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\begin{document}

\title{Climate extremes factor attribution: a small data challenge in ML realm}



\author[1]{PRASHANT Dave}%
\affil[1]{Indian Institute of Technology Bombay}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
The identification of factors driving the climate extremes have been
conventionally driven by the physical models evaluated using global
climate models and/or using statistical analysis.\\
However, owing to lack of spatial historical records, both of these
approaches pose a data insufficiency challenge. Moreover, identification
of primary drivers of climate extremes from a larger set of factors can
pose another challenge. Bagging machine learning models in conjugation
of synthetic sampling techniques can address both of these challenges.\\
Here, I demonstrate the applicability of three synthetically sampling
techniques along with Random Forest (RF) to identify the main drivers
and their spatial locations affecting the heatwave days over India for
the period of 1979-2013. The three sampling techniques used to generate
balanced data are undersampling, oversampling and synthetic minority
oversampling technique (SMOTE). It was RF model with SMOTE that could
identify the most important factors with greater precision and recall
(\$f1-\$score (0.85)) as compared to other sampling techniques.
Geopotential height\textbackslash{}@500 hPa along with sensible heating
fluxes were identified as important factors characterizing the Indian
heatwave days. The work has repercussion for any of the climate extremes
which lacks balanced data along with significantly lesser number of
observations than the factors.%
\end{abstract}%



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