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 1-3-1: Modelling of Blue-Green Infrastructure / NBS / SUDS / LID 2 Location: HSB0 Session Chair: Max Maurer Session Chair: Bryn Elizabeth Reynolds | |
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2:30pm - 2:45pm
From fragmented data to long-term BGI management models 1: INSA Lyon, DEEP, UR7429, 69621 Villeurbanne, France; 2: RECOVER, INRAE, Aix Marseille Université, 3275 Route de Cézanne CS 40061, 13182 Aix-en-Provence Cedex 5, France; 3: Norwegian University of Science and Technology (NTNU), Department of Civil and Environmental Engineering, Water and wastewater engineering (VA) group, Trondheim, Norway.; 4: Norwegian University of Life Sciences, Institute of Civil and Environmental Engineering, Water and Environmental Engineering, Ås, Norway.; 5: School of Agriculture, Food and Ecosystem Sciences, The University of Melbourne, Burnley, VIC 3121, Australia Blue-Green Infrastructure (BGI) has become a vital component of urban stormwater management, offering hydrological benefits and ecosystem services. However, incomplete data on the long-term performance, operation and maintenance of BGI poses challenges for their sustainable asset management. This study proposes an agent-based model (ABM) to address these gaps by integrating hydrological and ecological aspects as well as stakeholders’ opinions. The ABM conceptual model incorporates agents representing utilities, maintenance practices, failure events, and BGI components, simulating their interactions over time. Data for the model is currently derived from a Fault Tree Analysis (FTA) and a survey of French BGI managers, with plans to expand to an international scope. FTA results help in identifying BGI failures and assessing their effects on BGI components, while survey data offers insights into maintenance practices and their frequency. ABM’s capacity to model emergent behaviours and simulate management strategies offers a promising path toward optimizing BGI performance. This study advances the understanding of BGI deterioration and supports the development of proactive, data-driven management frameworks to sustain the long-term benefits of these critical infrastructures. | |