Abstract Urban flooding is increasingly exacerbated by the accumulation of floating debris in rivers, which obstructs water flow, degrades water quality, and poses significant risks to human safety and environmental sustainability. Effective monitoring of floating debris is therefore critical for early flood warning and long-term water resource management. This study presents a real-time monitoring framework that integrates deep learning-based object detection models You Look Only Once (YOLO) with video surveillance for the identification and quantification of floating debris in urban rivers. Field deployments were conducted in flood-prone sites in Shah Alam, Malaysia, to evaluate the system under real-world environmental conditions. Results show that YOLOv7 achieved higher accuracy and robustness across diverse debris classes and lighting conditions compared to YOLOv9, with precision, recall, and F1-scores demonstrating strong detection reliability. Beyond technical accuracy, the system provides timely and actionable information for flood risk assessment, river management, and environmental monitoring. By automating debris detection and quantification, this study contributes to Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities) and 13 (Climate Action), offering a scalable monitoring solution for flood-prone regions.
Shukri et al. (Fri,) studied this question.