Citing k-diagram

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

We recommend citing the software paper (JOSS) and the software package itself. You may also cite any related application or methods papers that informed your work.

Software Paper (Planned JOSS Submission)

This paper focuses on the k-diagram software and is intended for the open-source and scientific 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 the Journal of
                  Open Source Software (JOSS)},
  year         = {2025},
  howpublished = {\url{https://github.com/earthai-tech/k-diagram}},
  release      = {|release|}
}

Citing the Software Package

If you wish to cite the software artifact directly, include the author, title, version used, and repository URL.

Recommended format:

Kouadio, K. L. (2025). k-diagram: Rethinking Forecasting Uncertainty via Polar-Based Visualization (Version 1.3.4). GitHub Repository. https://github.com/earthai-tech/k-diagram

Note

Replace |release| with the specific version you used. You can check the installed version with k-diagram --version or import kdiagram; print(kdiagram.__version__).)

Furthermore, we plan to archive stable releases on Zenodo to provide a persistent DOI. Please check the repository for updates once DOIs are issued.

Related Publications

If your work uses concepts, diagnostics, or applications demonstrated with k-diagram, consider citing the relevant papers below.

Note

Some entries are submitted or in preparation. DOI, volume, and page information will be added once available.

Land Subsidence Uncertainty Analysis (IJF submission)

This paper analyzes the structure and consistency of predictive uncertainty in land-subsidence forecasting using diagnostic diagrams related to k-diagram.

@unpublished{kouadio_subsidence_ijf_2025,
  author  = {Kouadio, Kouao Laurent and Liu, Rong and
             Loukou, Kouam{\\'e} Gb{\\`e}l{\\`e} Hermann},
  title   = {Analytics Framework for Interpreting Spatiotemporal
             Probabilistic Forecasts},
  journal = {International Journal of Forecasting},
  note    = {Submitted},
  year    = {2025}
}

Urban Land Subsidence Forecasting (Nature Communications)

This paper introduces a physics-informed deep learning framework and applies visualization techniques related to k-diagram to forecast urban land subsidence.

@article{liu_subsidence_nat_comm_2025,
  author  = {Kouadio, Kouao Laurent and Liu, Rong and Jiang, Shiyu
             and Liu, Jianxin and Kouamelan, Serge 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 Communications},
  year    = {2025},
  note    = {Submitted}
}

Thank you for citing k-diagram!