An interactive MkDocs-based documentation site for learning hydrological discharge prediction using Python, featuring Simple Linear Regression, Multiple Linear Regression, and Artificial Neural Networks.
🌐 Live Site: https://rudeprover.github.io/discharge-prediction-docs/
- Interactive Code Examples - Copy and run code snippets with one click
- Step-by-Step Tutorials - From data import to advanced neural networks
- Mathematical Foundations - Understand the theory behind each model
- Performance Metrics - Learn R², NSE, and PBIAS for model evaluation
- Feature Engineering - Master lag features and cross-correlation
- Resource Library - Discover powerful time series forecasting libraries
- Dark/Light Theme - Toggle between themes for comfortable reading
- Mobile Responsive - Access tutorials on any device
Visit the live documentation at: https://rudeprover.github.io/discharge-prediction-docs/
discharge-prediction-docs/
├── mkdocs.yml # MkDocs configuration
├── docs/ # Documentation content
│ ├── index.md # Home page
│ ├── setup/ # Installation & data import guides
│ │ ├── installation.md
│ │ └── data-import.md
│ ├── fundamentals/ # Core concepts
│ │ ├── performance-metrics.md
│ │ └── feature-engineering.md
│ ├── models/ # Model implementations
│ │ ├── simple-linear-regression.md
│ │ ├── multiple-linear-regression.md
│ │ └── artificial-neural-network.md
│ ├── resources/ # Additional resources
│ │ └── libraries.md
│ └── assets/ # CSS, JS, and images
│ ├── css/
│ ├── js/
│ └── images/
├── requirements.txt # Python dependencies
└── .github/
└── workflows/
└── ci.yml # GitHub Actions for auto-deployment
- MkDocs - Static site generator
- Material for MkDocs - Beautiful theme with many features
- Python - For hydrological modeling examples
- GitHub Pages - Free hosting
- GitHub Actions - Automated deployment
- Installing required libraries (pandas, numpy, scikit-learn, TensorFlow)
- Loading and preparing discharge data
- Data visualization techniques
- Performance metrics (R², NSE, PBIAS)
- Feature engineering with lag variables
- Cross-correlation analysis
- Simple Linear Regression: Basic rainfall-discharge relationships
- Multiple Linear Regression: Incorporating multiple variables
- Artificial Neural Networks: Deep learning for complex patterns
- Time series forecasting libraries (Prophet, Darts, statsmodels)
- Hydrology-specific tools
- Learning resources and tutorials
By following this documentation, you will:
- ✅ Understand hydrological prediction fundamentals
- ✅ Implement regression models from scratch
- ✅ Build and train neural networks for time series
- ✅ Evaluate model performance using appropriate metrics
- ✅ Apply feature engineering techniques
- ✅ Discover advanced forecasting libraries
Contributions are welcome! Please feel free to submit a Pull Request. For major changes:
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Original Jupyter notebook content adapted for interactive documentation
- Inspired by hydrological modeling best practices
- Built with the amazing MkDocs Material theme
Project Link: https://github.com/rudeprover/discharge-prediction-docs
Made with ❤️ for the Hydrology Community