• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
• /
• 2003.08a
• /
• pp.353-358
• /
• 2003

As fur quantitative evaluation of the amount of sediments released into the ambient waters by various works fur coastal development, the instrument and method of the measurement of suspended sediment concentration (SSC) are critical for estimating the efficiency of a silt screen to reduce the spreading of sediment plumes generated by coastal works. (omitted)

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
• /
• 2003.08a
• /
• pp.344-352
• /
• 2003

Sediment plumes arising from various coastal works can cause detrimental effects on the coastal ecosystem in various manners. Although the most active countermeasure against the plumes is to restrict the works to specified time periods known as environmental windows (Reine et al., 1998), silt screens have been widely used for reducing the spreading of suspended sediments (SS) generated by coastal works. (omitted)

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
• /
• 2003.08a
• /
• pp.179-184
• /
• 2003

It is inevitable for dredging to increase the suspended sediment concentration (SSC) of the ambient waters in some degree, which has the potential to affect the coastal ecosystem in various manners. Thus, quantitative under- standing of dredging-induced sediment loss is essential fur the reliable environmental impacts assessment. (omitted)

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.09b
• /
• pp.1353-1354
• /
• 2005

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.09b
• /
• pp.866-867
• /
• 2005

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
• /
• 2001.08a
• /
• pp.69-78
• /
• 2001

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
• /
• 2001.08a
• /
• pp.152-157
• /
• 2001

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.09b
• /
• pp.1355-1355
• /
• 2005

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
• /
• 2002.08a
• /
• pp.191-197
• /
• 2002

The prediction of the dynamic behaviors of buoyant water discharges into a large volume of water bodies, the flows of water accompanying the density differences due to temperature differences and sometimes also to salinity differences, have attracted great concern over several decades. Heated water surface discharges from power plants and freshwater discharges in estuaries are typical examples of the buoyant flows. (omitted)

• Journal of Ocean Engineering and Technology
• /
• v.36 no.3
• /
• pp.194-210
• /
• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.