Unravelling oceanic wave patterns: A comparative study of machine learning approaches for predicting significant wave height

Abstract

Predicting significant wave height (SWH) is critical to ocean engineering, navigation, and renewable energy harvesting. This study comprehensively analyzes several machine learning (ML) and deep learning (DL) models. These include the adaptive neuro-fuzzy inference system with subtractive clustering ANFIS (SC), an artificial neural network with Bayesian regularization ANN (BR), bidirectional long-short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and BiGRU with attention mechanism (AM), all employed for the prediction of SWH. The models were evaluated using data from three stations: a Canadian station in the North Atlantic Ocean and two Chinese stations in the East China Sea. Our study demonstrates the superior performance of the ANFIS (SC) model across all stations. For instance, the ANFIS model achieved an R2 value of 0.9979 at the Canadian station, outperforming the gated recurrent unit (GRU) based model with an R2 of 0.9127 from previous research. Similarly, at the Lianyungang (LYG) station, the ANFIS model achieved an R2 value of 0.9812, surpassing the performance of the GRU network used in prior studies, which reached an R2 of 0.6436 for 6-hour forecasts. Moreover, our study outperforms the convolutional neural network (CNN) based BiGRUAM model used for shale oil production prediction regarding R2 values. Furthermore, compared to the dynamic ensemble ESN model used for wave height prediction, our ANFIS model demonstrated more robust and consistent performance. Therefore, this research significantly contributes to the field of SWH prediction by presenting the superior performance of the ANFIS model across different geographical locations and conditions. This research opens new avenues for future research and practical applications in oceanography, weather forecasting, and renewable energy harvesting.