The increasing congestion in major global maritime routes poses significant threats to international maritime safety, exacerbated by the proliferation of large, high-speed vessels. To improve the detection of abnormal ship behavior, this research employed automatic identification system (AIS) data for ship trajectory forecasting. Traditional methods primarily focus on spatial and temporal correlations but often lack accuracy and reliability. In this study, ship path predictions were enhanced using the WTG model, which combines wavelet transform, temporal convolutional networks (TCN), and gated recurrent units (GRU). Initially, wavelet decomposition was applied to deconstruct the input trajectory time series. The TCN and GRU modules then extracted features from both the time series and the decomposed data. The predicted elements were reassembled using a multi-head attention mechanism and a pooling layer to produce the final predictions. Comparative experiments demonstrated that the WTG model surpasses other models in the accuracy of ship trajectory prediction. The model proposed in this study proves to be reliable for forecasting ship paths, which is crucial for marine traffic management and ensuring safe navigation.

Tsunamis, generated by submarine earthquakes, landslides, or volcanic eruptions, are a significant hazard to coastal areas owing to their sudden onset and rapid propagation speed. Thus, tsunami disaster risk assessment is crucial to determine potential losses and severity of future tsunami impacts, providing essential support for disaster prevention and mitigation efforts. This study presents a case study of Xiamen City in Fujian province for developing a high-precision tsunami model. Utilizing nearshore water depth and digital elevation model data, we established a high-resolution tsunami numerical simulation model for Xiamen City that accounts for the impact of water overrunning levees and overshoals. Historical tsunami disaster records were used to validate the numerical model. By determining multiple tsunami source scenarios that could potentially affect the counties of Xiamen City, we simulated the inundation range and water depth distribution required for the potential maximum tsunami event. The simulation results facilitated a tsunami hazard assessment. Considering land use and important tsunami-affected exposures including industrial and chemical enterprises and ports, we evaluated the vulnerability of Xiamen City to tsunami disasters. Based on the determination of hazard and vulnerability levels, we investigated the risk distribution of tsunami disasters in Xiamen City. The results of this study lay the groundwork for developing methodologies to improve tsunami disaster risk assessment in coastal areas.

Continuous and reliable offshore wind speed time series are essential for offshore wind resource assessment, power forecasting, and long-term wind energy analysis. However, ERA5 reanalysis data may contain site-scale biases over offshore areas, and missing records in offshore wind-tower observations limit their practical use. To improve the reliability of offshore wind speed series, this study proposes a residual learning framework for ERA5 bias correction and continuous gap reconstruction by integrating ERA5 background fields with target- and neighboring-station information. Using FINO1 and FINO3 observations in the North Sea from 2018 to 2025, ERA5 wind-vector components were matched with tower observations, and residuals of the east–west and north–south wind components were used as prediction targets. Statistical methods, linear models, tree-based ensemble models, and GRU models were evaluated under a cross-year framework, with additional analyses of historical residuals, neighboring-station information, and wind-direction-related features. Results show that target-station historical residuals dominate site-scale correction, reducing RMSE by 50.77% and 32.69% for FINO1 and FINO3, respectively, under a 1 h observation lag. Neighboring-station information provides useful constraints when local observations are limited or missing. The framework maintains stable improvements across years and strong-wind conditions and supports reliable offshore wind speed reconstruction for wind energy applications.