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首页> 外文会议>International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering >Pollution Transport Simulation and Machine-Learning Aided Source Detection in Metropolitan Areas
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Pollution Transport Simulation and Machine-Learning Aided Source Detection in Metropolitan Areas

机译：大城市地区的污染物迁移模拟和机器学习辅助源检测

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Air pollution is one of the world’s largest environmental health threats. This study aims to use simple remote signals to locate the source of a pollution release, which will significantly enhance our readiness to counter its threat. In urban areas, the flow structures advecting the pollution are extremely complex: boundary layer separation generates vortical structures that increase the spread of pollutants and break the plume into smaller patches by dispersion effect. Furthermore, the blocking and mixing in urban areas make it more obscured to locate the pollution sources. Flow structures were obtained by solving the two-dimensional NavierStokes equations using Computational Fluid Dynamics in a simplified scenario with imaginary urban architectures. We applied the canonical neural network to relate characteristics in the remote pollutant detector signals to the actual location of the pollutant release. The proposed algorithm identifies the source location and its uncertainty through a Monte Carlo analysis. When the number of training samples is small, as limited by the number of trial-releases we can perform in reality, data augmentation is done by introducing noisy measurement as new training samples. While the source localization is reasonable in the cross-flow direction, it is much harder to locate the streamwise location of the source due to signal similarity. The data augmentation technique we applied reduced the uncertainty of the source location by introducing under-fitting phenomena into the model. Furthermore, sensors away from the center line of the flow outperforms the ones near the center line, especially for detecting off-center sources. This indicates a pronounced effect of blocking and mixing right behind the building on the center line, which blurs sensor measurements from different source locations, and thus hinders the ability to trace back to the source.
机译：空气污染是世界上最大的环境健康威胁之一。这项研究旨在使用简单的远程信号来确定污染释放的源头，这将显着增强我们准备应对其威胁的能力。在城市地区，影响污染的流动结构极为复杂：边界层的分离产生了涡旋结构，这些涡旋结构增加了污染物的扩散，并通过扩散作用将烟羽分成较小的斑块。此外，城市地区的阻塞和混合使得对污染源的定位更加模糊。在具有虚拟城市结构的简化方案中，通过使用计算流体动力学求解二维NavierStokes方程来获得流动结构。我们应用规范神经网络将远程污染物检测器信号中的特征与污染物释放的实际位置相关联。所提出的算法通过蒙特卡洛分析来识别源位置及其不确定性。当训练样本的数量较少时（受我们实际可以执行的试用版数量的限制），通过引入噪声测量作为新的训练样本来完成数据扩充。尽管源定位在错流方向上是合理的，但是由于信号相似性，要定位源的流向位置要困难得多。我们采用的数据增强技术通过在模型中引入拟合不足现象来减少源位置的不确定性。此外，远离流中心线的传感器的性能要优于靠近中心线的传感器，特别是在检测偏心源时。这表明在中心线上建筑物的正后方有明显的阻塞和混合效果，这会使来自不同源位置的传感器测量值变得模糊，从而阻碍了追溯到源的能力。

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