\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[english]{babel}



\usepackage{float}








\begin{document}

\title{Leveraging Contextual Cues from a Conceptual Model with Predictive
Skills of Machine Learning for Improved Predictability and
Interpretability in the Hydrological Processes}



\author[1]{Pravin Bhasme}%
\author[2]{Udit Bhatia}%
\affil[1]{Indian Institute of Technology Gandhinagar}%
\affil[2]{Indian Institute of Technology, Gandhinagar}%


\vspace{-1em}



  
  \date{June 17, 2024}


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





\selectlanguage{english}
\begin{abstract}
In recent years, Machine Learning (ML) techniques have gained the
attention of the hydrological community for their better predictive
skills. Specifically, ML models are widely applied for streamflow
predictions. However, limited interpretability in the ML models
indicates space for improvement. Leveraging domain knowledge from
conceptual models can aid in overcoming interpretability issues in ML
models. Here, we have developed the Physics Informed Machine Learning
(PIML) model at daily timestep, which accounts for memory in the
hydrological processes and provides an interpretable model structure. We
demonstrated three model cases, including lumped model and
semi-distributed model structures with and without reservoir. We
evaluate the first two model structures on three catchments in India,
and the applicability of the third model structure is shown on the two
United States catchments. Also, we compared the result of the PIML model
with the conceptual model (SIMHYD), which is used as the parent model to
derive contextual cues. Our results show that the PIML model outperforms
simple ML model in target variable (streamflow) prediction and SIMHYD
model in predicting target variable and intermediate variables (for
example, evapotranspiration, reservoir storage) while being mindful of
physical constraints. The water balance and runoff coefficient analysis
reveals that the PIML model provides physically consistent outputs. The
PIML modeling approach can make a conceptual model more modular such
that it can be applied irrespective of the region for which it is
developed. The successful application of PIML in different climatic as
well as geographical regions shows its generalizability.%
\end{abstract}%



\sloppy




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

