Explainable Methods for Water Demand Forecasting as a Key Aspect of Trustworthy Artificial Intelligence

The accurate prediction of daily drinking water demand for the next few days is the basis for many operational decisions and applications. In Europe, recently, the “Artificial Intelligence (AI) Act” was authorised, emphasising the trustworthiness and explainability of AI in the future. We therefore test and compare different AI methods regarding their performance, transparency and robustness. As the results show, opaque models are not per se superior to linear models, whereas linear models are especially ahead in terms of robustness and transparency. Bayesian linear models are particularly interesting as they additionally output credible intervals indicating upper and lower estimation bounds.