基于解析方程的人工神经网络预测PM2 .5
Analytical Equations based Prediction Approach for PM2.5 using Artificial Neural Network
摘要：
颗粒物污染是全球最致命的空气污染类型之一，因为它对全球环境和人类健康产生重大影响。颗粒物（PM2.5）是衡量空气质量指数（AQI）的重要颗粒污染物之一。空气质量监测站用来监测PM2.5的传统仪器昂贵、笨重、耗时且耗电。此外，由于有限的数据可用性和不可扩展性，这些站不能实时提供高的空间和时间分辨率。为了克服现有方法的缺点，本文提出了基于解析方程的预测方法，使用人工神经网络（ANN）的PM2.5。由于推导出的预测分析方程可以使用无线传感器节点（WSN）或低成本的处理工具计算，它表明了所提出的方法的实用性。此外，研究了PM2.5与其他污染物之间的相关性，以选择合适的预测因子。印度中央污染控制委员会（CPCB）在线站的大型认证数据集用于所提出的方法。使用8个预测因子得到的RMSE和决定系数（R2）分别为1.7973 μ g/m3和0.9986。而所提出的方法的结果表明，RMSE为7.5372微克/立方米和R2为0.9708，使用三个预测器。因此，结果表明，所提出的方法是监测PM2.5的有前途的方法之一，没有耗电的气体传感器和笨重的分析仪。
英文摘要：
Particulate matter pollution is one of the deadliest types of air pollution worldwide due to its significant impacts on the global environment and human health. Particulate Matter (PM2.5) is one of the important particulate pollutants to measure the Air Quality Index (AQI). The conventional instruments used by the air quality monitoring stations to monitor PM2.5 are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, these stations cannot provide high spatial and temporal resolution in real-time. To overcome the disadvantages of existing methodology this article presents analytical equations based prediction approach for PM2.5 using an Artificial Neural Network (ANN). Since the derived analytical equations for the prediction can be computed using a Wireless Sensor Node (WSN) or low-cost processing tool, it demonstrates the usefulness of the proposed approach. Moreover, the study related to correlation among the PM2.5 and other pollutants is performed to select the appropriate predictors. The large authenticate data set of Central Pollution Control Board (CPCB) online station, India is used for the proposed approach. The RMSE and coefficient of determination (R2) obtained for the proposed prediction approach using eight predictors are 1.7973 ug/m3 and 0.9986 respectively. While the proposed approach results show RMSE of 7.5372 ug/m3 and R2 of 0.9708 using three predictors. Therefore, the results demonstrate that the proposed approach is one of the promising approaches for monitoring PM2.5 without power-hungry gas sensors and bulkier analyzers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
