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

ABSTRACT

Particulate matter is emitted from diverse sources and affect the human health very badly. Dust particles exposure from the stated environment can affect our heart and lungs very badly. The particle pollution exposure creates a variety of problems including nonfatal heart attacks, premature deaths in people with lung or heart disease, asthma, difficulty in breathing, etc. In this article, we developed an automated tool by computing multimodal features to capture the diverse dynamics of ambient particulate matter and then applied the Chi-square feature selection method to acquire the most relevant features. We also optimized parameters of robust machine learning algorithms to further improve the prediction performance such as Decision Tree, SVM with Linear and Regression, Naïve Bayes (NB), Random Forest (RF), Ensemble Classifier, K-Nearest Neighbor, and XGBoost for classification. The classification results with and without feature selection methods yielded the highest detection performance with random forest, and GBM yielded 100% of accuracy and AUC. The results revealed that the proposed methodology is more robust to provide an efficient system that will detect the particulate matters automatically and will help the individuals to improve their lifestyle and comfort. The concerned department can monitor the individual’s healthcare services and reduce the mortality risk

Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP 2/46/43).

Princess Nourah bint Abdulrahman University Researchers supported Project number (PNURSP2022R114), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

The authors would like to thank the ‎Deanship of Scientific Research at Umm Al-Qura University ‎for supporting this work by Grant Code: (22UQU4310373DSR22).

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Availability of data and materials

The data set on Particulate Matter (PM2.5) concentrations was collected from the main campus of University of Azad Jammu and Kashmir, which is located at the roadside, leading from Combined Military Hospital (CMH) to upper Addah, of Muzaffarabad as detailed in Hussain et al. (2020a). The data can be provided on request.