Optimisation of a fuzzy logic-based local real-time control system for mitigation of sewer flooding using genetic algorithms
Optimisation of a fuzzy logic-based local real-time control system for mitigation of sewer flooding using genetic algorithms
复制标题
使用遗传算法优化基于模糊逻辑的局部实时控制系统以减轻下水道洪水
DOI：
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复制
发表时间：
2020
影响因子：
2.7
通讯作者：
S. Tait
中科院分区：
文献类型：
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作者：
S. Mounce;W. Shepherd;S. Ostojin;M. Abdel;A. Schellart;J. Shucksmith;S. Tait
城市洪水破坏财产、造成经济损失并严重威胁公众健康。一种创新的、基于模糊逻辑（FL）的本地自主实时控制（RTC）方法已被开发出来，用于利用城市排水网络现有的闲置容量来减轻这种危险。控制算法的默认参数使用基于水位的数据，是根据领域专家知识得出的，并通过以编程方式将控制算法链接到水力下水道网络模型来优化。本文描述了一种针对所开发的控制算法的 FL 隶属函数 (MF) 的新型遗传算法 (GA) 优化。为了给 GA 提供强大的训练和测试场景，在优化中使用了基于记录降雨量并结合多个事件编制的降雨时间序列。进行了小数和整数 GA 优化。事实证明，整数优化在处理看不见的事件时比十进制版本表现得更好，并且大大减少了计算运行时间。与专家为未见降雨事件选择的洪水量相比，优化的 FL MF 可使洪水量平均减少 25%。与传统的 RTC 洪水风险管理方法相比，这种使用 GA 优化的分布式自主控制具有显着的优势。
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.