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\begin{document}

\title{Automated Input Variable Selection for Analog Methods Using Genetic
Algorithms}



\author[1]{Pascal Horton}%
\author[1]{Olivia Martius}%
\author[1]{Simon Lukas Grimm}%
\affil[1]{University of Bern}%


\vspace{-1em}



  
  \date{May 29, 2024}


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\begin{abstract}
Analog methods (AMs) have long been used for precipitation prediction
and climate studies. However, they rely on manual selections of
parameters, such as predictor variables and analogy criteria. Previous
work showed the potential of genetic algorithms (GAs) to optimize most
of the AM parameters. This research goes one step further and
investigates the potential of GAs for automating the selection of the
input variables and the analogy criteria (distance metric between two
data fields) in AMs. Our study focuses on the prediction of daily
precipitation in central Europe, specifically Switzerland, as a
representative case.~

Comparative analysis against established methods demonstrates the
superiority of GA-optimized AMs in terms of predictive accuracy. The
selected input variables exhibit strong associations with key
meteorological processes that influence the generation of precipitation.
Further, we identify a new analogy criterion inspired by the
Teweles-Wobus criterion, which consistently performs better than other
Euclidean distances and could be used in classic AMs. In contrast to
conventional stepwise selection approaches, GA-optimized AMs display a
preference for a flatter structure characterized by a single level of
analogy and an increased number of variables.

Overall, our study demonstrates the successful application of GAs in
automating input variable selection for AMs, with potential implications
for application in diverse locations and data exploration to predict
alternative predictands. In a broader context, GAs could be used to
perform input variable selection in other data-driven methods, opening
perspectives for a broad range of applications.%
\end{abstract}%



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