A Transformer variant for multi-step forecasting of water level and hydrometeorological sensitivity analysis based on explainable artificial intelligence technology

arXiv:2405.13646v1 Announce Type: new
Abstract: Understanding the combined influences of meteorological and hydrological factors on water level and flood events is essential, particularly in today's changing climate environments. Transformer, as one kind of the cutting-edge deep learning methods, offers an effective approach to model intricate nonlinear processes, enables the extraction of key features and water level predictions. EXplainable Artificial Intelligence (XAI) methods play important roles in enhancing the understandings of how different factors impact water level. In this study, we …

abstract analysis artificial artificial intelligence artificial intelligence technology arxiv climate cs.lg deep learning edge environments events explainable artificial intelligence flood forecasting intelligence kind sensitivity technology transformer type understanding water