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Performance assessment of stormwater GI practices using artificial neural networks
Science of the Total Environment ( IF 9.8 ) Pub Date : 2018-10-12 , DOI: 10.1016/j.scitotenv.2018.10.155
Shanshan Li , Hamidreza Kazemi , Thomas D. Rockaway

This study evaluates the performance of a suite of stormwater green infrastructure (GI) practices at the Belknap Campus, University of Louisville. In lack of instrumentation within individual GIs, and detailed drainage and sewer information, data mining procedures and artificial neural networks (ANN) were used. Two separate Back Propagation Neural Network Models (BPNNMs) were developed to estimate the reductions of flow volume and peak flow rates within the combined sewer system. The results from developed BPNNMs showed that following the construction of stormwater GIs at the Belknap campus, downstream wet-weather related flow decreased. The developed BPNNMs showed that the flow volume reduction and the peak flow attenuation rates had averages of approximately 33% and 61% per storm event, respectively. The flow reduction rates generally were lower for larger storms. Similarly, the peak flow rates decreased by increase of maximum intensity values per storm. However, further analysis indicated that even for large storm events, with long durations, the GIs had a positive impact on mitigation of combined sewer flows. Additionally, using rainfall data and downstream sewer flow in conjunction with artificial neural network modeling, was determined to be an effective technique for evaluating the combined hydrological performance of a suite of stormwater GIs.



中文翻译:

人工神经网络对雨水地理标志实践的绩效评估

这项研究评估了路易斯维尔大学Belknap校园的一套雨水绿色基础设施(GI)实践的效果。由于单个GI中缺少仪器,因此使用了详细的排水和下水道信息,数据挖掘程序和人工神经网络(ANN)。开发了两个单独的反向传播神经网络模型(BPNNM)来估计组合下水道系统内流量的减少和峰值流量的减少。发达的BPNNMs的结果表明,在Belknap校园建造了雨水GI之后,下游与潮湿天气有关的流量减少了。发达的BPNNMs表明,每个风暴事件的流量减少量和峰值流量衰减率分别平均约为33%和61%。对于较大的风暴,流量减少率通常较低。同样,每次暴风雨的最大强度值的增加会降低峰值流速。但是,进一步的分析表明,即使对于持续时间较长的大型暴风雨事件,地理标志也对缓解下水道综合流量产生了积极影响。此外,结合人工神经网络建模,结合使用降雨数据和下游污水流,已被确定为评估一套雨水地理标志的综合水文性能的有效技术。

更新日期:2018-10-19
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