Reproducibility

All data used in this work can be accessed with the following link: https://zenodo.org/record/7729001#.ZA8oAuzMKhY

If one would like to conduct a similar study, with data compiled themselves, they can access the raw data from CRMS stations from the following sites: https://www.lacoast.gov/CRMS/ and https://cims.coastal.louisiana.gov/monitoring-data/

The full tutorial will be able to be run via Binder, with the all the dependencies accounted for.

Sources

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Bishop, Christopher M. Pattern Recognition and Machine Learning. Information Science and Statistics. New York: Springer, 2006.

Etienne Chenevert. (2023). XAI Sedimentation Use Case (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729001

Molnar, C. (2019). Interpretable machine learning: A guide for making black box models explainable.

Pekel, Jean-François, Andrew Cottam, Noel Gorelick, and Alan S. Belward. “High-Resolution Mapping of Global Surface Water and Its Long-Term Changes.” Nature 540, no. 7633 (December 15, 2016): 418–22. https://doi.org/10.1038/nature20584.

Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, 2005. https://doi.org/10.7551/mitpress/3206.001.0001.

Silva, Sam J., Christoph A. Keller, and Joseph Hardin. “Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence.” Journal of Advances in Modeling Earth Systems 14, no. 4 (April 2022). https://doi.org/10.1029/2021MS002881.

Wager, R.T., & E.L. Haywood III, (2022). State of Louisiana Coastal Protection and Restoration Authority (CPRA) Coastal Information Management System (CIMS) Data Dictionary. Version 1.0, pp. 1-133.