A novel bagging ensemble approach for predicting summertime ground-level ozone concentration

Figures & data

Figure 1. (a) Map of India. (b) Map of Tamil Nadu. (c) Map showing the study area at Gummidipoondi (domain size: (length × height: 1.5 km × 2.4 km). (a), (b) Image source: maps of India. (c) Image source: Google Earth Pro.

Figure 2. Processes involved in model development of ensemble bagging tree.

Figure 3. Schematic diagram showing the proposed modeling methodology.

Table 1. Statistics summary of the attributes for the prediction of ground-level O₃ concentration during the study period.

	Statistics summary (training set) (847 Instances)				Statistics summary (test set) (165 Instances)
Attribute	Minimum	Maximum	Mean	Std. dev.	Minimum	Maximum	Mean	Std. dev.
O3 (µg/m^3)	15.44	497.232	61.64	61.35	20.98	398.928	61.55	46.87
Relative humidity (%)	24.53	99.07	82.128	17.78	21.5	99.1	77.7	15.8
Temperature (°C)	28.86	40.59	33.019	2.60	32.58	40.3	30.2	2.8
Wind speed (mph)	0.21	4.04	1.36	0.621	0.15	4.5	1.9	0.7
Wind direction (degrees)	0	358	142.16	101.32	0	355	169	83
Solar radiation (W/m^2)	50.48	933.5	296.69	265.5	65.71	924.9	273.83	272.6

Figure 4. (a) Time-series plot of O₃ across the entire study period (January 2016–December 2016). The dashed line indicates the threshold limit value of hourly ambient O₃ concentration (180 μg/m³). Observed O₃ concentration greater than 180 μg/m³ marked using red circles are considered as peak values. (b) Time-series plot of O₃ during summer season (March 2016–May 2016). The interpretations of the dashed line and red circles are the same as in (a). (c) Hourly variation of relative humidity, temperature, and wind speed (March 2016–May 2016).

Figure 5. Seven days air mass back trajectory using HYSPLIT reaching the monitoring location (March 2016–May 2016).

Table 2. Performance of the bagging classifier for predicting ground-level O₃ concentration.

	Without NOX		With NOX
	IoAd	R^2	IoAd	R^2
MLP	0.73	0.40	0.71	0.38
Bagged MLP	0.64	0.67	0.64	0.57
RTree	0.75	0.57	0.76	0.55
Bagged RTree	0.78	0.69	0.75	0.61
REPTree	0.62	0.69	0.60	0.62
Bagged REPTree	0.69	0.71	0.65	0.68
Random forest	0.82	0.70	0.80	0.69
Bagged random forest	0.96	0.93	0.93	0.86

Figure 6. Improvement of R² statistic for various iteration and cluster size in case of bagged random forest.

Table 3. Wilcoxon signed rank test for base classifiers and bagged classifiers.

	MLP	Rtree	REPTree	Random forest	B-MLP	B-Rtree	B-REPTree	B-Random forest
MLP	—
Rtree	−13.036*	—
REPTree	−11.5*	−7.967	—
Random forest	−10.396*	−9.394*	−2.946*	—
B-MLP	−12.141*	−2.553*	−8.498	−11.095	—
B-Rtree	−10.731*	−7.071*	−0.073	−2.552*	−8.173	—
B-REPTree	−10.596*	−7.397*	−1.389	−1.489	−11.67	0.99*	—
B-Random forest	−9.579*	−8.994*	−4.038*	−4.519*	−12.662	−4.04*	−3.319*	—

Note. *Significant at 0.05 level.

Figure 7. Box plot representation of observed vs. predicted concentration for single base models and bagged ensemble models.

Figure 8. Scatter plots of observed vs. predicted ozone for (a) random forest and (b) bagged random forest. The interpretations of the dashed line and red circles are same as in Figure 4a.

Table 4. Number of peaks (values >180 μg/m³) having PEP (%) less than the specified range.

	Single classifiers					Ensemble classifiers
	<1%	<5%	<10%	<15%	<20%	<1%	<5%	<10%	<15%	<20%
MLP	0	5	5	4	0	0	9	7	3	2
RTree	4	8	5	6	4	2	8	5	6	4
REPTree	1	10	9	3	0	3	6	7	6	4
Random forest	1	8	10	3	4	10	20	12	5	2

Table 5. Performance of the Bagging classifier for predicting ground-level O₃ concentration using a test data set.

	IoAd	R^2
MLP	0.68	0.35
Bagged MLP	0.60	0.61
RTree	0.69	0.52
Bagged RTree	0.75	0.60
REPTree	0.70	0.71
Bagged REPTree	0.59	0.65
Random forest	0.78	0.63
Bagged random forest	0.89	0.86

Figure 9. Prediction of hourly O₃ concentration using random forest and bagged random forest for an independent test set (April 10, 2016, and March 28, 2016).