Modeling PM_2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

Figure 9

Neural Network’s regressive prediction of Cotocollao PM_2.5 concentration (light grey) compared to the real data (dark grey) during the wet season plotted against daily rain accumulation and wind speed thresholds, >1 mm and >2.5 m/s, respectively (see Table 6, thresholds obtained from 3-class classification). The dashed black line represents the national standards for PM_2.5 annual concentrations.