This study proposed a data-driven improved optimizationsimulation open的简体中文翻译

This study proposed a data-driven i

This study proposed a data-driven improved optimizationsimulation open-source tool based on the fuzzy logic theory and geneticalgorithm, aimed to optimize fuzzy control efficiency and to reducedownstream flooding volume at a real-world urban drainage systems(UDSs). The results show that traditional UDSs can be controlled byfuzzy logic control (FLC) to take advantage of their functionalities tohandle downstream urban flooding issues. The major advantage ofthis tool lies in the noticeable improvement in controller optimal performance (COP) and flooding volume reduction. This open-sourcesimulation-optimization tool is supposed to be implemented with different metaheuristic algorithms to promote applicability and helpdecision-makers and researchers to find effective solutions for mitigating urban flooding. The main contributions of this work are summarizedas four parts below:1) A real-time control simulation-optimization tool called SWMM_FLCwas developed for incorporating FLC into rainfall-runoff simulationsin UDSs. This tool was distributed at https://github.com/Jiadalee/SWMM_FLC for public access. More information about how to runand modify this tool for personal usage can be found in the ‘SoftwareAvailability’ section below.2) Long-term water depth and flow rate measurements were used totrain the fuzzy relationship between inputs and outputs in FIS(fuzzy inference system). Compared with manually building suchrelations, this data-driven method noticeably enhances the efficiency of FIS training process;3) GA (Genetic algorithm) was used to tune the CMFPs (ControllerMembership Function Parameters) before implementing FIS intoSWMM MATLAB wrapper. The error metric COP decreasing from0.22 in non-optimal FIS to 0.07 in optimal FIS scenario indicatesthat GA can improve FIS performance by reducing the deviations between predictions and expectations.4) The SWMM_FLC performance testing finds that SWMM_FLC can reduce total urban flooding volume by up to 4.55% under varying rainfall scenarios, which illustrates the possibility that urban floodingseverity can be alleviated by implementing FLC into UDSs.
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结果 (简体中文) 1: [复制]
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本研究提出了一种基于模糊逻辑理论和遗传<br>算法的数据驱动的优化仿真开源工具,旨在优化模糊控制效率并减少<br>现实世界城市排水系统<br>(UDS)的下游洪水量。结果表明,传统的UDS可以通过<br>模糊逻辑控制(FLC)进行控制,以利用其功能来<br>处理下游城市洪水问题。该<br>工具的主要优点在于,控制器的最佳性能(COP)和洪水体积的减少都有显着改善。这个开源<br>应当使用不同的元启发式算法来实现模拟优化工具,以提高适用性,并帮助决策者和研究人员找到缓解城市洪水的有效解决方案。这项工作的主要贡献归纳<br>为以下四个部分:<br>1)开发了一种称为SWMM_FLC的实时控制仿真优化工具,<br>用于将FLC纳入<br>UDSs的降雨径流模拟中。该工具已分发到https://github.com/Jiadalee/ <br>SWMM_FLC,以供公众访问。有关如何运行<br>和修改此工具以供个人使用的更多信息,请参见<br>下面的“软件可用性”部分。<br>2)使用长期水深和流量测量来<br>在FIS <br>(模糊推理系统)中训练输入和输出之间的模糊关系。与手动建立这种<br>关系相比,这种数据驱动的方法显着提高了FIS培训过程的效率;<br>3)<br>在将FIS实施到<br>SWMML MATLAB包装器之前,使用GA(遗传算法)对CMFP(控制器成员功能参数)进行了调整。误差度量COP从<br>非最佳FIS中的0.22 降低到最佳FIS情况中的0.07,表明<br>GA可以通过减少预测与期望之间的偏差来提高FIS性能。<br>4)SWMM_FLC性能测试发现,在变化的降雨情况下,SWMM_FLC最多可以减少4.55%的城市总洪灾量,这说明了<br>通过在UDS中实施FLC可以减轻城市洪灾的严重性。
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结果 (简体中文) 2:[复制]
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This study proposed a data-driven improved optimizationsimulation open-source tool based on the fuzzy logic theory and genetic<br>algorithm, aimed to optimize fuzzy control efficiency and to reduce<br>downstream flooding volume at a real-world urban drainage systems<br>(UDSs). The results show that traditional UDSs can be controlled by<br>fuzzy logic control (FLC) to take advantage of their functionalities to<br>handle downstream urban flooding issues. The major advantage of<br>this tool lies in the noticeable improvement in controller optimal performance (COP) and flooding volume reduction. This open-source<br>simulation-optimization tool is supposed to be implemented with different metaheuristic algorithms to promote applicability and helpdecision-makers and researchers to find effective solutions for mitigating urban flooding. The main contributions of this work are summarized<br>as four parts below:<br>1) A real-time control simulation-optimization tool called SWMM_FLC<br>was developed for incorporating FLC into rainfall-runoff simulations<br>in UDSs. This tool was distributed at https://github.com/Jiadalee/<br>SWMM_FLC for public access. More information about how to run<br>and modify this tool for personal usage can be found in the ‘Software<br>Availability’ section below.<br>2) Long-term water depth and flow rate measurements were used to<br>train the fuzzy relationship between inputs and outputs in FIS<br>(fuzzy inference system). Compared with manually building such<br>relations, this data-driven method noticeably enhances the efficiency of FIS training process;<br>3) GA (Genetic algorithm) was used to tune the CMFPs (Controller<br>Membership Function Parameters) before implementing FIS into<br>SWMM MATLAB wrapper. The error metric COP decreasing from<br>0.22 in non-optimal FIS to 0.07 in optimal FIS scenario indicates<br>that GA can improve FIS performance by reducing the deviations between predictions and expectations.<br>4) The SWMM_FLC performance testing finds that SWMM_FLC can reduce total urban flooding volume by up to 4.55% under varying rainfall scenarios, which illustrates the possibility that urban flooding<br>severity can be alleviated by implementing FLC into UDSs.
正在翻译中..
结果 (简体中文) 3:[复制]
复制成功!
本研究提出了一个基于模糊逻辑理论和遗传算法的改进型数据驱动优化仿真开源工具<br>算法,旨在优化模糊控制的效率并降低<br>实际城市排水系统的下游洪水量<br>(UDSs)。结果表明,传统的UDSs可以通过<br>模糊逻辑控制(FLC)利用其功能<br>处理下游城市洪水问题。主要优点<br>该工具在控制器优化性能(COP)和洪水体积减少方面有显著改善。这个开源<br>仿真优化工具应该使用不同的元启发式算法来实现,以提高其适用性,帮助决策者和研究人员找到缓解城市洪水的有效方案。总结了这项工作的主要贡献<br>如下四部分:<br>1) 一种实时控制仿真优化工具SWMM_-FLC<br>为将FLC纳入降雨径流模拟而开发<br>在UDSs中。此工具发布于https://github.com/jiadale/<br>用于公共访问的SWMM U FLC。有关如何运行的详细信息<br>修改此工具供个人使用,可在“软件”中找到<br>“可用性”部分。<br>2) 长期水深和流速测量用于<br>在FIS中训练输入输出之间的模糊关系<br>(模糊推理系统)。与人工建造相比<br>这种数据驱动方法显著提高了FIS训练过程的效率;<br>3) 采用遗传算法(GA)对CMFPs(控制器)进行整定<br>成员函数参数)在将FIS实施到<br>SWMM MATLAB包装器。误差度量COP从<br>非最佳财务状况下为0.22,最佳财务状况为0.07<br>该遗传算法可以通过减少预测值与期望值之间的偏差来提高财务信息系统的性能。<br>4) SWMM_FLC性能测试发现,在不同的降雨情景下,SWMM_FLC可以将城市总洪水量减少4.55%,这说明了城市洪水的可能性<br>通过在UDSs中实现FLC可以减轻严重性。<br>
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