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Discharge Prediction with Python

The foundation of scientific computing in this project.

Welcome to the Hydrological Prediction Guide

This interactive documentation demonstrates step-by-step methodologies for making hydrological predictions using Python. Whether you're a student, researcher, or practitioner in hydrology, this guide will help you understand and implement various prediction models.

📚 What You'll Learn

This comprehensive guide covers:

  • Simple Linear Regression (SLR) - Understanding basic relationships between rainfall and discharge
  • Multiple Linear Regression (MLR) - Incorporating multiple variables for improved predictions
  • Artificial Neural Networks (ANN) - Using deep learning for complex hydrological patterns
  • LSTM (Time Series) - Modeling sequential dependencies for improved discharge forecasting

🎯 Key Features

  • Feature Engineering

    Master lag features and cross-correlation analysis

    Explore

  • Neural Networks

    Build and train ANNs for discharge prediction

    Get started

  • Performance Metrics

    Learn about R², NSE, and PBIAS for model evaluation

    Learn more

  • Resources

    Discover powerful time series forecasting libraries

    Browse

🚀 Quick Start

Prerequisites

  • Python 3.8 or higher
  • Basic understanding of hydrology
  • Familiarity with Python programming

Installation

# Create a virtual environment - run these into a command prompt/python shell/terminal in your IDE 
python -m venv hydro_env

# Activate it
# On Windows:
hydro_env\Scripts\activate
# On Mac/Linux:
source hydro_env/bin/activate

# Install required packages
pip install pandas numpy matplotlib scikit-learn statsmodels tensorflow

🎓 Learning Path

We recommend following this learning path:

  1. Start with Setup: Install necessary libraries and import your data
  2. Understand Fundamentals: Learn about performance metrics and feature engineering
  3. Build Models: Progress from simple to complex models
  4. Explore Resources: Discover advanced libraries for your projects

🤝 Contributing

Found an issue or want to contribute? Visit our GitHub repository to: - Report issues - Suggest improvements - Submit pull requests

📝 Citation

If you use this guide in your research, please cite:

@online{discharge_prediction_2024,
  author = {Zuhail Abdullah, Dr. Harsh Upadhyay (Scientist-B)},
  title = {Discharge Prediction with Python/ML: An Interactive Guide},
  year = {2025},
  url = {https://rudeprover.github.io/discharge-prediction-docs/}
}

Ready to Start?

Head over to the Installation Guide to begin your journey in hydrological prediction!

📧 Contact

For questions or feedback, please reach out through: - GitHub Issues: Create an issue - Email: gn0720@myamu.ac.in