The team proposes developing an AI-enabled water well predictor to forecast various aspects of water well performance. Such a predictor would analyze data using machine learning models to help ensure reliable water supply. It could help with efficient resource allocation, drought mitigation, groundwater management, well siting, water quality monitoring, and emergency response. The effectiveness depends on quality and quantity of data as well as model accuracy. The team is from LingarajAppa Engineering College Bidar and includes 6 members and 2 industry mentors.

1. Basic Details of the Team and Problem Statement
Ministry/Organization Name/Student Innovation: Ministry of Jal Shakti
PS Code: 1292
Problem Statement Title: AI-enabled water well predictor
Team Name: Bug Bashers
Team Leader Name: Kavya kore
Institute Code (AISHE): C-45309
Institute Name: LingarajAppa Engineering College Bidar
ThemeName: Cleen & Green Technology

2. Idea/Approach Details
Describe your idea/Solution/Prototype here:
A water well predictor is a system or tool that uses data
analysis and predictive modeling, often powered by artificial
intelligence and machine learning, to forecast various
aspects of a water well's performance. These predictors are
valuable for ensuring the availability and sustainability of
water resources.
The effectiveness of a water well predictor depends on the
quality and quantity of data available, the accuracy of the
predictive models, and the integration of real-time
monitoring if applicable. Such predictors can be vital tools
for ensuring a consistent and reliable supply of clean water,
especially in areas where wells are a primary source of
drinking water or agriculture.
Flow chart
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Describe your Technology stack here:

3. Idea/Approach Details
Describe your Use Cases here Describe your Dependencies / Show stopper here
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⮚ 1. *Resource Allocation*: Predicting water well performance
can help in efficient allocation of resources for maintenance,
repair, or drilling of new wells. It ensures that resources are
directed to wells that are likely to yield the best results.
⮚ 2.*Drought Mitigation*: During periods of drought, predicting
well performance can aid in prioritizing wells with higher
expected yields, ensuring a more reliable water supply for
communities and agriculture.
3.*Groundwater Management*: It can assist in managing
groundwater resources sustainably by identifying trends and
potential issues, helping authorities make informed decisions
about usage restrictions and conservation efforts.
4.*Well Siting*: When planning to drill new wells, a predictor
can aid in selecting optimal locations based on geological and
historical data, increasing the likelihood of finding productive
aquifers.
5. *Water Quality Monitoring*: In addition to quantity, such
predictors can also estimate water quality parameters based
on historical data, enabling better management of water
treatment and purification processes.
6.*Emergency Response*: During emergencies like natural
disasters or contaminations, knowing which wells are likely to
remain functional can be crucial for planning emergency
response efforts.
⮚ 1. *Data Sources*: Reliable and extensive data sources are
crucial. This includes historical well performance data,
geological data, hydrological data, weather data, and
information on local aquifers.
⮚ 2. *Data Quality*: Accurate and up-to-date data is essential.
Inaccurate or incomplete data can lead to unreliable
predictions. Data cleaning and validation processes are often
necessary.
⮚ 3. *Machine Learning Models*: Most water well predictors use
machine learning algorithms to analyze data and make
predictions. The choice of the specific model (e.g., decision
trees, random forests, neural networks) depends on the
nature of the data and the problem.
⮚ 4. *Feature Engineering*: Identifying and selecting relevant
features or variables from the data is critical. Domain
knowledge is often required to engineer meaningful features
for the predictive model.
⮚ 5. *Training Data*: A substantial dataset for training the
predictive model is necessary. The dataset should cover a
wide range of well characteristics and conditions to ensure
model generalizability.
⮚ 6. *Computational Resources*: Depending on the complexity
of the predictive model and the size of the dataset,.

4. Team Member Details
Team Leader Name: Kavya Kore
Branch : Btech Stream : CSE Year : IV
Team Member 1 Name: Ambika
Branch : Btech Stream : CSE Year : IV
Team Member 2 Name: Nikhita Patil
Branch : Btech Stream : CSE Year : IV
Team Member 3 Name: Kanchana
Branch : Btech
Team Member 4 Name: Nikhil Reddy
Stream : CSE Year : IV
Branch : Btech Stream : CSE Year : IV
Team Member 5 Name: Rahuf Khan
Branch : Btech Stream : CSE Year : IV
Team Mentor 1 Name: Sateesh Ambesange
Category : Industry Expertise : AI Research Scholar Domain Experience : 15+ years
Team Mentor 2 Name: Type Your Name Here
Category : Industry Expertise : AI Research Scholar Domain Experience : 8+ years

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