Physics-Informed Machine Learning for Extreme Rainfall Nowcasting: A Rapid Review Toward Sustainable Early Warning Systems - Vasconcelos, Gyslla; Dornellas, Nayara; Aguiar, Laisa; Ferro, Mariza (Anais do Seminário Integrado de Software e Hardware (SEMISH), 2026)
Extreme rainfall nowcasting is essential for urban early warning systems, yet remains a challenging task. Data-driven machine learning models show promising performance but often suffer from limited generalization, physical inconsistency, and high computational cost. Physics-Informed Machine Learning (PIML) addresses these issues by integrating physical knowledge into learning algorithms. This paper presents a Rapid Review of PIML for extreme rainfall nowcasting, organizing integration strategies and addressing terminological fragmentation. The results reveal different levels of physical integration and limited focus on nowcasting. Most works neglect computational efficiency and resource-aware evaluation, highlighting challenges for developing reliable and sustainable nowcasting systems.