Ai Enabled Ground Water And Water Level Prediction

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outdated lumped approaches that treat groundwater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 seawater
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.