首页|University of Melbourne Reports Findings in Machine Learning (Assessment of surrogate models for flood inundation: The physicsguided LSG model vs. state-of-the-art machine learning models)
University of Melbourne Reports Findings in Machine Learning (Assessment of surrogate models for flood inundation: The physicsguided LSG model vs. state-of-the-art machine learning models)
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
New research on Machine Learning is the subject of a report. According to news reporting from Victoria, Australia, by NewsRx journalists, research stated, “Hydrodynamic models can accurately simulate flood inundation but are limited by their high computational demand that scales nonlinearly with model complexity, resolution, and domain size. Therefore, it is often not feasible to use high-resolution hydrodynamic models for real-time flood predictions or when a large number of predictions are needed for probabilistic flood design.”
VictoriaAustraliaAustralia and New ZealandCyborgsEmerging TechnologiesGaussian ProcessesMachine Learning
NETL
NSTL
