This study was conducted to evaluate the performance of the support vector regression (SVR) model with and without applying wavelet transformation for predicting the PM10, PM2.5, SO₂, NO₂, CO, and O₃ in Isfahan metropolis, central Iran. Ground-based data, TerraClimate, and MODIS products were used to predict air pollution parameters. These factors were first trained using the SVR and Wavelet-SVR models, and their performance was then compared using the error evaluation statistics. Uncertainties were evaluated using local errors and the clustering method. The influencing factors were lastly determined using the permutation features importance method. The results indicated that the Wavelet-SVR model resulted in improving the performance of prediction compared to the SVR model. The mean prediction interval values were also decreased after applying the wavelet transformation on the SVR model. It was found that the dominant agents affecting the temporal changes of study pollutants are soil moisture and meteorological drought. Urban development and increased energy consumption were observed in the areas with the highest air pollution. Researchers and stakeholders can use these findings to assess air pollution hazards and to improve air quality and human living conditions in metropolises.

进行这项研究以评估支持向量回归（SVR）模型在使用和不使用小波变换来预测PM10，PM2.5，SO ₂，NO ₂，CO和O _{3时的性能}位于伊朗中部伊斯法罕（Isfahan）大都市。地面数据，TerraClimate和MODIS产品用于预测空气污染参数。首先使用SVR和Wavelet-SVR模型对这些因素进行训练，然后使用错误评估统计数据对它们的性能进行比较。使用局部误差和聚类方法评估不确定性。最后使用排列特征重要性法确定影响因素。结果表明，与SVR模型相比，Wavelet-SVR模型可提高预测性能。在SVR模型上应用小波变换后，平均预测间隔值也减小了。发现影响研究污染物的时间变化的主要因素是土壤水分和气象干旱。在空气污染最高的地区观察到城市发展和能源消耗增加。研究人员和利益相关者可以利用这些发现来评估空气污染的危害，并改善大都市的空气质量和人类生活条件。