Using Genetic Algorithms to Optimize Bathymetric Sampling for Predictive Model Input

Abstract

Abstract This paper describes the use of an optimization method to effectively reduce the required bathymetric sampling for forcing 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, autonomous underwater vehicle (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 most recent and complete bathymetric data available. 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.