\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[greek,english]{babel}



\usepackage{float}








\begin{document}

\title{A new method for predicting the spatial and temporal distribution of
precipitation \selectlanguage{greek}δ \selectlanguage{english}18 O based on deep learning and spatial and temporal
clustering\selectlanguage{english}}



\author[1]{Yang Li}%
\author[1]{Siyuan Huo}%
\author[2]{Bin Ma}%
\author[3]{Jie Lv}%
\author[1]{Bingbing Pei}%
\author[1]{Qiankun Tan}%
\author[4]{Qing Guo}%
\author[1]{Deng Wang}%
\author[1]{Longbiao Yu}%
\affil[1]{Yangtze University}%
\affil[2]{China University of Geosciences}%
\affil[3]{Wuhan Municipal People's Government}%
\affil[4]{Changjiang Water Resources Commission}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Predicting precipitation \selectlanguage{greek}δ \selectlanguage{english}\textsuperscript{18}O accurately is crucial
for understanding water cycles, paleoclimates, and hydrological
applications. Yet, forecasting its spatio-temporal distribution remains
challenging due to complex climate interactions and extreme events. We
developed a method combining spatio-temporal clustering and deep
learning neural networks to improve multi-site, multi-year precipitation
\selectlanguage{greek}δ \selectlanguage{english}\textsuperscript{18}O predictions. Using a comprehensive dataset from
33 German sites (1978-2021), our model considers precipitation \selectlanguage{greek}δ
\selectlanguage{english}\textsuperscript{18}O and its controlling factors, including
precipitation and temperature distribution. We applied the K-means++
method for classification and divided data into training and prediction
sets. The CNN{[}1{]}(\#fn-0002) model extracted spatial features, while
the Bi-LSTM model focused on temporal features. Spatio-temporal
clustering using K-means++ improved forecast accuracy and reduced
errors. This study highlights the potential of deep learning and
clustering techniques for forecasting complex spatio-temporal data and
offers insights for future research on isotope distributions.%
\end{abstract}%



\sloppy


\textbf{Hosted file}

\verb`Hp submit.docx`  available at \url{https://authorea.com/users/564748/articles/661392-a-new-method-for-predicting-the-spatial-and-temporal-distribution-of-precipitation-%CE%B4-18-o-based-on-deep-learning-and-spatial-and-temporal-clustering}

\selectlanguage{english}
\FloatBarrier
\end{document}

