Conference Agenda
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Daily Overview |
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SES 3-3-3: Combat Location: HSB2 Session Chair: Martin Oberascher Session Chair: Robert Sitzenfrei | |
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2:09pm - 2:21pm
Seeding and biasing genetic algorithms 1: Department of Water Management, Delft University of Technology, the Netherlands; 2: Universidad de Los Andes, Colombia; 3: Department of Multi Actors Systems, Delft University of Technology, the Netherlands We propose a multi-phase method to effectively optimise the location of nature-based solutions (NBS). During Phase 1, the largely branched drainage network will be partitioned into sections using Louvain algorithms for graph partitioning. The sensitivity of the system to NBS implementation in defined sectors is evaluated through automatic manipulation of the rainfall data for those sections, mimicking the hydrodynamic effects of the NBS on the overall system. This is done for the individual sectors and for all possible combinations of sectors, using the most impactful rainfall events on the objective function. The sensitivities of each sector will be ranked and used to steer the next optimisation in Phase 2. Here, a computationally efficient metamodel, which can mimic the flooding behaviour and uses the urban hydrology as an input will be deployed using transfer learning. The metamodel will be combined with a custom genetic algorithm: the optimisation-parameters are skewed towards the most impactful sectors and interventions, using a biased-random sampling based on the ranking of the partitioned sectors. The optimisation function used will include normalisation and weighting of the multi-objectives of the combat. Periodically, the highest scored solutions are evaluated with the SWMM-model to validate the proposed solutions. | |