工作流程
灵敏度(控制系统)
排水
计算机科学
环境科学
工程类
数据库
生态学
生物
电子工程
作者
Barnaby Dobson,Tijana Jovanovic,Diego Alonso‐Álvarez,Taher Chegini
标识
DOI:10.1016/j.envsoft.2025.106358
摘要
Improvements in public geospatial datasets provide opportunities for deriving urban drainage networks and simulation models of these networks (UDMs). We present SWMManywhere, which leverages such datasets for generating synthetic UDMs and creating a Storm Water Management Model for any urban area globally. SWMManywhere's modular and parameterised approach enables customisation to explore hydraulicly feasible network configurations. Key novelties of our workflow are in network topology derivation that accounts for combined effects of impervious area and pipe slope. We assess SWMManywhere by comparing pluvial flooding, drainage network outflows, and design with known networks. The results demonstrate high quality simulations are achievable with a synthetic approach even for large networks. Our sensitivity analysis shows that manholes locations, outfalls, and underlying street network are the most sensitive parameters. We find widespread sensitivity across all parameters without clearly defined values that they should take, thus, recommending an uncertainty driven approach to synthetic drainage network modelling. • SWMManywhere can synthesise an urban drainage network model anywhere in the world. • SWMManywhere is a parameterised approach for customising the synthesised network. • We use sensitivity analysis to investigate uncertainty of network synthesis. • We find significant interaction between parameters, suggesting an ensemble approach. • Parameters controlling surface observable network elements are most sensitive.
科研通智能强力驱动
Strongly Powered by AbleSci AI