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Community Optimization and Numerical Framework for Large-domain Understanding of Environmental Networks and Computational Exploration

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CONFLUENCE

Community Optimization and Numerical Framework for Large-domain Understanding of Environmental Networks and Computational Exploration

Overview

CONFLUENCE is an advanced hydrological modeling platform designed to facilitate comprehensive modeling and analysis across various scales and regions. It integrates multiple components for data management, model setup, optimization, uncertainty analysis, forecasting, visualization, and workflow management.

NOTE: CONFLUENCE is currently in development

Features

  • Data preprocessing and acquisition
  • Hydrological model setup and initialization
  • Model optimization and calibration
  • Result visualization and reporting
  • Workflow management for complex modeling tasks

Installation

  1. Clone the repository:
git clone https://github.com/your-repo/CONFLUENCE.git   
  1. Install the required dependencies:
cd CONFLUENCE 

pip install -r requirements.txt
  1. Clone and compile/install the desired/required binaries

Configuration

  • Copy config_template.yaml to config_active.yaml or any other name
  • Modify the copy according to your needs
  • All config files except config_template.yaml are ignored by git

Usage

  1. Set up your project configuration in config_active.yaml
  2. Run the main CONFLUENCE script:

python CONFLUENCE.py

or

  1. Run the scripts in /jupyter notebooks for a stepwise introduction to CONFLUENCE

For more detailed usage instructions, please refer to the documentation.

Contributing

We welcome contributions to CONFLUENCE! Please follow these steps to contribute:

  1. Fork the repository
  2. Create a new branch for your feature
  3. Commit your changes
  4. Push to your fork
  5. Submit a pull request

Please make sure to update tests as appropriate and adhere to the project's coding standards.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

Contact

Darri Eythorsson, University of Calgary

[email protected]

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Community Optimization and Numerical Framework for Large-domain Understanding of Environmental Networks and Computational Exploration

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