资源环境科技发展态势分析平台

Reliable flood damage assessment is important for decision-making in flood risk management. Flood damage assessment is often done with damage curves based only on water depth. These depth-damage curves are usually developed based on data from a specific location and specific flood conditions. Such depth-damage curves tend to be applied outside the scope of their validity. Validation studies show that in such cases depth-damage curve are not very reliable, probably due to excluded influencing variables. The expectation is that the inclusion of more variables in a damage function will improve its transferability. We compare multi-variable models based on Bayesian Networks and Random Forests developed on the basis of flood damage data sets from Germany and The Netherlands. The performance of the models is tested on a validation sub-set of both countries' data. The models are also updated with data from the other country and then tested again. The results show that the German models (BN/RF-FLEMOps) perform better in the Netherlands than the Dutch models (BN/RF-Meuse) perform in Germany. This is probably because the FLEMOps models are based on more heterogeneous data than the Meuse models. The FLEMOps models, therefore, are better able to capture damages processes from other events and in other locations. Model performance improves via updating the models with data from the location to which the model is transferred to. The results show that there is high potential to develop improved damage models, by training multi-variable models with heterogeneous data, for example from multiple flood events and locations.