Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain.
Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain.
Sci Total Environ. 2018 Apr 15;621:753-761. doi: 10.1016/j.scitotenv.2017.11.291. Epub 2017 Dec 1.
Atmospheric particulate matter (PM) is one of the pollutants that may have a significant impact on human health. Data collected over seven years in a city of the north of Spain is analyzed using four different mathematical models: vector autoregressive moving-average (VARMA), autoregressive integrated moving-average (ARIMA), multilayer perceptron (MLP) neural networks and support vector machines (SVMs) with regression. Measured monthly average pollutants and PM (particles with a diameter less than 10μm) concentration are used as input to forecast the monthly averaged concentration of PM from one to seven months ahead. Simulations showed that the SVM model performs better than the other models when forecasting one month ahead and also for the following seven months.
大气颗粒物（PM）是可能对人类健康产生重大影响的污染物之一。本文使用四个不同的数学模型（向量自回归移动平均（VARMA）、自回归综合移动平均（ARIMA）、多层感知器（MLP）神经网络和支持向量机（SVM）回归），对西班牙北部一个城市的七年数据进行了分析。以每月平均污染物和 PM（直径小于 10μm 的颗粒）浓度作为输入，预测未来一到七个月的 PM 月平均浓度。模拟结果表明，SVM 模型在预测一个月和随后七个月的结果时表现优于其他模型。
