Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method

Recently accrued attention has been given to machine learning approaches for flooding prediction. However, most of these studies focused mainly on time-series flooding prediction at specified sensors, rarely on spatio-temporal prediction of inundations. In this work, an integrated long short-term memory (LSTM) and reduced order model (ROM) framework has been developed. This integrated LSTM-ROM has the capability of representing the spatio-temporal distribution of floods since it takes advantage of both ROM and LSTM. To reduce the dimensional size of large spatial datasets in LSTM, the proper orthogonal decomposition (POD) and singular value decomposition (SVD) approaches are introduced. The LSTM training and prediction processes are carried out over the reduced space. This leads to an improvement of computational efficiency while maintaining the accuracy. The performance of the LSTM-ROM developed here has been evaluated using Okushiri tsunami as test cases. The results obtained from the LSTM-ROM have been compared with those from the full model (Fluidity). In predictive analytics, it is shown that the results from both the full model and LSTM-ROM are in a good agreement whilst the CPU cost using the LSTM-ROM is decreased by three orders of magnitude compared to full model simulations. Additionally, prescriptive analytics has been undertaken to estimate the uncertainty in flood induced conditions. Given the time series of the free surface height at a specified detector, the corresponding induced wave conditions along the coastline have then been provided using the LSTM network. Promising results indicate that the use of LSTM-ROM can provide the flood prediction in seconds, enabling us to provide real-time predictions and inform the public in a timely manner, reducing injuries and fatalities