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 (optionally) the software package/repository version you used. You may also cite any related methods or application papers that informed your work.

Software Paper (JOSS)

This paper describes the k-diagram software and its core ideas.

@article{Kouadio2025,
  doi       = {10.21105/joss.08661},
  url       = {https://doi.org/10.21105/joss.08661},
  year      = {2025},
  publisher = {The Open Journal},
  volume    = {10},
  number    = {116},
  pages     = {8661},
  author    = {Kouadio, Kouao Laurent},
  title     = {k-diagram: Rethinking Forecasting Uncertainty via Polar-based Visualization},
  journal   = {Journal of Open Source Software}
}

Citing the Software Package

If you wish to cite the software artifact directly (e.g., a specific version in a reproducible workflow), 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 1.4.0). 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__).

Related Publications

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

Diagnostic Framework Paper (Environmental Modelling & Software — Published)

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

CAS: Cluster-Aware Scoring for Probabilistic Forecasts (IJF submission)

This paper introduces CAS (Cluster-Aware Severity / Cluster-Aware Scoring), a metric that penalizes bursty, clustered forecast errors and complements traditional scoring rules.

Examples and reproducible scripts are provided in the repository: https://github.com/earthai-tech/k-diagram/tree/main/examples/cas

@unpublished{kouadio_cas_ijf_2025,
  author  = {Kouadio, Kouao Laurent and Liu, Rong},
  title   = {CAS: Cluster-Aware Scoring for Probabilistic Forecasts},
  journal = {International Journal of Forecasting},
  note    = {Submitted},
  year    = {2025}
}

Diagnostic Framework Paper (Environmental Modelling & Software — Published)

This is the primary methods paper that introduces the diagnostic framework underlying k-diagram, including the spatial-polar visualization paradigm, the CAS score, and their application to spatiotemporal probabilistic forecasts of land subsidence.

Full citation:

Kouadio, K. L., Liu, R., Loukou, K. G. H., Liu, W., Qing, Z., & Liu, Z. (2026). A diagnostic framework for interpreting spatiotemporal probabilistic forecasts. Environmental Modelling & Software, 107052. ISSN 1364-8152. https://doi.org/10.1016/j.envsoft.2026.107052

@article{kouadio_envsoft_2026,
  author  = {Kouadio, Kouao Laurent and Liu, Rong and
             Loukou, Kouam{\'e} Gb{\`e}l{\`e} Hermann and
             Liu, Wenxiang and Qing, Zhanhui and Liu, Zhuo},
  title   = {A diagnostic framework for interpreting spatiotemporal
             probabilistic forecasts},
  journal = {Environmental Modelling \& Software},
  year    = {2026},
  pages   = {107052},
  issn    = {1364-8152},
  doi     = {10.1016/j.envsoft.2026.107052},
  url     = {https://www.sciencedirect.com/science/article/pii/S1364815226001994}
}

Urban Land Subsidence Regimes (IEEE TGRS — Under Review)

This paper introduces a physics-informed deep learning framework for diagnosing divergent urban land-subsidence regimes and applies the spatial-polar visualization techniques implemented in k-diagram.

Note

This manuscript is currently under review. Citation details will be updated once the DOI is assigned.

@unpublished{kouadio_tgrs_2026,
  author = {Kouadio, Kouao Laurent and Liu, Rong and Liu, Zhuo and
            Jiang, Shiyu and Kouamelan, Serge Kouamelan and
            Liu, Wenxiang and Qing, Zhanhui and Zheng, Zhiwen},
  title  = {Physics-Informed Deep Learning Reveals Divergent Urban
            Land Subsidence Regimes},
  note   = {Under review in {IEEE} Transactions on Geoscience and
            Remote Sensing},
  year   = {2026}
}
}

Thank you for citing k-diagram!