Deep-learning-based prediction of damages related to surface water floods for impact-based warning

Surface water floods are responsible for a substantial amount of damage to buildings, yet they have received less attention than fluvial floods. Nowadays, both research and insurance companies are increasingly focusing on these phenomena to enhance knowledge and prevention efforts. This study builds upon pluvial-related damage data provided by the Swiss Mobiliar Insurance Company and the Building Insurance of Canton Zurich (GVZ) with the goal of developing a data-driven model for predicting potential damages in future precipitation events. This work is a continuation of a previous method applied to Swiss data, relying on thresholds based on the quantiles of precipitation intensity and event volume, which, however, resulted in an excessive number of false alarms. First, a logistic regression has been assessed using different characteristics of the precipitation event. Subsequently, a random forest was established, incorporating terrain attributes to better characterize local conditions. Finally, a deep learning model was developed to account for the spatio-temporal properties of the precipitation fields on a domain larger than the targeted 1 km cell. The deep learning model comprises a convolutional neural network (CNN) for 4D precipitation data and subsequent dense layers, incorporating static attributes. The model has been applied to predict the probability of damage occurrence, as well as the damage degree quantified by the number of claims relative to the number of insured buildings.