Air Quality Prediction Model using Supervised Machine Learning Algorithms
DOI:
https://doi.org/10.32628/CSEIT206435Keywords:
AQI, PM2.5, PM10, Machine Learning Algorithms, Dataset, Preprocessing, Regression, Training, Testing, Standardization, Normalization, Outlier, Correlation.Abstract
Air pollution is the “world’s largest environmental health threat”[1], causing 7 million deaths[1] worldwide every year. Its major constituents are PM2.5, PM10 and the harmful green house gases S02, N02, C0 and other effluents from vehicles and factories affecting not only humans but also other living organisms both on land and sea. The only effective solution to this global issue is to implement machine learning algorithms to predict the AQI (Air Quality Index ) that can make the people aware of the condition of the air of a certain region such that certain actions could be issued by the government for the improvement of the air quality in the future. The prime objective behind this project is to predict the AQI based on the concentration of PM2.5, PM10,S02, N02, C0 as well as weather conditions like temperature, pressure and humidity[2].Hence the data set is combined from various web sources like cpcb.nic.in and uci repository in order to bring accuracy in the prediction and to justify whether the Quality of air is suitable or not. This prediction will be brought about with the help of some supervised machine learning algorithms and the observation and the result will state which algorithm is giving better accuracy in prediction of AQI and which one is giving less error.
References
- Campbell-Lendrum, D., & Prüss-Ustün, A. (2018). Climate change, air pollution and noncommunicable diseases. Bulletin Of The World Health Organization, 97(2), 160-161. https://doi.org/10.2471/blt.18.224295
- Li, J., Li, X., & Wang, K. (2019). Atmospheric PM2.5Concentration Prediction Based on Time Series and Interactive Multiple Model Approach. Advances In Meteorology, 2019, 1-11. https://doi.org/10.1155/2019/1279565
- Soundari, M., Jeslin, M., & A.C, A. (2019). INDIAN AIR QUALITY PREDICTION AND ANALYSIS USING MACHINE LEARNING. International Journal Of Applied Engineering Research, 14(0973-4562), 1-6. Retrieved 22 July 2020, from https://www.ripublication.com/ijaerspl2019/ijaerv14n11spl_34.pdf.
- C R, A., Deshmukh, C., D K, N., Gandhi, P., & astu, V. (2018). Detection and Prediction of Air Pollution using Machine Learning Models. International Journal Of Engineering Trends And Technology, 59(4), 204-207. https://doi.org/10.14445/22315381/ijett-v59p238
- Kleine Deters, J., Zalakeviciute, R., Gonzalez, M., & Rybarczyk, Y. (2017). Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters. Journal Of Electrical And Computer Engineering, 2017, 1-14. https://doi.org/10.1155/2017/5106045
- Sayyed, M., Sarode, A., Salunke, A., & Desai, S. (2020). A thorough Survey on prediction of Airpollution. Journal Of Emerging Technologies And Innovative Research, 7(3), 1-3. Retrieved 22 July 2020, from http://www.jetir.org/papers/JETIR2003302.pdf.
- Bhalgat, P., Pitale, S., & Bhoite, S. (2019). Air Quality Prediction using Machine Learning Algorithms. International Journal Of Computer Applications Technology And Research, 8(9), 367-370. https://doi.org/10.7753/ijcatr0809.1006
- Brownlee, J. (2020). Linear Regression for Machine Learning. Machine Learning Mastery. Retrieved 22 July 2020, from https://machinelearningmastery.com/linear-regression-for-machine-learning/.
- Decision Tree Regression — scikit-learn 0.23.1 documentation. Scikit-learn.org. (2020). Retrieved 22 July 2020, from http://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html.
- Random Forest Regression in Python - GeeksforGeeks. GeeksforGeeks. (2020). Retrieved 22 July 2020, from https://www.geeksforgeeks.org/random-forest-regression-in-python/#:~:text=A%20Random%20Forest%20is%20an,Aggregation%2C%20commonly%20known%20as%20bagging.&text=Random%20Forest%20has%20multiple%20decision%20trees%20as%20base%20learning%20models.
- Data pre-processing. En.wikipedia.org. (2020). Retrieved 22 July 2020, from https://en.wikipedia.org/wiki/Data_pre-processing.
- Outlier. En.wikipedia.org. (2020). Retrieved 22 July 2020, from https://en.wikipedia.org/wiki/Outlier#:~:text=In%20statistics%2C%20an%20outlier%20is,serious%20problems%20in%20statistical%20analyses.
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