Flood simulations can give insight into the consequences of flood scenario's and can help to create hazard- and risk maps to support decision-making in flood risk management and in crisis management. 2D hydrodynamic simulations give accurate descriptions of the propagation of a flood and rely on advanced numerical methods to solve a set of physics-based mathematical equations. A drawback of these models is that they can be computationally expensive with run times in the order of hours or days depending on the time and spatial resolutions. In this study we explore the use of deep learning techniques in a surrogate model for 2D flood simulation. We propose and test a deep learning-based surrogate modelling framework that can be used to train a deep learning-based surrogate model. Once trained, the surrogate model can be used as a substitute for the hydrodynamic model with the advantage of being much more efficient in terms of run time and can be of great value in for example crisis situations. For training, a data set of expensive 2D hydrodynamic simulations was created using the SOBEK software program. Such simulations require a lot of input data, such as input parameter maps specifying the terrain over the computational grid and boundary conditions. To make training data-efficient, a sampling strategy was used for the input of the flood simulations. Three deep learning architectures were trained and tested. The first two architectures are feed-forward networks and the third architecture is of recurrent network type. These networks contain convolutional neural network (CNN) architectures with an encoder-decoder structure to make patch-level predictions of the flood characteristics in time. These patches contain a small section of the flood prone area and an encoder network is used to extracts coarse feature maps from this data that is then refined by a decoder network to create a prediction of the flood propagation. Using patches has the advantage of making a surrogate model able to create flood simulations over prone areas without restrictions on size or shape by tiling the output patches with flow predictions. Also it allows the surrogate to focus only on regions where the flood has reached and not on the regions where no water has arrived. It was found that with the recurrent architecture, the surrogate model was most capable of emulating the ground truth flood simulations in the test simulation. This trained network architecture was used in a case study where the surrogate was applied to create flood simulations in a small dike ring in the Netherlands. This shows that the surrogate modelling framework can be used to train a deep learning-based surrogate and, once trained, can be used to create flood simulations similar to hydrodynamic simulations. However, two main challenges were identified in using such data-driven deep learning-based surrogates. Firstly, keeping the predictions of the flood characteristics accurate enough to avoid large error propagation. Secondly, accurately generating large amounts of data from relatively little information present in the boundary condition and terrain.