Intensification of the number of rainfall floods caused by heavy rainfall has been observed, which has major economic consequences for both affected citizens and municipalities. By notifying citizens and preparedness before a servere flooding, they have the opportunity to take precautionary measures, and thereby reducing the consequences. It has been chosen to investigate the potential of a warning system against rain-flooding using a real-time model that calculates the response of the system in real time, without compromising the complexity of the model.

It has been chosen to construct the real-time model based on surrogate modeling, whereby a catalog of responses is produced. The catalog of responses is produced by simulating the MIKE FLOOD model with historical rain series as input. The catalog of responses is analyzed by using two methods; logistic functions and neural network and subsequently examine the methods in relation to describing the response in the system in real time. In addition, the potential for using artificially constructed precipitation for the production of the real-time model is examined.

It can be concluded that a real-time model can be produced which calculates whether future rainfall events result in flooding in real time, by both methods, where the neural network calculates the response with a greater certainty. In addition, it can be concluded that artificially constructed precipitation in the form of CDS-rain can be used to build a real-time model that calculates the response with satisfying certainty.