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Groundwater Analytics

Exploring Environmental Data with ML

I've always been interested in how data can help solve environmental issues. This project was my first deep dive into the 'why' behind ML predictions. I learned how to engineer domain-specific features like 'Recharge Efficiency' and used Plotly to make the data tell a story. It taught me that a model is only as good as the features you create for it.

Project Highlights

[+]Learned the importance of feature engineering by creating custom environmental metrics.
[+]Used Scikit-Learn's Random Forest to model complex, non-linear water stress patterns.
[+]Built an interactive dashboard with Streamlit to make ML insights accessible.
[+]Explored Explainable AI (XAI) to understand which factors most influence water stress.

Tech Stack

#Python#Streamlit#Plotly#Scikit-Learn#Pandas#Joblib

Open Source Repository

Challenge

Environmental data is messy. I wanted to see if I could extract meaningful predictions from complex rainfall and usage patterns.

Solution

Combined robust data preprocessing with a Random Forest model and a visual 'What-If' simulator.

Workspace

root

app.py

data

Groundwater.csv

app.py

1import streamlit as st

2import plotly.express as px

4st.title("ðŸ’§ My Groundwater Analytics")

6# Scenario Analysis Tool

7st.header("ðŸ”® Simulation Tool")

8col1, col2 = st.columns(2)

10with col1:

11 rain = st.slider("Monsoon Rainfall", 0.0, 50.0, 15.0)

12 usage = st.slider("Irrigation Usage", 0.0, 50.0, 10.0)

14# Real-time Feature Engineering

15usage_intensity = usage / (avail + 1e-5)

16recharge_eff = rain / (total_rain + 1e-5)

18prediction = model.predict([[rain, usage, usage_intensity, recharge_eff]])

19st.metric("Predicted Stress Level", prediction)

Console

Initializing Groundwater Analytics Environment...

Connecting to Scikit-Learn backends...

Ready.

Building the Interface

I built this dashboard to visualize how changes in rainfall and usage affect water stress in real-time.

Key Insight

I learned that seeing the data move with a slider makes it much easier to understand the 'tipping points' of water stress.

Project Specs

Dataset

Indian States Groundwater

Architecture

Random Forest

Framework

Streamlit + Plotly

Concept

Scenario Simulation

Implementation Details

What-If Analysis
Dynamic Plotly Charts
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