by overturning it, resulting in significant losses. This can be prevented by optimizing the shipping route. Forecasting wave height might help to avoid dangerous conditions while simultaneously enhancing productivity and preserving fuel [3]. Waves at sea can be considered one renewable energy source, since they continuously propagate as the wind blows. It is critical to estimate wave heights accurately to provide an overview of current ocean wave conditions [5]

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Conclusion