There has been a rapid growth in the field of remote sensing and its various applications in the area of water management. Nowadays, there are several remote sensing techniques that can be used as a source to derive bathymetry data along coastal areas. The key techniques are: sonar (sound navigating and ranging), LiDAR (light detection and ranging) and high-resolution satellite images. The present paper describes a method which was developed and used to create a shallow water bathymetry data along the Dutch side of Sint Maarten Island by combining sonar measurements and satellite images in a nonlinear machine learning technique. The purpose of this work is to develop a bathymetry dataset that can be used to set up physically-based models for coastal flood modelling work. The nonlinear machine learning technique used in the work is a support vector machine (SVM) model. The sonar data were used as an output whereas image data were used as an input into the SVM model. The results were analysed for three depth ranges and the findings are promising. It remains to further verify the capacity of the new method on a dataset with higher resolution satellite imagery.

The present paper reviews several approaches that can be used in capturing urban features in coarse resolution two-dimensional (2D) models and it demonstrates the effectiveness of a new approach against the straightforward 2D modelling approach on a hypothetical and a real-life case study work. The case study work addresses the use of coarse grid resolutions in 2D non-inertia models. The 2D non-inertia model used solves continuity and momentum equations over the cells of the coarse model while taking the minimum elevation as a surface level. The volume stored in every cell is calculated as a volume-depth relationship. In order to replicate restriction in conveyances in x–y directions of fine resolution models due to building blocks, the friction values of the coarse-resolution model are adjusted to match the results of the high-resolution model. The work presented in this paper shows the possibility of applying a 2D non-inertia model more effectively in urban flood modelling applications whilst still making use of the high resolution of topographic data that can nowadays be easily acquired.