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About Shire

SHIRE (Susceptibility Hazard mappIng fRamEwork) is a tool to facilitate and streamline landslide susceptibility and hazard mapping using a Random Forest classifier. It provides support for repetitive steps in landslide susceptibility and hazard mapping such as input dataset generation including data pre-processing. It is a Python-based modular framework that can be complemented with individual modules necessary for answering individual mapping challenges due to the open-access nature of the code.

Shire was developed as part of the KISTE Project

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Getting Started

Please make sure to set up a virtual environment before installing the prerequisites. This is important as some packages might have dependencies among each other. Furthermore, the current version of the framework still uses numpy.interp2d which has recently been announced to be deprecated.

The framework was developed using Python 3.7

Then, clone the repository to your local system and you are ready to go.

Prerequisites

The framework has been developed and tested on a MacBook Pro using MacOS Monteray 12.7. Testing of the framework on other operating systems is planned.

There are known issues with using the tkinter python package which is used for the gui in the python editor Spyder. Therefore, it is recommended to launch the script from the command line using

   python shire.py

Installation

After setting up the virtual environment, clone the repository

   git clone https://git-ce.rwth-aachen.de/mbd/shire.git

and then install the prerequisites in requirement.txt

   pip install -r /path/to/requirements.txt

How to use SHIRE

SHIRE is available as plain python code (called Plain version) or as a version with user interface (called GUI version). Please refer to the documentation in Docs which summarises all necessary preperatory steps for both versions, explains settings and options and describes the output files of the framework.

Also refer to this example which illustrates how to use SHIRE. The necessary data and further information can be found in example/.

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
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

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

If you have questions or comments please feel free to reach out to:
Ann-Kathrin Edrich - edrich@mbd.rwth-aachen.de
Methods for model-based Development in Computational Engineering
RWTH Aachen University

Project Link: KISTE Project

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Acknowledgments

• We acknowledge the funding through the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection under grant no 67KI2043 (KISTE project).
• This work was performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)

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Cite as

Edrich A., Yildiz A., Kowalski J. (2024) Landslide Susceptibility and Hazard Mapping Framework SHIRE [Software]. https://git-ce.rwth-aachen.de/mbd/shire

@misc{shire,
  author = {Edrich, Ann-Kathrin and Yildiz, Anil and Kowalski, Julia},
  title  = {{Landslide Susceptibility and Hazard Mapping Framework SHIRE [Software]}},
  year   = {2024},
  note   = {Access under \url{https://doi.org/10.6084/m9.figshare.24339643}},
}

Corresponding publications:

Edrich A., Yildiz A., Roscher R., Bast A., Graf F., Kowalski J. (2024) A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning. Natural Hazards 120, 8953–8982. https://doi.org/10.1007/s11069-024-06563-8

@article{edrich2024modular,
  title   = {{A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning}},
  author  = {Edrich, Ann-Kathrin and Yildiz, Anil and Roscher, Ribana and Kowalski, Julia},
  journal = {Natural Hazards},
  year    = {2024},
  doi     = {https://doi.org/10.1007/s11069-024-06563-8}<br>
}