A stochastic approach for automatic generation of urban drainage systems

M. Möderl*, D. Butler** and W. Rauch*
*Unit of Environmental Engineering, University of Innsbruck, Technikerstr. 13, 6020 Innsbruck, Austria
**Centre for Water Systems, University of Exeter, North Park Road, Exeter EX4 4QF, UK
(E-mail: Michael.Moederl@uibk.ac.at; D.Butler@exeter.ac.uk; Wolfgang.Rauch@uibk.ac.at)

Abstract

Typically, performance evaluation of new developed methodologies is based on one or more case studies. The investigation of multiple real world case studies is tedious and time consuming. Moreover extrapolating conclusions from individual investigations to a general basis is arguable and sometimes even wrong. In this article a stochastic approach is presented to evaluate new developed methodologies on a broader basis. For the approach the Matlab-tool .Case Study Generator. is developed which generates a variety of different virtual urban drainage systems automatically using boundary conditions e.g. length of urban drainage system, slope of catchment surface, etc. as input. The layout of the sewer system is based on an adapted Galton-Watson branching process. The sub catchments are allocated considering a digital terrain model. Sewer system components are designed according to standard values. In total, 10,000 different virtual case studies of urban drainage system are generated and simulated. Consequently, simulation results are evaluated using a performance indicator for surface flooding. Comparison between results of the virtual case studies and two real world case studies indicates the promise of the method. Finally, it is shown that the presented approach is appropriate to evaluate new developed methodologies.

Keywords

Design, Case Study Generator, Performance Evaluation, Stochastic Analysis, Surface Flooding

Downloads

Download (Version 1.2)

Download (Version 1.1)

Examples

A standardized set of 250 case studies calibrated on real world case studies with alpine character can be downloaded below:

Input files: vcs_set_250_inp.zip

Maps of case studies: vcs_set_250_jpg.zip