Analysis and Prediction for Air Quality Using Various Machine Learning Models

• Autorzy: Dao To-Hieu , Dieu Vu Hoang , Nhat Hoang Van , Thu Nguyen Thi , Tran Duc-Tan , Trung Hoang Quang , Tran Duc-Nghia

Identyfikatory

DOI

10.15439/2022R03

• Języki publikacji: EN

Abstrakty

EN

Air pollution has been a concern in recent years. Measuring the extent of pollution is important to know about the air quality. Previous research has used machine learning algorithms to forecast the Air Quality Index (AQI) in specific locations. Even though that research achieved quite reliable results, they still have some drawbacks that need to be taken into consideration, such as low accuracy or lack of data analysis.On a public dataset, we used Random Forest, XGBoost, and Neural Network to build a machine learning model for the purpose of making predictions about the air quality index (AQI) in a number of cities located in India. The performances of these models were evaluated by using their score errors, Root Mean Square Error (RMSE), and Coefficient Of Determination (R2). This paper demonstrates the analysis of air pollutants from the dataset, which is an effective way to enhance the model's performance.

Słowa kluczowe

EN

machine learning; AQI; data analysis

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 89--94
• Opis fizyczny: Bibliogr. 24 poz., wykr., tab.

Twórcy

autor

Dao To-Hieu

• EEE, Phenikaa University
• GU of Science & Technology Ha Noi, Viet Nam

autor

Dieu Vu Hoang

• EEE, Phenikaa University
• GU of Science & Technology Ha Noi, Viet Nam

autor

Nhat Hoang Van

• Electrical & Electronic Engineering Phenikaa University Ha Noi, Viet Nam

autor

Thu Nguyen Thi

• Electrical & Electronic Engineering Phenikaa University Ha Noi, Viet Nam

autor

Tran Duc-Tan

• Electrical & Electronic Engineering Phenikaa University Ha Noi, Viet Nam

autor

Trung Hoang Quang

• Electrical & Electronic Engineering Phenikaa University Ha Noi, Viet Nam

autor

Tran Duc-Nghia

• Institute of Information Technology Viet Nam Academy of Science & Technology Ha Noi, Viet Nam

Bibliografia

• [1] D.-C. Nguyen, T. Duc-Tan, and D.-N. Tran, “Application of compressed sensing in effective power consumption of WSN for landslide scenario,” in 2015 Asia Pacific Conference on Multimedia and Broadcasting, 2015, pp. 1–5.
• [2] D. T. Pham, D. C. Nguyen, V. V. Pham, B. C. Doan, and D. T. Tran, “Development of a Wireless Sensor Network for Indoor Air Quality Monitoring,” in The 2015 International Conference on Integrated Circuits, Design, and Verification, Vietnam, 2015, pp. 178–183.
• [3] H. Gu, W. Yan, E. Elahi, and Y. Cao, “Air pollution risks human mental health: an implication of two-stages least squares estimation of interaction effects,” Environmental Science and Pollution Research, vol. 27, no. 2, pp. 2036–2043, 2020.
• [4] S. Kumari and M. K. Jain, “A Critical Review on Air Quality Index,” Environmental Pollution, vol. 77, pp. 87–102, 2018.
• [5] S. Lemes, “Air Quality Index (AQI) - comparative study and assesment of an appropriate model For B&H,” in 2th Scientific/Research Symposium with International Participation ‘Metallic And Nonmetallic Materials. MNM, 2018, pp. 282–291.
• [6] N. H. Van, P. Van Thanh, D. N. Tran, and D.-T. Tran, “A new model of air quality prediction using lightweight machine learning,” International Journal of Environmental Science and Technology, 2022. [Online]. Available: https://doi.org/10.1007/s13762-022-04185-w
• [7] N. C. Minh, T. H. Dao, D. N. Tran, Q. H. Nguyen, T. T. Nguyen, and D. T. Tran, “Evaluation of Smartphone and Smartwatch Accelerometer Data in Activity Classification,” in 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), 2021, pp. 33–38.
• [8] N. T. Thu, T.-h. Dao, B. Q. Bao, D.-n. Tran, P. V. Thanh, and D.-T. Tran, “Real-Time Wearable-Device Based Activity recognition Using Machine Learning Methods,” International Journal of Computing and Digital Systems, vol. 12, no. 1, pp. 321–333, 2022. [Online]. Available: https://dx.doi.org/10.12785/ijcds/120126
• [9] J. K. Sethi and M. Mittal, “A new feature selection method based on machine learning technique for air quality dataset,” Journal of Statistics and Management Systems, vol. 22, no. 4, pp. 697–705, 2019. [Online]. Available: https://doi.org/10.1080/09720510.2019.1609726
• [10] H. Liu, Q. Li, D. Yu, and Y. Gu, “Air quality index and air pollutant concentration prediction based on machine learning algorithms,” Applied Sciences (Switzerland), vol. 9, no. 19, 2019.
• [11] M. Castelli, F. M. Clemente, A. Popovic, S. Silva, and L. Vanneschi, "A Machine Learning Approach to Predict Air Quality in California,” Complexity, vol. 2020, pp. 1–23, 2020. [Online]. Available: https://doi.org/10.1155/2020/8049504
• [12] P. Bhawan and E. A. Nagar, “Central Pollution Control Board,” pp. 1–93, 2019.
• [13] R. R. Dickerson, D. C. Anderson, and X. Ren, “On the use of data from commercial NOx analyzers for air pollution studies,” Atmospheric Environment, vol. 214, no. June, p. 116873, 2019. [Online]. Available: https://doi.org/10.1016/j.atmosenv.2019.116873
• [14] S. Zhang, “Nearest neighbor selection for iteratively kNN imputation,”Journal of Systems and Software, vol. 85, no. 11, pp. 2541–2552, 2012. [Online]. Available: http://dx.doi.org/10.1016/j.jss.2012.05.073
• [15] Y. Liu, Y. Wang, and J. Zhang, “New machine learning algorithm: Random forest,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7473 LNCS, pp. 246–252, 2012.
• [16] Y. Fang and J. Wang, “Selection of the number of clusters via the bootstrap method,” Computational Statistics and Data Analysis, vol. 56, no. 3, pp. 468–477, 2012. [Online]. Available: http://dx.doi.org/10.1016/j.csda.2011.09.003
• [17] L. Yang, S. Liu, S. Tsoka, and L. G. Papageorgiou, “A regression tree approach using mathematical programming,” Expert Systems with Applications, vol. 78, pp. 347–357, 2017. [Online]. Available: http://dx.doi.org/10.1016/j.eswa.2017.02.013
• [18] I. A. Ibrahim, T. Khatib, A. Mohamed, and W. Elmenreich, “Modeling of the output current of a photovoltaic grid-connected system using random forests technique,” Energy Exploration and Exploitation, vol. 36, no. 1, pp. 132–148, 2018.
• [19] Y. Wang, Z. Pan, J. Zheng, L. Qian, and M. Li, “A hybrid ensemble method for pulsar candidate classification,” Astrophysics and Space Science, vol. 364, no. 8, 2019.
• [20] D. Faraggi and R. Simon, “A neural network model for survival data,”Statistics in Medicine, vol. 14, no. 1, pp. 73–82, 1995.
• [21] C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Climate Research, vol. 30, no. 1, pp. 79–82, 2005.
• [22] A. H. Murphy, “The coefficients of correlation and determination as measures of performance in forecast verification,” Weather and Forecasting, vol. 10, no. 4, pp. 681–688, 1995.
• [23] A. C. Müller and S. Guido, Introduction To Machine Learning With Python: A Guide For Data Scientists. O’Reilly Media, Inc., 2016.
• [24] C. Song and X. Fu, “Research on different weight combination in air quality forecasting models,” Journal of Cleaner Production, vol. 261, p. 121169, 2020. [Online]. Available: https://doi.org/10.1016/j.jclepro.2020.121169