data unseen during model training & validation, used to assess the model's ability to generalise to new locations). The image below visualises the performance of one of these models (a fully-convolutional neural network) in one of the three test zones considered (i.e. Using machine learning to improve free topography data for flood modellingĪs part of the requirements for the Master of Disaster Risk & Resilience programme at the University of Canterbury, this research project explored the potential for machine learning models to make free Digital Surface Models (such as the widely-used SRTM) more applicable for flood modelling, by stripping away vertical biases relating to vegetation & built-up areas to get a "bare earth" Digital Terrain Model.
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