Work

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

Year: 2024

Type: conference-paper

Abstract: 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.

DOI: 10.3390/engproc2024069032

Authors: Claudia Maußner, Martin Oberascher, Arnold Autengruber, Arno Kahl, Robert Sitzenfrei

Institution: Universität Innsbruck

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Funder: Klima- und Energiefonds

Award: G6956263036