Abstract
Abstract. In many countries, urban flooding due to local, intense rainfall is expected to become more frequent because of climate change and urbanization. Cities trying to adapt to this growing risk are challenged by a chronic lack of surface flooding data that are needed for flood risk assessment and planning. In this work, we propose a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale. The approach uses a deep convolutional neural network (DCNN) to detect floodwater in surveillance footage and a novel qualitative flood index (namely, the static observer flooding index – SOFI) as a proxy for water level fluctuations visible from a surveillance camera's viewpoint. To demonstrate the approach, we trained the DCNN on 1218 flooding images collected from the Internet and applied it to six surveillance videos representing different flooding and lighting conditions. The SOFI signal obtained from the videos had a 75 % correlation to the actual water level fluctuation on average. By retraining the DCNN with a few frames from a given video, the correlation is increased to 85 % on average. The results confirm that the approach is versatile, with the potential to be applied to a variety of surveillance camera models and flooding situations without the need for on-site camera calibration. Thanks to this flexibility, this approach could be a cheap and highly scalable alternative to conventional sensing methods.
Highlights
1.1 The need for urban pluvial flood monitoring dataUrban pluvial floods are floods caused by intense local rainfall in urban catchments, where drainage systems are usually not designed to cope with storm events of more than a 10-year return period
deep convolutional neural network (DCNN) are a subset of artificial neural networks (ANN), machine learning models with a structure that mimics the structure of neurons in the brain
The qualitative trend obtained in this manner was judged sufficient for the present study as this study only investigates the ability of static observer flooding index (SOFI) to predict water level trend, and not the actual water level
Summary
1.1 The need for urban pluvial flood monitoring data. Urban pluvial floods are floods caused by intense local rainfall in urban catchments, where drainage systems are usually not designed to cope with storm events of more than a 10-year return period. To cope with urban pluvial flood risk, urban drainage managers must understand long-term flooding trends, design appropriate flood mitigation solutions in the medium term, and provide flood alerts in the short term. Numerical flood modeling is a widely used tool for all of these tasks, but a certain amount of data is needed for modeling. Flood monitoring data allow for model calibration, which is essential for improving the accuracy of urban drainage models (Tscheikner-Gratl et al, 2016).
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