According to a field survey in 2012, the bathymetry near Mailiao Port, located in central Taiwan’s west coast, has a scouring hole that extends approximately 500 m × 100 m with a maximum scour depth of 26 m (eroded from its design depth −22 m to −48 m). To investigate the scour mechanism near the breakwater head and prevent the breakwater from collapsing, this study conducts comprehensive analysis by analyzing field observed data, performing hydraulic model tests, and conducting numerical modeling for the area near the Mailiao Port. The results show that the current plays an important role in the scour process. The physical model tests and numerical simulations both can reproduce the scour phenomenon of the study site. By analyzing field observed data we validated through physical and numerical models. This study concludes that the current caused by the ebb and flood tide, as well as the steep and shallow seabed topography, together comprise the seabed scour mechanism near the Mailiao Port breakwater head.

Marine energy development has been actively promoted in recent years. Analyzing wave data in the surrounding sea areas is crucial for improving wave power generator efficiency. This study collected unidirectional wave spectrum data from the northeast coastal area of Taiwan (e.g., Pengjia Islet, Fugui Cape, and NTOU). It aimed to analyze the main wave types (i.e., wind waves, swell, or mixed sea) and their proportions during the northeast and non-northeast monsoon periods. The results indicate that swell waves dominate at the NTOU buoy, while wind waves dominate at the other two stations. In addition, the analysis of wave spectra ratios revealed a low percentage of unimodal spectra (only about 0.5%), and bimodal and unformed spectra presented as predominant. Furthermore, this study also introduced a brand new JW-J wave spectrum, which combines the JW and JONSWAP spectra to describe low-frequency (swell) and high-frequency (wind wave) systems, showing superior performance compared to the Torsethaugen spectrum. Finally, this research analyzed the JW-J spectrum parameters from the Pengjia Islet and Fugui Cape stations and applied these results to the NTOU bouy for effectively describing wind/swell wave variations.

Considering practical and wide-ranging data acquisition, the development of image recognition technology can compensate for the shortcomings of traditional contact sampling methods. This study utilizes UAVs equipped with cameras to capture optical images of marine environments. It performs cluster analysis on the captured images using tricolor pixel values such as red (R), green (G), and blue (B) as clustering indicators. The research results demonstrate that in image recognition, the best image recognition can clearly distinguish various clusters of water bodies according to the colors corresponding to different turbidity levels, showing better recognition results compared to the highest value image recognition. Furthermore, by further calibrating with turbidity data measured by water quality meters, the analysis reveals a highly positive correlation between turbidity and chromaticity. This indicates that optical images captured by UAVs and classified by algorithms can quickly obtain the distribution characteristics of classified chromaticity groups