Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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SES 3-3-1: Artificial Intelligence machine learning 2 Location: HSB0 Session Chair: Pete Melville-Shreeve Session Chair: Sebastian Ramsauer | |
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1:30pm - 1:45pm
Comparison between single and multi-objective strategies for urban drainage model optimization using genetic algorithms: A case study of Badalona Urban drainage network 1: Universitat Politècnica de Catalunya, Barcelona, Spain; 2: BGEO OPEN GIS S.L, Spain Urban drainage networks are critical to address the exacerbated flooding in the cities due to climate change and rapid urbanization. Badalona, a city in Spain has been facing recurrent pluvial flooding due to high-intensity and short-duration rainfall events driven by the Mediterranean climate. Although the city has its combined sewer networks modeled in SWMM, MOUSE, and Info works, it is supported through manual calibration methods. This approach is highly time-consuming for such a large network, and is subjective, depending on the modeler which can lead to suboptimal parameter selection. This research aims to address this limitation, by configuring a hybrid algorithm leveraging Non-Dominated Sorting Genetic Algorithm (NSGA-II) and SWMM to automatize the calibration process comparing single and multi-objective optimization strategies. Results demonstrate that the multi-objective optimization strategy offers a more holistic approach with a balance between various objective criteria effectively. With this methodology integrated into urban drainage management, an effective and comprehensive framework can be provided for sustainable water infrastructure that helps in achieving improved water quality, model performance, system resilience, and flood prevention. | |