首页|Researchers at Thomas Jefferson National Accelerator Facility Release New Study Findings on Machine Learning (A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia)
Researchers at Thomas Jefferson National Accelerator Facility Release New Study Findings on Machine Learning (A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Newp ort News, Virginia, by NewsRx correspondents, research stated, "Low-lying coasta l cities, exemplified by Norfolk, Virginia, face the challenge of street floodin g caused by rainfall and tides, which strain transportation and sewer systems an d can lead to personal and property damage." Our news correspondents obtained a quote from the research from Thomas Jefferson National Accelerator Facility: "While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational comp lexity renders them unsuitable for real-time applications. Using data from Norfo lk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep lea rning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)." According to the news reporters, the research concluded: "The comparison of deep learning to the random forest algorithm is motivated by the desire to utilize a machine learning architecture that allows for the future inclusion of common un certainty quantification techniques and the effective integration of relevant, m ulti-modal features."
Thomas Jefferson National Accelerator Fa cilityNewport NewsVirginiaUnited StatesNorth and Central AmericaCyborg sEmerging TechnologiesMachine Learning
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