利用AO-SVR模型预测PM_(2.5)浓度

PM_(2.5) Concentration Prediction Using AO-SVR Model

针对支持向量回归(support vector regression, SVR)模型无法主动选取最优参数和核函数等问题,采用天鹰算法(aquila optimizer, AO)对其进行优化,构建天鹰算法优化支持向量回归模型(AO-SVR)。分别构建AO-SVR、SVR、灰狼算法优化支持向量回归(GWO-SVR)、鲸鱼算法优化支持向量回归(WOA-SVR)4种模型,使用2020-01-01~30拉萨、乌鲁木齐、长春、武汉、上海5市的大气污染物、气象因素以及天顶对流层延迟(zenith tropospheric delay, ZTD)的小时数据,分别预测5市2020-01-31的PM_(2.5)浓度变化。结果表明,AO-SVR模型的适用性更好,其中,上海的预测值最贴近实际观测值。

To solve the problems that support vector regression(SVR) models cannot actively select optimal parameters and kernel functions, we optimize the support vector regression model by the aquila optimizer(AO) and construct the aquila optimized support vector regression(AO-SVR) model. The four models, AO-SVR and SVR, gray wolf optimized support vector regression(GWO-SVR), and whale optimization algorithm support vector regression(WOA-SVR), are combined with atmospheric pollutants, meteorological factors and hourly zenith tropospheric delay(ZTD) data in five cities of Lhasa, Urumqi, Changchun, Wuhan, and Shanghai from 2020-01-01 to 30 to predict the changes of PM_(2.5) concentrations in the five cities on 2020-01-31, respectively. The results show that the AO-SVR model has better applicability;the predicted values in Shanghai are the closest to the actual observed values.