Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting
  Leveraging Heterogeneous Community Features   The objective of this study is to develop and test a novel structured
deep-learning modeling framework for urban flood nowcasting by integrating
physics-based and human-sensed features. We present a new computational
modeling framework including an attention-based spatial-temporal graph
convolution network (ASTGCN) model and different streams of data that are
collected in real-time, preprocessed, and fed into the model to consider
spatial and temporal information and dependencies that improve flood
nowcasting. The novelty of the computational modeling framework is threefold;
first, the model is capable of considering spatial and temporal dependencies in
inundation propagation thanks to the spatial and temporal graph convolutional
modules; second, it enables capturing the influence of heterogeneous temporal
data streams that can signal flooding status, including physics-based features
such as rainfall intensity and water elevation, and human-sensed data such as
flood reports and fluctuations of human activity. Third, its attention
mechanism enables the model to direct its focus on the most influential
features that vary dynamically. We show the application of the modeling
framework in the context of Harris County, Texas, as the case study and
Hurricane Harvey as the flood event. Results indicate that the model provides
superior performance for the nowcasting of urban flood inundation at the census
tract level, with a precision of 0.808 and a recall of 0.891, which shows the
model performs better compared with some other novel models. Moreover, ASTGCN
model performance improves when heterogeneous dynamic features are added into
the model that solely relies on physics-based features, which demonstrates the
promise of using heterogenous human-sensed data for flood nowcasting,

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The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
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The categories of this paper are: ao-sci