The "black-box" nature of representations, combined with the known susceptibility of networks to learn spurious correlations [

These processes find wide application in the natural sciences and machine learning, but their inference is known to be far from trivial.

This supplementary document follows the Datasheets for Datasets template of (8) to document the Global Flood Forecasting (GFF) dataset and its creation. Further resources are provided: in the accompanying publication https://arxiv.org/abs/2409.18591 in the GitHub repository https://github.com/Multihuntr/GFF

While successful, these methods are mainly designed for sampling from unconstrained domains.

This gave birth to learning-augmented algorithms, which use these predictions to go beyond the standard long-standing worst-case limitations. The design of such algorithms requires establishing good tradeoffs between consistency and robustness, i.e. having improved performance when the predictions are accurate, and not behaving poorly