Citing k-diagram

If you use k-diagram in your research or work, please consider citing it. Proper citation helps acknowledge the effort involved in developing and maintaining this software and allows others to find and verify the tools used.

We recommend citing both the software package itself and any relevant publications describing the methods or applications.

Citing the Software

For citing the k-diagram software package directly, please include the author, title, version used, and the software repository URL.

Recommended Format:

Kouadio, K. L. (2024). k-diagram: Rethinking Forecasting Uncertainty via Polar-Based Visualization (Version 1.0.3). GitHub Repository. https://github.com/earthai-tech/k-diagram

(Please replace |release| with the specific version of k-diagram you used in your work. You can check the installed version using ``k-diagram –version`` or ``import kdiagram; print(kdiagram.__version__)``.)

Note

We plan to archive stable releases on platforms like Zenodo to provide a persistent Digital Object Identifier (DOI) for easier citation in the future. Please check the repository for updates on DOIs.

Related Publications

If your work relates to the concepts or applications demonstrated using k-diagram, please also consider citing the relevant papers:

Note

Please note that details for submitted or planned publications (like DOI, volume, pages) are placeholders. This information will be updated once the papers are formally published.

Land Subsidence Uncertainty Analysis (IJF Submission)

This paper delves into analyzing the structure and consistency of predictive uncertainty in land subsidence forecasting, applying methods related to k-diagram.

@unpublished{kouadio_subsidence_ijf_2025,
  author       = {Kouadio, Kouao Laurent and Liu, Rong and Loukou, Kouamé Gbèlè Hermann and Liu, Rong},
  title        = {{Understanding Uncertainty in Land Subsidence Forecasting}},
  journal      = {International Journal of Forecasting},
  note         = {Submitted},
  year         = {2025},
}

Urban Land Subsidence Forecasting (Nature Sustainability Submission)

This paper introduces the XTFT deep learning framework and applies visualization techniques (related to those in k-diagram) to forecast land subsidence in Nansha and Zhongshan, China.

@article{liu_subsidence_nat_sust_2025,
  author       = {Liu, Rong and Kouadio, Kouao Laurent and Jiang, Shiyu and Liu, Jianxin and Kouamelan, Serge Kouamelan and Liu, Wenxiang and Qing, Zhanhui and Zheng, Zhiwen},
  title        = {{Forecasting Urban Land Subsidence in the Era of Rapid Urbanization and Climate Stress}},
  journal      = {Nature Sustainability},
  year         = {2025},
  note         = {Submitted},
}

Software Paper (Planned JOSS Submission)

This upcoming paper will provide a focused description of the k-diagram software package itself, targeting the open-source software community.

@article{kouadio_kdiagram_joss_prep_2025,
  author       = {Kouadio, Kouao Laurent},
  title        = {{k-diagram: Rethinking Forecasting Uncertainty via Polar-Based Visualization}},
  note         = {In preparation for submission to Journal of Open Source Software},
  year         = {2025},
  howpublished = {\url{https://github.com/earthai-tech/k-diagram}},
  release      = |release|
}

Thank you for citing k-diagram!