Incremental Learning Approaches for Flood Detection in Dynamic River Environments - Coletti, Otávio Ferracioli; Neves, Saulo Matos; Ranieri, Caetano Mazzoni; Ramachandran, Gowri Sankar; Jurdak, Raja; Ueyama, Jó (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
Identifying urban floods promptly can mitigate risks to public safety. Traditional models for this task rely on static datasets and are ill-suited to handle continuous environmental variation. This work presents an image-based flood monitoring system that integrates an incremental learning pipeline capable of adapting in real-time to distributional shifts. The model is periodically updated through cloud-based retraining informed by expert feedback. We evaluate different replay buffer strategies and demonstrate that the buffer replacement policy has a greater influence on performance than the buffer size. Our results show that incremental learning with selective replay significantly improves model robustness in dynamic flood monitoring scenarios.