Robotics & Machine Learning Daily News2024,Issue(Jun.28) :122-122.

Data on Machine Learning Published by Researchers at Virginia Polytechnic Instit ute and State University (Virginia Tech) (Quantifying cascading uncertainty in c ompound flood modeling with linked process-based and machine learning models)

弗吉尼亚理工学院和州立大学(弗吉尼亚理工大学)的研究人员发布的机器学习数据(用基于过程的链接模型和机器学习模型量化综合洪水模型中的级联不确定性)

Robotics & Machine Learning Daily News2024,Issue(Jun.28) :122-122.

Data on Machine Learning Published by Researchers at Virginia Polytechnic Instit ute and State University (Virginia Tech) (Quantifying cascading uncertainty in c ompound flood modeling with linked process-based and machine learning models)

弗吉尼亚理工学院和州立大学(弗吉尼亚理工大学)的研究人员发布的机器学习数据(用基于过程的链接模型和机器学习模型量化综合洪水模型中的级联不确定性)

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摘要

由一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-关于人工智能的最新研究结果已经发表。根据NewsRx记者来自弗吉尼亚州布莱克斯堡的新闻,研究表明,"复合洪水(CF)模型G能够模拟非线性水位动态,其中并发的连续洪水驱动因素协同作用,产生比独立驱动因素更大的影响。"这项研究的财政支持者包括地球科学局。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news originating from Blacksburg, V irginia, by NewsRx correspondents, research stated, “Compound flood (CF) modelin g enables the simulation of nonlinear water level dynamics in which concurrent o r successive flood drivers synergize, producing larger impacts than those from i ndividual drivers.” Financial supporters for this research include Directorate For Geosciences.

Key words

Virginia Polytechnic Institute and State University (Virginia Tech)/Blacksburg/Virginia/United States/North and Cent ral America/Cyborgs/Emerging Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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