This study employs the XBeach surfbeat model (XBSB) to explore the effects of vegetation on wave attenuation and dune erosion in a case study of Mexico Beach during Hurricane Michael. The XBSB model was validated against laboratory experiments of wave-induced dune erosion and wave attenuation by vegetation. In the case study of vegetation on dunes in Mexico Beach during Hurricane Michael, different vegetation drag coefficients were evaluated to investigate the effects of vegetation on wave attenuation and dune erosion. LiDAR data of dune profiles before and after Hurricane Michael were used for model validation. The findings reveal that vegetation on dunes significantly affects wave attenuation and dune erosion. Under vegetated conditions, as the vegetation drag coefficient value increases, wave attenuation also increases, leading to a reduction of dune erosion. An increase in vegetation density enhances wave attenuation in the vegetated area, including reductions in significant wave height and flow velocity. However, the rate of change in attenuation decreases as the vegetation density increases. Through simulations under regular wave condition on Mexico Beach, an optimal vegetation density was identified as 800 units/m². Beyond this density, additional vegetation does not substantially improve wave attenuation. Furthermore, the position of the dune crest elevation is related to the location where the alongshore flow velocity begins to decrease. The findings highlight the essential role of coastal vegetation in enhancing coastal resilience against hurricanes.

An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index (ETI) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting.

Accurate wave forecasting under typhoon conditions is essential for coastal safety in the Pearl River Estuary. This study explores the use of Random Forest (RF) and Long Short-Term Memory (LSTM) models to predict significant wave heights, using SWAN-simulated data from 87 historical typhoon events. Ten representative typhoons were reserved for independent testing. Results show that the LSTM model outperforms RF in 3 h forecasts, achieving a lower mean RMSE and higher R², particularly in capturing wave peaks under highly dynamic conditions. For 6 h forecasts, both models exhibit decreased accuracy, with RF performing slightly better in stable scenarios, while LSTM remains more responsive in complex wave evolution. Generalization tests at three nearby stations demonstrate that both models, especially LSTM, retain strong predictive skill beyond the training location. These findings highlight the potential of combining numerical wave models with machine learning for short-term, data-driven wave forecasting in typhoon-prone and observation-sparse regions. The study also points to future improvements through integration of wind field predictors, model updating strategies, and ensemble meteorological data.

The shipping industry faces a pressing challenge with carbon emissions, prompting a focus on speed optimization for energy conservation and emission reduction. While much research has centered on optimizing speeds in oceans and rivers, canals have received less attention, despite their unique challenges of narrow waterways and busy locks. This study fills this gap by establishing a fuel consumption prediction model integrating key environmental factors such as water depth, width, and flow velocity. Drawing upon established methodologies in speed optimization, this study augments these models with waiting time limits for each canal segment. To validate the efficacy of the model, three representative ships are selected as case studies. The findings reveal a high predictive capability of the fuel consumption model, as evidenced by R² values exceeding 0.97 across all cases. Notably, the optimization approach yields a fuel consumption reduction ranging from 4% to 5% for short waiting times. Furthermore, compared to conventional methods, the proposed optimization strategy achieves an 8.19% enhancement in fuel consumption and carbon emission reduction for long waiting times, culminating in an overall optimization rate of 11.54%. These results underscore the significance of employing the proposed speed optimization methodology, particularly during peak periods of canal congestion.