论文标题
使用人工神经网络的基于分析方程的PM2.5的预测方法
Analytical Equations based Prediction Approach for PM2.5 using Artificial Neural Network
论文作者
论文摘要
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了重大影响。颗粒物（PM2.5）是测量空气质量指数（AQI）的重要颗粒污染物之一。空气质量监测站用于监视PM2.5的常规仪器是昂贵，笨重，耗时和渴望的。此外，由于数据可用性有限和非量表性，这些站点无法实时提供高空间和时间分辨率。为了克服现有方法论的缺点，本文使用人工神经网络（ANN）提出了基于分析方程的PM2.5的预测方法。由于可以使用无线传感器节点（WSN）或低成本处理工具来计算预测的派生分析方程，因此它证明了所提出的方法的有用性。此外，进行了与PM2.5和其他污染物之间相关性有关的研究以选择适当的预测因子。印度中央污染控制委员会（CPCB）在线站的大型身份验证数据集用于拟议的方法。使用八个预测因子获得的预测方法获得的确定的RMSE和系数分别为1.7973 ug/m3和0.9986。虽然提出的方法结果显示，使用三个预测因子，RMSE的RMSE为7.5372 ug/m3和R2为0.9708。因此，结果表明，所提出的方法是监测PM2.5的有希望的方法之一，而无需渴望渴望的气体传感器和较大的分析仪。
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.
