Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network

Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas.