Conference Agenda

Our intended approach for the “Combat of Retrofitting Urban Drainage Networks with Nature-Based Solutions” leverages our AI-powered web application, Ziggurat (www.ziggurat.ai). Ziggurat automatically designs and optimizes sustainable urban drainage systems by integrating advanced algorithms from sewer system hydraulics, graph theory, mathematical optimization and artificial intelligence. It considers a comprehensive set of optimization variables, including network layout, pipe sizing, pumping, storage facilities, and the incorporation of various Low Impact Development (LID) measures. To handle the complexity of continuous rainfall series and the large-scale problem setting, we apply several preprocessing strategies. First, we extract a reduced set of critical rainfall events by analyzing the full time series to identify only the most relevant extreme events. This significantly decreases runtime while preserving the accuracy of the optimization objectives. Next, we employ subcatchment clustering to reduce the dimensionality associated with distinct drainage areas, streamlining the search space for solution candidates. Additionally, we replaces time-consuming rainfall-runoff simulations with data-driven emulators that offer rapid yet reliable performance estimates for different NBS scenarios. By combining these approaches, our team aims to efficiently identify an optimal configuration of nature-based solutions that meets the combat’s objectives of improving drainage performance, mitigating flooding, and enhancing multiple co-benefits.