December 17, 2024 This article has been reviewed according to Science X's editorial process and policies . Editors have highlightedthe following attributes while ensuring the content's credibility: fact-checked trusted source proofread by Johnny von Einem, University of Adelaide Hydrologic modelers are increasingly using explainable AI (XAI) to provide additional insight into complex hydrological problems, but a new University of Adelaide study suggests XAI's insights may not be as revolutionary as proponents suggest. XAI is a field of research and set of methods that helps people understand how AI algorithms work and trust the results they produce.
The traditional use of hydrological modeling would see a researcher use information on rainfall and evaporation to address issues such as water supply security and flooding. If such models are developed using AI approaches, XAI is tasked with explaining the rationale that the AI model used to develop the relationships it describes between factors such as rainfall and water supply. But according to the study published in the Journal of Hydrology X , led by Professor Holger Maier of the University of Adelaide's School of Architecture and Civil Engineering, using XAI in hydrological modeling has not yet created the advancements the technology might eventually lead to.
"Many XAI approaches are similar to more traditional methods of interrogating existing models, such as sensitivity or break-even analysis," says Professor Maier. "In fa.
