Southern Methodist University Reports Findings in Machine Learning (Predicting R eal-time Roadway Pluvial Flood Risk: a Hybrid Machine Learning Approach Coupling a Graph-based Flood Spreading Model, Historical Vulnerabilities, and Waze Data)

Robotics & Machine Learning Daily News2024，Issue(Jul.15) ：33-34.

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Southern Methodist University Reports Findings in Machine Learning (Predicting R eal-time Roadway Pluvial Flood Risk: a Hybrid Machine Learning Approach Coupling a Graph-based Flood Spreading Model, Historical Vulnerabilities, and Waze Data)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting originatingfrom Dallas, Texas, by NewsRx correspondents, research stated, “Urban pluvial flash flooding(PFF), driven by extreme weather and urban expansion, introduces complex challenges that arise f romthe dynamic interaction of rainfall hazard, road vulnerability, and traffic exposure. These three criticalcomponents must be interconnected to provide a co mprehensive prediction of roadway PFF risk.”

Key words

Dallas/Texas/United States/North and Central America/Cyborgs/Emerging Technologies/Machine Learning/Southern Meth odist University

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出版年

2024

Robotics & Machine Learning Daily News

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