AquaGuard AI is an environmental machine learning platform designed to assess groundwater quality for both drinking and irrigation purposes.
The platform leverages groundwater chemistry measurements and machine learning models to determine whether water is suitable for human consumption and agricultural use.
The system combines environmental analytics, domain-specific water quality standards, feature engineering, statistical transformations, and machine learning to provide accurate groundwater suitability assessments.
Groundwater is one of the most important freshwater resources worldwide.
Poor groundwater quality can negatively impact:
- Human health
- Agricultural productivity
- Soil quality
- Crop yield
- Sustainable water management
Traditional assessment methods often require extensive laboratory analysis and expert interpretation.
AquaGuard AI automates groundwater quality assessment using machine learning models trained on groundwater quality indicators.
Evaluates groundwater suitability for human consumption using:
- pH
- TDS
- EC
- Chloride
- Fluoride
- Nitrate
- Sulphate
- Other groundwater quality indicators
Assessment standards include:
- BIS Standards
- WHO Drinking Water Guidelines
Evaluates suitability for agricultural irrigation using:
- Electrical Conductivity (EC)
- Sodium Adsorption Ratio (SAR)
- Residual Sodium Carbonate (RSC)
- Total Hardness
- Sodium Concentration
- Additional irrigation quality indicators
Supports irrigation suitability classification for agricultural decision-making.
- Drinking water suitability assessment
- Irrigation water suitability assessment
- Automated quality classification
- Feature Engineering
- Data Transformation
- Random Forest Classification
- Prediction Pipeline
Implemented:
- Box-Cox Transformation
- Yeo-Johnson Transformation
Used to improve feature distributions and model performance.
- Water quality analysis
- Agricultural suitability assessment
- Drinking water safety evaluation
Groundwater Dataset
↓
Data Cleaning
↓
Feature Engineering
↓
Power Transformations
↓
Feature Selection
↓
Random Forest Model
↓
Groundwater Suitability Prediction
| Category | Technology |
|---|---|
| Programming Language | Python |
| Data Processing | Pandas |
| Numerical Computing | NumPy |
| Machine Learning | Scikit-Learn |
| Visualization | Matplotlib |
| Visualization | Seaborn |
| Modeling | Random Forest |
| Notebook Development | Jupyter |
AquaGuard-AI
├── drinking_water/
│ ├── src/
│ └── models/
├── irrigation_water/
│ ├── src/
│ └── models/
├── requirements.txt
├── README.md
├── LICENSE
└── .gitignore
Drinking Water Quality Assessment
Irrigation Water Quality Assessment
Environmental Data Analytics
Random Forest Classification
Feature Engineering
Statistical Transformations
Groundwater Suitability Prediction
Water Quality Intelligence
Identify groundwater suitable for human consumption.
Evaluate irrigation suitability for crops.
Support groundwater management initiatives.
Assist decision-making for sustainable water utilization.
- Streamlit Dashboard
- GIS Integration
- Groundwater Mapping
- Real-Time Monitoring
- Explainable AI (SHAP)
- API Deployment using FastAPI
- Cloud Deployment
AquaGuard AI combines environmental science and machine learning to solve real-world groundwater quality assessment challenges.
The project demonstrates practical experience in:
- Environmental AI
- Machine Learning
- Data Science
- Water Quality Analytics
- Feature Engineering
- Predictive Modeling
making it a strong portfolio project for AI Engineer, Data Scientist, Machine Learning Engineer, and Environmental Analytics roles.