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)

Robotics & Machine Learning Daily News2024，Issue(Feb.14) ：3-4.

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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)

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Abstract

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.”

Key words

Victoria/Australia/Australia and New Zealand/Cyborgs/Emerging Technologies/Gaussian Processes/Machine Learning

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出版年

2024

Robotics & Machine Learning Daily News

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