AI Enabled Water Well Predictor

Abstract

Groundwater models in India are vital for
managing water resources, understanding water flow,
and assessing environmental impacts. These models
address tasks such as managing water balance,
simulating water flow, and establishing protection
zones. However, current models rely on outdated
lumped approaches that treat groundwater as a single
entity, neglecting its complex interactions with streams
and aquifers. This limitation affects their accuracy in
predicting water availability and safe withdrawals. Our
proposed system improves on these models by
incorporating advanced techniques like Random Forest
and Deep Convolutional Neural Networks (DCNN).
With proposed algorithms implemented clustering
algorithm to group similar points for aqua dataset and
K-means clustering will group all states with less water
in one cluster and states with high water in other
cluster. These methods better capture groundwater
system complexities, including recharge rates,
interactions with streams, and sea-water intrusion.
While traditional models may offer limited accuracy,
our approach provides significantly improved
predictions. This results in more reliable data for
groundwater management. In practice, our system
enhances decision-making for sustainable water use,
effectively addressing current groundwater
management challenges. Web application using Django
framework is implemented to get easy interface to the
user.