Lily-belle Sweet

Jul 10, 2023 | journals.ametsoc.org | Christoph Müller |Mohit Anand |Jakob Zscheischler |Lily-belle Sweet

Abstract Machine learning algorithms are able to capture complex, nonlinear interacting relationships and are increasingly used to predict yield variability at regional and national scales. Using explainable artificial intelligence (XAI) methods applied to such algorithms may enable better scientific understanding of drivers of yield variability. However, XAI methods may provide misleading results when applied to spatiotemporal correlated datasets.