An AI model for predicting the spatiotemporal evolution process of coastal waves by using the Improved-STID algorithm

Abstract

Accurate and rapid prediction of water wave evolution is crucial for ensuring the safe and healthy operation of ocean and coastal engineering. Using hourly wave data from the coastal areas of the Hawaiian Islands from 2019 to 2021, this study developed an artificial intelligence model for rapidly predicting the spatiotemporal evolution of coastal waves by improving the STID algorithm. In the study, the SWAN wave model was used to generate an initial training dataset for driving Improved-STID algorithm, where the Improved-STID algorithm and SWAN model share the same unstructured computational grid with good adaptability to complex topography. The proposed Improved-STID algorithm takes into account the overall spatiotemporal correlations of wave evolution across different regions. The results show that on the basis of prediction efficiency on a par with the original STID, the Improved-STID can be more accurately applied to the spatiotemporal evolution process of coastal waves. The prediction errors in this study are: the RMSE of 0.23 m, MAE of 0.14 m, MAPE of 17.96 %, and the calculation time of the proposed AI model is only about 1.8 % of that of the SWAN wave model.