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. 2024 Mar 21;14(1):6795.
doi: 10.1038/s41598-024-54807-1.

Optimized machine learning model for air quality index prediction in major cities in India

Suresh Kumar Natarajan  1 , Prakash Shanmurthy  2 , Daniel Arockiam  3 , Balamurugan Balusamy  4 , Shitharth Selvarajan  5
Affiliations

Optimized machine learning model for air quality index prediction in major cities in India

Suresh Kumar Natarajan et al. Sci Rep. .

Abstract

Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.

Keywords: Air pollution; Air quality index; Decision tree regression; Grey-wolf optimization; Machine learning; Optimization algorithm.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
AQI categories for different pollutants.
Figure 2
Figure 2
Sample data in air quality dataset for Delhi City.
Figure 3
Figure 3
Sample data in air quality dataset for Kolkata city.
Figure 4
Figure 4
Data balancing using SMOTE.
Figure 5
Figure 5
Grey wolf optimization algorithm.
Figure 6
Figure 6
Position update in grey wolf optimization algorithm.
Figure 7
Figure 7
Proposed prediction model.
Figure 8
Figure 8
Details of New Delhi city before and after preprocessing and data balancing.
Figure 9
Figure 9
Details of Bangalore city before and after preprocessing and data balancing.
Figure 10
Figure 10
Details of Chennai city before and after preprocessing and data balancing.
Figure 11
Figure 11
Details of Kolkata city before and after preprocessing and data balancing.
Figure 12
Figure 12
Details of Hyderabad city before and after preprocessing and data balancing.
Figure 13
Figure 13
Details of Visakhapatnam city before and after preprocessing and data balancing.
Figure 14
Figure 14
R-square analysis.
Figure 15
Figure 15
Error analysis.
Figure 16
Figure 16
Accuracy analysis.
See this image and copyright information in PMC

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Dataset
    1. https://www.kaggle.com/datasets/rohanrao/air-quality-data-in-india