💧 Water Resource Risk Mapping

Predicting and visualizing water resource risks through intelligent data analysis and real-time monitoring

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🌊 Overview

Water Resource Risk Mapping is an intelligent monitoring and prediction system that helps identify and visualize water-related risks including droughts, floods, and contamination events. By combining historical data analysis with real-time monitoring, this platform empowers decision-makers to take proactive measures in water resource management.

🎯 Key Features

• 📊 Real-time Risk Assessment - Dynamic calculation of water resource risks based on rainfall and water level data
• 🗺️ Geospatial Visualization - Interactive maps showing risk levels across different water sources
• 📈 Predictive Analytics - Machine learning models to forecast potential water crises
• 🔔 Alert System - Automated notifications for high-risk conditions
• 📱 RESTful API - Clean, well-documented endpoints for data integration

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🏗️ Architecture

┌─────────────────┐      ┌──────────────────┐      ┌─────────────────┐
│   Frontend      │ ───▶ │   FastAPI        │ ───▶ │   MySQL DB      │
│   Dashboard     │ ◀─── │   Backend        │ ◀─── │   (Water Data)  │
└─────────────────┘      └──────────────────┘      └─────────────────┘
                                 │
                                 ▼
                         ┌──────────────────┐
                         │  Risk Engine     │
                         │  (Algorithm)     │
                         └──────────────────┘

Tech Stack

Backend:

• 🚀 FastAPI - High-performance async API framework
• 🗄️ SQLAlchemy - Database ORM with Alembic migrations
• 🔐 Pydantic - Data validation and settings management
• 🐬 MySQL - Relational database for water resource data

Data Processing:

• 📊 Pandas - Data manipulation and analysis
• 🤖 Scikit-learn - Machine learning models (planned)
• 📈 NumPy - Numerical computations

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🚀 Quick Start

Prerequisites

Python 3.12+
MySQL 8.0+
pip or conda

Installation

1. Clone the repository

git clone https://github.com/yourusername/Water-Resource-Risk-Mapping.git
cd Water-Resource-Risk-Mapping

2. Set up virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

cd backend
pip install -r requirements.txt

4. Configure environment

cp .env.example .env
# Edit .env with your database credentials

5. Run database migrations

alembic upgrade head

6. Start the server

uvicorn app.main:app --reload

Visit http://localhost:8000/docs for interactive API documentation! 🎉

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📊 API Endpoints

Water Sources

POST /api/water-sources/

Create a new water source with risk assessment

Request Body:

{
  "name": "Lake Victoria",
  "latitude": -1.2921,
  "longitude": 36.8219,
  "rainfall": 75.5,
  "water_level": 12.3,
  "source_type": "lake"
}

Response:

{
  "id": 1,
  "name": "Lake Victoria",
  "risk_level": "moderate",
  "risk_score": 0.65,
  "created_at": "2024-01-15T10:30:00Z"
}

Risk Calculation Engine

The risk engine analyzes multiple factors:

• 🌧️ Rainfall patterns - Historical and current precipitation data
• 💧 Water levels - Current vs. historical averages
• 📍 Location - Geographic and topographic considerations
• 🕐 Seasonality - Time-based risk adjustments

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🗂️ Project Structure

Water-Resource-Risk-Mapping/
├── backend/
│   ├── app/
│   │   ├── api/              # API endpoints
│   │   ├── core/             # Config, database, security
│   │   ├── models/           # SQLAlchemy models
│   │   ├── schemas/          # Pydantic schemas
│   │   ├── services/         # Business logic (risk engine)
│   │   └── main.py           # FastAPI application
│   ├── migrations/           # Alembic migrations
│   ├── tests/                # Unit and integration tests
│   └── requirements.txt
├── frontend/                 # (Coming soon)
├── notebooks/                # Jupyter notebooks for analysis
└── README.md

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🛣️ Roadmap

• Core API with risk calculation
• Database schema and migrations
• Frontend dashboard with interactive maps
• Machine learning prediction models
• Real-time data ingestion from IoT sensors
• Historical trend analysis
• Mobile app for field workers
• Integration with weather APIs
• Alert notification system

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🌟 Why This Matters

Water scarcity affects 2.2 billion people worldwide. By leveraging data engineering, real-time monitoring, and predictive analytics, this project aims to:

• 🎯 Enable proactive water resource management
• 🌍 Support sustainable development goals
• 📉 Reduce the impact of water-related disasters
• 💡 Provide actionable insights to policymakers

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📧 Contact

Project Maintainer: Isaac Mwesigwa

Email: mwesigwaisaac40@gmail.com

GitHub: @IsaacJM03

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