Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany

Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models.

Automated summary

Data-driven models, including CNN and RF, were evaluated for predicting flood water depth. The RF models outperformed the CNN models within the training domain, while the CNN models had higher potential to generalize beyond the training domain. Transfer learning techniques improved the performance of CNN models outside training domains.