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This document presents an in-depth study on the utilization of machine learning techniques to model urban flooding in New York City. It details the application of various statistical and neural network models with increasing complexities to understand urban flood occurrences. The study emphasizes the significance of data-driven techniques, particularly the Graph Wavenet (GWN), in accurately predicting flood events despite challenges posed by data scarcity. Through spectral clustering, the...
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urban flooding machine learning Graph Wavenet spatio-temporal forecasting New York City flood modeling neural networks predictive analytics data-driven techniques spectral clustering

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