A Bayesian deep learning approach for video-based estimation and uncertainty quantification of urban rainfall intensity

Abstract

Accurate, high-resolution spatiotemporal estimates of rainfall intensity (RI) are essential for effective prevention and control of urban flooding. Traditional methods are often costly and provide inadequate coverage. Meanwhile, the associated uncertainty of RI estimates is often overlooked, potentially leading to poor decisions in management of urban floods. Here we examine the potential of video imagery recorded by surveillance cameras in urban areas, for providing real-time estimates of RI. Specifically, we propose the use of Bayesian deep learning (DL) to estimate RI and its related uncertainty in a real-time manner. Our DL approach combines the strengths of convolutional neural network (CNN) and long short-term memory (LSTM) to construct a suitable model for this task, and uses variational inference to quantify the uncertainty of the CNN-LSTM model. The proposed approach is tested using video imagery captured under various light-intensity conditions, including daytime, nighttime, early morning, and early evening, and evaluated via experiments using both random samples and independent rainfall events. Further, we show that the temporal processing provided by the LSTM network is important to achieve good performance of RI estimation. The results indicate the strong potential for leveraging urban camera networks to obtain high-precision spatiotemporal RI estimates at low cost, which can be extremely valuable for implementing effective flood control measures and planning emergency responses in the urban areas.