Abstract: Flooding is one of the severest natural disasters to human lives and properties; to reduce flood emergency response time, an efficient vital method is to use flood routing results. Most of the previous flood routing calculations are based on the traditional 2D-hydro-physical model that requires massive computer capability and high CPU cost, failing to meet the demand by quick emergency response. This paper uses the results of a 2D-hydro-physical model as the driving data to train a CNN model, and then this CNN model is applied to flood routing. Results show that the new model brings about a huge reduction in CPU cost and promotes dramatically the efficiency of flood emergency response in reality. It can calculate flood routings of 1-6 hours long, a duration much longer than the traditional one of 1-2 hours, and its overall results meet engineering demands. Thus, the data-driven CNN method is a new approach and methodology for flood routing and useful for other science and engineering problems with specific inputs and outputs.

Key words: flood routing, shallow water equations, deep learning, prediction