Using Genetic Algorithms to Optimize Bathymetric Surveys for Hydrodynamic Model Input

The first part of this thesis deals with studying the effect of the specified bathymetric resolution and ideal bathymetric form parameters on the output from the wave and hydrodynamic modules of Delft-3D. This thesis then describes the use of an optimization to effectively reduce the required bathymetric sampling for input to a numerical forecast model, by using the model’s sensitivity to this input. A genetic algorithm is developed to gradually evolve the survey path for a ship, AUV, or other measurement platform to an optimum, with the resulting effect of the corresponding measured bathymetry on the model, used as a metric. Starting from an initial simulated set of possible random or heuristic sampling paths over the given bathymetry using certain constraints like limited length of track, the algorithm can be used to arrive at the path that would provide the best possible input to the model under those constraints. This suitability is tested by a comparison of the model results obtained by using these new simulated observations, with the results obtained using the best available bathymetry. Two test study areas were considered, and the algorithm was found to consistently converge to a sampling pattern that best captured the bathymetric variability critical to the model prediction