Application of an intelligent wave forecasting method to waters around the islands and reefs in the South China Sea

Wang Fayun; Wang Shengjian; Chen Bin; Liang Naian; Qiu Liguo

doi:10.12284/hyxb20250111

Volume 47 Issue 12

Dec. 2025

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Wang Fayun，Wang Shengjian，Chen Bin, et al. Application of an intelligent wave forecasting method to waters around the islands and reefs in the South China Sea[J]. Haiyang Xuebao，2025, 47(12)：136–149 doi: 10.12284/hyxb20250111

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Application of an intelligent wave forecasting method to waters around the islands and reefs in the South China Sea

doi: 10.12284/hyxb20250111

Hainan Academy of Ocean and Fisheries Sciences, Haikou 571126, China

Received Date: 2025-08-25
Rev Recd Date: 2025-11-13

Available Online: 2025-11-22

Publish Date: 2025-12-31

Abstract

Abstract

This study aims to enhance wave forecast accuracy and model generalization in the South China Sea island and reef waters using a BO-LSTM model. We systematically investigated the effects of input factors, model cross-station transferability, and multi-step prediction performance. The research employed single-factor (historical wave height) and dual-factor (historical wave height + wind speed) input schemes. Forecasts for 1−24 h were generated and validated at four stations (Qilianyu, Ganquan Island, Jinqing Island, and Huaxia Shoal) using the Rolling Forecast (RF) and Direct Multi-step (DM) methods. Results show that model performance is highly correlated with the hydrodynamic environment dictated by station geomorphology. The model trained on data from Qilianyu Station, with its “semi-sheltered to semi-open” setting, demonstrated the strongest and most stable generalization ability (optimal window n = 2) and excellent cross-station performance. In contrast, models for the “localized lagoon” stations (Ganquan Island, Jinqing Island) and the “open water” station (Huaxia Shoal) exhibited limited transferability due to significant “data domain shift” arising from their distinct geographic settings. For short-term forecasts, historical wave height was the core input factor, but its dominance showed geographic dependence. Its weight was significantly higher (>1.7 times) than wind speed at Qilianyu and Jinqing Island, while wind speed contribution was greater (advantage ratio <1.4) at Ganquan Island and Huaxia Shoal. For multi-step forecasting, “DM + Dual” performed best for short-to-medium terms (1−18 h), whereas “RF + Dual” was superior for long-term forecasts (19−24 h) and at Ganquan Island across all horizons. This study validates BO-LSTM’s effectiveness for wave forecasting in the South China Sea and provides physically interpretable insights for developing regional intelligent forecasting models by linking data-driven patterns with geophysical mechanisms.
- wave forecasting,
- BO-LSTM,
- island and reef waters in the South China Sea,
- cross-station transfer,
- data domain shift

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