The existing hydrodynamic models consider full physics approaches to calculate storm surge at coastal regions. However, due to the complexity of the equations that model these processes, the computational time and power required to run them can be large, compared to models that consider simplified equations. By contrast, simplified hydraulic models lack physical background, what leads to a minor accuracy in the surge estimations with respect to hydrodynamic models. In this project, a stochastic model has been developed with the objective to estimate surge at the coast of Mississippi (United States) at a reasonable accuracy and time, without solving equations that represent complex physical processes. The stochastic model needs to be trained by means of hurricane data, including surge levels. This information must be generated beforehand, by simulating a limited number of hurricanes with an hydrodynamic model. In this project, the hydrodynamic model Delft3D Flexible Mesh has been used for this purpose. The approach followed to build the stochastic model has been based on three main steps. The first step has been setting up and validating the hydrodynamic model in Delft3D FM. Hurricane Katrina (2005) has been simulated to calibrate the input parameters of the model, by comparing the maximum simulated water levels at 41 stations along the shoreline of Mississippi to the high water marks observed at the same locations during the event. Tide, surge and wave setup have been considered in the validation. The results of the validation show a line best fit slope from the origin of 0.912 and an R-squared of 0.996. At Gulfport, the absolute error of the surge estimation is 19 centimeters, equivalent to a relative error of 2.5%. The second step in the construction of the stochastic model has been the generation of a historical hurricane data base. The hurricane best tracks have been retrieved from the HURDAT2 data base. The variables considered have been the forward speed and the forward direction of the hurricane at landfall, the wind speed at landfall, the distance from landfall location to a reference point (Galveston Bay) and the maximum storm surge during the hurricane. In this case, only the hurricane forcing is considered as external action. The storm surge has been recorded at Gulfport Harbour (central coast of Mississippi). The values of the surge have been obtained by using the validated model to simulate the historical hurricanes making landfall in a rectangular domain of 600 kilometers, being Gulfport Harbour the center of the rectangle. Due to the scarce number of hurricanes making landfall in this region, the tracks of the hurricanes making landfall in the North of the Gulf of Mexico but outside the rectangular domain have been shifted inside the domain, in order to generate a sufficiently large data base to train the stochastic model. A data base with 140 hurricanes has been built, from which the 85% (119 hurricanes) have been used for the training of the stochastic model and the other 15% (21 hurricanes) have been used for the validation of the stochastic model. The third and last step has been the setup and validation of the stochastic model, by comparing the storm surge obtained from the stochastic model to the surge obtained from the hydrodynamic simulations. The stochastic model used to estimate storm surge has been a Bayesian Network that assumes normal copulas to represent the joint distribution between nodes of the network. The calculated slope of the best fit line for the mean surge values has been 0.861, with an R-squared of 0.885. Moreover, the average standard deviation of the estimations is 1.16 meters. These results indicate a reasonable estimation of the surge by means of the Bayesian Network. This estimation can be made in the order of seconds.

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.