Over the recent years, flood risks and losses have been increasing for coastal cities due to climate change, subsidence, population and economic growth. Hong Kong and Macau are two cities located in the Pearl River Delta that experience a significant flood risk due to storm surges. The increased losses and risks has sparked interest around the world for efficient and accurate flood forecasting. At the moment coastal flooding events are often simulated with difficult hydrodynamic models that reproduce the physical phenoms. Over the last decades there has been more interest in other methods to forecast storm surges, namely neural networks. Other than hydrodynamic models a neural network is capable is making predictions in seconds, while the model can take hours to finish simulation. fast and accurate storm surge forecasting is of importance for disaster and evacuations management strategies and will only become more important in the future. During this research the main goal is to develop a neural network capable of prediction maximum water levels due to storm surges in case of an approaching tropical cyclone. The neural network is trained with data that is obtained from hydrodynamic simulations. A synthetic storm database is used to provide the necessary data to conduct 1000 simulations of which the results are used in the neural network. The first focusses on developing a hydrodynamic model capable of accurately simulation tropical cyclone induced storm surges in Hong Kong and Macau. The model is calibrated in a way to reproduce the real world as close as possible. It accounts for the real life bathymetry, topography, astronomical tides and wind forcing. The storm surge model is validated extensively based on three historical tropical cyclones. The errors between the observed and simulated water level are below 20 cm, after calibrations of different physical parameters within the model. The second step of this research is to use the validated storm surge model to run synthetic storm simulations. Instead of using historical storms who only have been recorded for 40 years, a synthetic storm database is used containing 10000 years worth of data. From that storm data, 1000 synthetic tropical cyclones are selected that come close to Hong Kong and Macau. With the data obtained from the previous two steps, it is finally possible to training the neural network. In this network a total of seven input parameters (tropical cyclone track parameters) are used to estimate the maximum water level that will occur during the tropical cyclone. The input parameters considered are: latitude, longitude of TC eye, maximum wind speed, minimum eye pressure, radius of maximum winds, forward speed, forward propagation direction. During the development of the network, three different types of configurations are tested. Extensive validation and calibration shows that neural networks are capable of making maximum water level predictions for a large number of cases. However, variety in the quality of the prediction is observed. Improvements still can be made for more accurate predictions.