An entropy approach for the optimization of cross-section spacing for river modelling

An accurate definition of river geometry is essential to implement one-dimensional (1D) hydraulic models and, in particular, appropriate spacing between cross-sections is key for capturing rivers hydraulic behaviour. This work explores the potential of an entropy-based approach, as a complementary method to existing guidelines, to determine the optimal number of cross-sections to support 1D hydraulic modelling. To this end, given a redundant collection of existing cross-sections, a location subset is selected minimizing total correlation (as a measure of redundancy) and maximizing joint entropy (as a measure of information content). The problem is posed as a multi-objective optimization problem, and solved using a genetic algorithm (NSGA-II). The proposed method is applied to a river reach of the Po River (Italy) and compared to standard guidelines for 1D hydraulic modelling. Cross-sections selected through the proposed methodology were found to provide an accurate description of flood water profile, while optimizing computational efficiency.