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\begin{document}

\title{A short-term regional precipitation prediction model based on
wind-improved spatiotemporal convolutional network}



\author[1]{Yunan Qiu}%
\author[1]{Zhenyu Lu}%
\author[1]{Haibo Tang}%
\affil[1]{Nanjing University of Information Science and Technology}%


\vspace{-1em}



  
  \date{June 10, 2024}


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\begin{abstract}
Accurate precipitation forecasting can better reflect climate change
trends, provide timely and effective environmental information for
management decisions, and prevent flood and drought disasters. In this
paper, we propose a short-term regional precipitation prediction model
based on wind-improved spatiotemporal convolutional network. Among them,
the improved Graph Convolution Network (GCN) integrates the effects of
wind direction and geographic location at past moments to capture the
spatial dependence, whilst the Gated Recurrent Unit (GRU) captures the
temporal dependence by learning the dynamic changes of data. The
spatio-temporal memory flow module and attention module are added to
capture spatial deformation and temporal variation more accurately,
thereby better matching the physical properties of precipitation.
Experimental results on real data sets show that the proposed model can
handle complex spatial dependence and temporal dynamic changes, better
learn the temporal and spatial characteristics of precipitation data,
and achieve better prediction results.%
\end{abstract}%



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