Effective flood risk mitigation in urban areas increasingly relies on the integration of real-time information and proactive decision-making. This study presents a lightweight and operationally practical framework for anticipatory urban flood early warning using existing CCTV infrastructure. Rather than performing long-horizon hydrological forecasting, the proposed system focuses on detecting incipient flood-risk signals from surveillance video and translating them into interpretable short-term warning indicators. The framework extracts visual water-occupancy signals from CCTV footage and applies Page–Hinkley-based change-point detection to identify statistically meaningful rising trends under noisy urban conditions. These trends are further characterized using short-term regression to estimate expected time-to-threshold (ETA) and probabilistic flood risk, which are integrated into a hysteresis-based dual-alert mechanism for reliable warning issuance. Using the best-performing preprocessing pipeline, the flood segmentation model achieved an mAP of 85.1%. Empirical validation across multiple urban flood scenarios further confirmed zero false positives under normal conditions (FPR = 0) and secured an average early-warning lead time of 32.9 ± 5 s during the pre-flood stage. The results indicate that the framework provides a robust and economically scalable alternative for real-time urban flood early warning, particularly in environments where dense sensor networks or computationally intensive hydrological models are impractical.
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号