Urban ﬂooding damages properties, causes economic losses and can seriously threaten public health. An innovative, fuzzy logic (FL)-based, local autonomous real-time control (RTC) approach for mitigating this hazard utilising the existing spare capacity in urban drainage networks has been developed. The default parameters for the control algorithm, which uses water level-based data, were derived based on domain expert knowledge and optimised by linking the control algorithm programmatically to a hydrodynamic sewer network model. This paper describes a novel genetic algorithm (GA) optimisation of the FL membership functions (MFs) for the developed control algorithm. In order to provide the GA with strong training and test scenarios, the compiled rainfall time series based on recorded rainfall and incorporating multiple events were used in the optimisation. Both decimal and integer GA optimisations were carried out. The integer optimisation was shown to perform better on unseen events than the decimal version with considerably reduced computational run time. The optimised FL MFs result in an average 25% decrease in the ﬂood volume compared to those selected by experts for unseen rainfall events. This distributed, autonomous control using GA optimisation offers signiﬁcant beneﬁts over traditional RTC approaches for ﬂood risk management.
|Number of pages||15|
|Journal||Journal of Hydroinformatics|
|Early online date||24 Dec 2019|
|Publication status||Published - 1 Mar 2020|
Bibliographical noteFunding Information:
The CENTAUR project (www.shef.ac.uk/centaur) has received funding from the European Union’s Horizon 2020 research and the innovation programme under grant agreement no. 641931. The contents of this paper reflect the view of the authors – the Executive Agency for Small and Medium-sized Enterprises (EASME) of the European Commission is not responsible for any use that may be made of the information it contains.
© IWA Publishing 2020