首页|New Findings from Sungkyunkwan University Describe Advances in Machine Learning (Review of Machine Learning Methods for River Flood Routing)
New Findings from Sungkyunkwan University Describe Advances in Machine Learning (Review of Machine Learning Methods for River Flood Routing)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Mdpi
New study results on artificial intelligence have been published. According to news reporting out of Suwon, South Korea, by NewsRx editors, research stated, “River flood routing computes changes in the shape of a flood wave over time as it travels downstream along a river.” Funders for this research include Korea Environmental Industry And Technology Institute. Our news reporters obtained a quote from the research from Sungkyunkwan University: “Conventional flood routing models, especially hydrodynamic models, require a high quality and quantity of input data, such as measured hydrologic time series, geometric data, hydraulic structures, and hydrological parameters. Unlike physically based models, machine learning algorithms, which are data-driven models, do not require much knowledge about underlying physical processes and can identify complex nonlinearity between inputs and outputs.” According to the news editors, the research concluded: “Due to their higher performance, lower complexity, and low computation cost, researchers introduced novel machine learning methods as a single application or hybrid application to achieve more accurate and efficient flood routing. This paper reviews the recent application of machine learning methods in river flood routing.”
NETL
NSTL
Mdpi
