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In response to the incident of tourists falling into the sea due to waves on the seawall berm at Macau Road, Qingdao, during the passage of Typhoon "Songda" in 2022, a combination of numerical simulations and physical model experiments was performed to investigate the mechanics of the event, with emphasis on the wave flow characteristics and the flow evolution process on the seawall berm as well as the force exerted on a human body-equivalent cylinder model. The study found that the thickness of the return flow was significantly greater than that of the overtopping flow on the landward part of the berm. The recoil forces applied to the model on the berm were larger than the impact forces, and the ratio tended towards 1 as the wave height increased. In addition, the stability of pedestrians on the seawall berm was analyzed. The instability conditions for pedestrians in cross-wave flows differed slightly from those in floods. [ABSTRACT FROM AUTHOR]

The terminal settling velocity of microplastics plays a vital role in the physical behavior of microplastics, and is related to the migration and fate of these microplastics in the ocean. At present, the terminal settling velocity is mostly calculated by formulae, which also leads to a fewer studies on the use of machine-learning models to predict its settling velocity in this field. This study fills this gap by studying the prediction of the settling velocity by machine-learning models and compares it with the traditional formula calculation method. This study evaluates three machine-learning models, namely, random forest, linear regression, and the back propagation neural network. The results of this study show that the prediction results of the three machine-learning models are more accurate than those of traditional formula calculations, with an accuracy increase of 12.79% (random forest), 9.3% (linear regression), and 13.92% (back propagation neural network), respectively. At the same time, according to the results of this study, random forest is better than the other models in the mean absolute error and root mean square error evaluation indicators, which are only 0.0036 and 0.0047. This paper proposes three machine-learning methods to prove that the prediction effect of machine learning is much better than traditional formula calculations, thereby improving the shortcomings in this field. At the same time, it also provides reliable data support for studying the migration behavior of microplastics in water bodies. [ABSTRACT FROM AUTHOR]