This study numerically investigates characteristics of nonlinear periodic waves generated by a moving bottom wavemaker. A high-order spectral (HOS) method incorporating the moving bottom boundary condition was adopted for simulations of waves initiated by triangular- and rectangular-shaped bottom wavemakers in both two- and three-dimensional settings, and a linear analytical solution was also derived. Results indicate that larger oscillation amplitudes intensify nonlinear effects, producing sharper crests, flatter troughs, and significantly higher harmonics. In three dimensions, snake-type oscillations successfully reproduced obliquely propagating waves. Shadow zones formed perpendicular to the propagation direction, accompanied by diffraction-induced energy transfer and oscillations along crests and troughs. The HOS model closely matched the analytical solution at weak forcing, but under strong nonlinearity, it captured harmonic generation and waveform deformation beyond the linear prediction. This work extends the application of bottom wavemakers from impulsive tsunami-type motions to periodic oscillations, providing new theoretical insights into nonlinear wave dynamics.

The distribution of air-borne pollutants is governed by complex fluid dynamics processes involving convection and diffusion. The process is further affected by the characteristics of emission sources, meteorological parameters, socioeconomic factors, and land use patterns. Compared to deterministic and probabilistic air quality forecasting methods, data driven modeling of air quality parameters can address the large degree of freedom in air quality influencing parameters as well as offer interpretability and understanding of air pollutants' distribution at an increased spatial and temporal resolutions. This study focuses on the citywide prediction of air quality index (AQI) based on observations of pollutant concentrations, meteorological parameters, and spatiotemporal data. The study area includes Ansan city in South Korea, which has been observed as a hotspot for high concentrations of particulate matter. The air quality and meteorological were collected from 16 monitoring stations located in Ansan city. A detailed spatiotemporal analysis was performed to investigate the correlation between AQI records at the air quality monitoring stations. Based on strong spatiotemporal correlations observed between stations, several deep learning (DL) models were proposed, and their performance was investigated for different scenarios. It was observed that the selection of appropriate DL models should be based on (1) understanding of the underlying fluid dynamics process that control pollutant distribution and (2) spatiotemporal characteristics of data. Additionally, the complexity of DL models does not always guarantee the accuracy of the forecasts, and simple models can give good performance if the predictors are selected carefully to reflect the underlying physical process.