Optimisation of a fuzzy logic-based local real-time control system for mitigation of sewer flooding using genetic algorithms

Abstract

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.

A data-driven improved fuzzy logic control optimization-simulation tool for reducing flooding volume at downstream urban drainage systems

“…In the output values of MFs, deviations between the expected responses and the simulated responses occur from now and then. Prior studies utilized evolutionary algorithm such as GA, PSO, or ANN, to adjust CMFPs in an efficient way (Mounce et al, 2019;. Hence, creating an accurate and optimal fuzzy inference system (FIS) significantly relies on applying an appropriate tuning method.…”

“…GA-optimized CMFPs was found to reduce flooding volume by 66% in a hypothetical urban drainage network (Mounce et al, 2019), which fairly motivated this study to select a genetic algorithm (GA) to optimize the parameter of membership functions.…”

“…Traditionally, the fuzzy control rules (FCRs or controllers) were designed on the basis of expert knowledge of the system. However, such an empiric set-up for control law might be less efficient and event less reliable when disturbance happens to the dynamical systems (Mounce et al, 2019). To seamlessly connect FCRs to dynamical systems, this study basically employed a data-driven improved GA programming approach to re-shape MFs for promoting the FLC performance.…”

“…Although previous studies have tried to implement FLC into stormwater management model (SWMM) (USEPA, 2015) for simulating flood control (Jafari et al, 2018;Wang and Altunkaynak, 2011), their methodology didn't directly connect SWMM and FLC as an efficient open-source optimization-simulation tool. Recent FLC studies contributed to implementing FLC into SWMM, but they seldom consider valuing FLC performance in terms of flooding severity alleviation on downstream UDSs under varying rainfall scenarios (Abdel-Aal et al, 2017;Mounce et al, 2019;Ostojin et al, 2017;Shepherd et al, 2016) To address these two problems, the first step of this study is to combine historical hydraulic measurements and GA to optimize the CMFPs in fuzzy logic simulation.…”

On the Use of a Real-Time Control Approach for Urban Stormwater Management

“…They use heuristic algorithms, based on externally imposed rules and previous knowledge of the drainage system (derived from experience and/or separate simulation procedures). The two most commonly used algorithms are Rule-Based Control (RBC) [51][52][53] and Fuzzy Logic Control (FLC) [54][55][56][57][58][59][60].…”