Figure 1.8 Heat map for ozone O3 for day and night in December, 2017.
Figure 1.9 Heat map for ozone O3 for day and night in June, 2020.
Figure 1.10 Heat map for all parameters for 3 days and nights in December, 2017.
Figure 1.11 Heat map for all parameters for 3 days and nights in June, 2020.
Figure 1.12 Predicted values for O3 for Anand Vihar, New Delhi.
Figure 1.13 Predicted values for PM10 for Sector 62, Noida.
Figure 1.14 Pollution levels in major Indian cities.
1.5 Conclusion
After applying K-means clustering using Silhouette coefficient, the data is divided into seven clusters. The SVM is successfully able to classify the data into its respective air quality class with accuracy of 99%. The LSTM models for different places have been tuned accordingly to minimize MAE and RMSE. The proposed model could be used for various purposes like predicting future trends of air quality, assessing past trends of air quality, visualizing data in an effective way, issuing health advisory, and providing health effects (if any) based on the current air quality. Various parameters can be compared and it could be determined which pollutant is affecting more in a particular area and accordingly actions could be taken beforehand. Anyone could get inference from the data easily which is tough to analyze numerically and could take certain actions to control air pollution in any area.
References
1. IHME and HEI State of Global Air/2017, A special report on global exposure to air pollution and its disease burden. State of Global Air, vol. 1, 1–17, 2017.
2. Li, H., Fan, H., Mao, F., A visualization approach to air pollution data exploration—a case study of air quality index (PM2. 5) in Beijing, China. Atmosphere, 7, 3, 35, 2016.
3. Lu, W., Ai, T., Zhang, X., He, Y., An interactive web mapping visualization of urban air quality monitoring data of China. Atmosphere, 8, 8, 148, 2017.
4. Kumar, A., Sinha, R., Bhattacherjee, V., Verma, D. S., & Singh, S., Modeling using K-means clustering algorithm. 1st International Conference on Recent Advances in Information Technology (RAIT), vol. 1, 554–558, IEEE, 2012.
5. Fan, J., Li, Q., Hou, J., Feng, X., Karimian, H., Lin, S., A spatiotemporal prediction framework for air pollution based on deep RNN. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 4, p. 15, 2017.
6. Pereira, R.L., Sousa, P.C., Barata, R., Oliveira, A., Monsieur, G., CitySDK Tourism API-building value around open data. J. Internet Serv. Appl., 6, 1, 1–13, 2015.
7. Adeleke, J.A., Moodley, D., Rens, G., Adewumi, A.O., Integrating statistical machine learning in a semantic sensor web for proactive monitoring and control. Sensors, 17, 4, 807, 2017.
8. Kim, S.H., Choi, J.W., Han, G.T., Air pollution data visualization method based on google earth and KML for Seoul air quality monitoring in realtime. Int. J. Software Eng. Its Appl., 10, 10, 117–128, 2016.
9. Sharma, S., Zhang, M., Gao, J., Zhang, H., Kota, S.H., Effect of restricted emissions during COVID-19 on air quality in India. Sci. Total Environ., 728, 138878, 2020.
10. Mahato, S., Pal, S., Ghosh, K.G., Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Sci. Total Environ., 730, 139086, 2020.
11. Lloyd, S., Least squares quantization in PCM. IEEE Trans. Inf. Theory, 28, 2, 129–137, 1982.
12. Hochreiter, S. and Schmidhuber, J., Long short-term memory. Neural Comput., 9, 8, 1735–1780, 1997.
13. Hasenkopf, C. A., Flasher, J. C., Veerman, O., & DeWitt, H. L., OpenAQ: A Platform to Aggregate and Freely Share Global Air Quality Data. AGU Fall Meeting Abstracts, 2015, A31D-0097, 2015.
14. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Vanderplas, J., Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 12, 2825–2830, 2011.
15. Manaswi, N. K., Understanding and working with Keras, Deep Learning with Applications Using Python, vol. 1, pp. 31–43, Springer, 2018.
1 *Corresponding author: [email protected]
2 †Corresponding author: [email protected]
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