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\begin{document}

\title{Machine Learning and Remote sensing method to Determine the Relationship
Between Climate and Groundwater Recharge.}



\author[1]{Adya Aiswarya Dash}%
\author[1]{Abhijit Mukherjee}%
\affil[1]{Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, West  Bengal 721302, India}%


\vspace{-1em}



  
  \date{May 23, 2024}


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\begin{abstract}
Through machine learning and remote sensing, a high-end model with a
finer resolution for groundwater recharge has been developed for the
region of South-East Asia. The groundwater recharge coefficient can be
found by the application of Random Forest regression followed by the
implication of the water budget method to calculate the Groundwater
Recharge values. Climatic factors such as precipitation and actual
evapotranspiration to map Groundwater Recharge has been framed with a
sophisticated machine learning method to be considered as a scale
predicting model. A comprehensive visualization of the dataset has been
done; the accuracy of the model is noted through random forest
regression. Thus, the model can be used for various regions of the
dataset specifically for the area where there is a lack of reach for
data. It can be successfully used to form a sophisticated end-to-end ML
model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge,
Climate science.%
\end{abstract}%



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