Hello! Given that a portion of the model evaluation is the model’s “explainability”, would it be possible to further clarify how this term is being used in this context? Clearly, understanding how predictors drive forecasts and changes in forecasts is important to operational decision makers. But how is this typically done? Is it a matter of being able to extract feature importance, or choosing an easy to explain model, or do decision makers care about something else? How specifically does explainability affect decision making in water supply forecasting?
In addition, who will be the technical experts who assess the explainability of our models? Are they the decision makers themselves or engineers who have built similar data-driven models?
Finally, why do previous data-driven models that try to predict water supply rely on principal components regression? Is this related to explainability, since PCR models are simpler to explain? Or related to ease of making quantile predictions, since PCR combines easily with quantile regression?
Any help to clarify these three questions would be much appreciated 