Smart Cities-Based Improving Atmospheric Particulate Matters Prediction Using Chi-Square Feature Selection Methods by Employing Machine Learning Techniques

Figures & data

Figure 1. Schematic diagram to detect particulate matter (PM).

Figure 2. Multimodal features ranking.

Table 1. Prediction of particulate matters based on the multimodal features extration approach and employing robust machine learning techniques using 10-fold cross-validation with the chi-square feature selection method

Classifier	Sensitivity	Specificity	PPV	NPV	Accuracy	AUC
CART	0.93	0.9	0.926	0.905	0.9167	1
KNN	0.81	1	1	0.808	0.8958	0.989
SVM-L	1	1	1	1	1	1
SVM-R	1	1	1	1	1	1
NB	0.89	0.86	0.889	0.857	0.875	0.926
RF	1	1	1	1	1	1
GBM	1	1	1	1	1	1
XGB-L	0.96	1	1	0.955	0.9792	0.996
XGB-T	1	1	1	1	1	1

Table 2. Prediction of particulate matters based on the multimodal features extration approach and employing robust machine learning techniques using 10-fold cross-validation without the feature selection method

Classifier	Sensitivity	Specificity	PPV	NPV	Accuracy	AUC
CART	0.93	0.9	0.926	0.905	0.916	0.915
KNN	0.96	0.9	0.929	0.95	0.9375	0.989
SVM-L	1	0.86	0.9	1	0.9375	0.986
SVM-R	1	1	1	1	1	1
NB	0.85	0.86	0.885	0.818	0.8542	0.885
RF	1	1	1	1	1	1
GBM	1	1	1	1	1	1
XGB-L	0.89	1	1	0.875	0.9375	0.996
XGB-T	0.86	1	1	0.955	0.9375	1

Figure 3. Frequency distribution of various multimodal extracted features to distinguish the indoor particulate matter (PM) from outdoor PM.