Volume 24 , Issue 2 , PP: 80-93, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Afrah Al-Bossly 1 * , Shoraim M. H. A. 2 , Amal O. A. Al magdashi 3 , Badr Eldeen A. A. Abouzeed 4
Doi: https://doi.org/10.54216/IJNS.240208
Industrialization and urbanization air is getting polluted due to human activities. CO, NO, C6H6, etc., are the major air pollutants. The focus of air pollutants in ambient air is controlled by the climatological parameters including wind direction, atmospheric speed of wind, temperature, and humidity. Air pollution prediction is a critical sector where machine learning (ML) technique plays a major role. Its main purpose is to tackle and understand the damaging effects of air pollutants on the environment and human health. By using a range of ML techniques such as neural networks, regression, and decision trees, we could analyze historical data on air quality alongside geographical and meteorological factors. This allows us to design model that could detect patterns and predict pollution levels. By taking proactive measures such as providing timely alerts to the public, adjusting controls on emissions, and, implementing strategies to reduce pollution, we can work towards creating healthier and cleaner environments. Embracing the potential of artificial intelligence (AI) in air pollution prediction empowers us to protect the well-being of our communities and make informed decisions. Therefore, this study develops an Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction (ISTSVNI-APP) approach. The major objective of the ISTSVNI-APP technique is to exploit AI concepts with neutrosophic sets (NS) models for the forecasting of air pollution. To do so, the ISTSVNI-APP technique makes use of min-max normalization as the initial preprocessing step. For predicting air pollution, the ISTSVNI-APP technique uses STSVNI approach. To improve the performance of the ISTSVNI-APP technique, modified crow search algorithm (MCSA) is used for the parameter tuning of the STSVNI system. The performance evaluation of the ISTSVNI-APP method is verified utilizing benchmark dataset. The experimental outcomes stated that the ISTSVNI-APP technique gains better performance in predicting air pollution
Artificial Intelligence , Air Pollution Prediction , Crow Search Algorithm , Neutrosophic Sets , Air Quality Index
[1] C. R. Aditya, C. R. Deshmukh, N. D K, P. Gandhi, and V. astu, “Detection and prediction of air pollution using machine learning models,” International Journal of Engineering Trends and Technology, vol. 59, no. 4, pp. 204–207, 2018.
[2] H. Liu, Q. Li, D. Yu, and Y. Gu, “Air quality index and air pollutant concentration prediction based on machine learning algorithms,” Applied Sciences, vol. 9, p. 4069, 2019.
[3] 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, Article ID 8049504, 23 pages, 2020.
[4] A. Shishegaran, M. Saeedi, A. Kumar, and H. Ghiasinejad, “Prediction of air quality in Tehran by developing the nonlinear ensemble model,” Journal of Cleaner Production, vol. 259, Article ID 120825, 2020.
[5] L. Tuan-Vinh, “Improving the awareness of sustainable smart cities by analyzing lifelog images and IoT air pollution data,” in Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), IEEE, Orlando, FL, USA, September 2021.
[6] G. Mani, J. K. Viswanadhapalli, and A. A. Stonie, “Prediction and forecasting of air quality index in Chennai using regression and ARIMA time series models,” Journal of Engineering Research, vol. 9, 2021.
[7] S. V. Kottur and S. S. Mantha, “An integrated model using Artifcial Neural Network (ANN) and Kriging for forecasting air pollutants using meteorological data,” Int. J. Adv. Res. Comput. Commun. Eng, vol. 4, pp. 146–152, 2015.
[8] H. Maleki, A. Sorooshian, G. Goudarzi, Z. Baboli, Y. Tahmasebi Birgani, and M. Rahmati, “Air pollution prediction by using an artifcial neural network model,” Clean Technologies and Environmental Policy, vol. 21, no. 6, pp. 1341–1352, 2019
[9] S. Halsana, “Air quality prediction model using supervised machine learning algorithms,” International Journal of Scientifc Research in Computer Science, Engineering and Information Technology, vol. 8, pp. 190–201, 2020.
[10] A. G. Soundari, J. Gnana, and A. C. Akshaya, “Indian air quality prediction and analysis using machine learning,” International Journal of Applied Engineering Research, vol. 14, p. 11, 2019.
[11] Deepan, S. and Saravanan, M., 2024. Air quality index prediction using seasonal autoregressive integrated moving average transductive long shortâterm memory. ETRI Journal, p.e12658.
[12] Alkabbani, H., Ramadan, A., Zhu, Q. and Elkamel, A., 2022. An improved air quality index machine learning-based forecasting with multivariate data imputation approach. Atmosphere, 13(7), p.1144.
[13] Anggraini, T.S., Irie, H., Sakti, A.D. and Wikantika, K., 2024. Machine learning-based global air quality index development using remote sensing and ground-based stations. Environmental Advances, 15, p.100456.
[14] Van, N.H., Van Thanh, P., Tran, D.N. and Tran, D.T., 2023. A new model of air quality prediction using lightweight machine learning. International Journal of Environmental Science and Technology, 20(3), pp.2983-2994.
[15] Veeranjaneyulu, R., Boopathi, S., Kumari, R.K., Vidyarthi, A., Isaac, J.S. and Jaiganesh, V., 2023, May. Air quality improvement and optimisation using machine learning technique. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-6). IEEE.
[16] Zhao, Z., Wu, J., Cai, F., Zhang, S. and Wang, Y.G., 2023. A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic. Scientific Reports, 13(1), p.1015.
[17] Abirami, G., Girija, R., Das, A. and Sreenivasan, N., 2022. Predicting air quality index with machine learning models. In Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems (pp. 353-371). CRC Press.
[18] Islam, M.J., Ahmad, S., Haque, F., Reaz, M.B.I., Bhuiyan, M.A.S. and Islam, M.R., 2022. Application of min-max normalization on subject-invariant EMG pattern recognition. IEEE Transactions on Instrumentation and Measurement, 71, pp.1-12.
[19] Ashraf, S. and Abdullah, S., 2020. Decision support modeling for agriculture land selection based on sine trigonometric single valued neutrosophic information. International Journal of Neutrosophic Science (IJNS), 9(2), pp.60-73.
[20] Das, S., Sahu, T.P. and Janghel, R.R., 2022. Stock market forecasting using intrinsic time-scale decomposition in fusion with cluster based modified CSA optimized ELM. Journal of King Saud University-Computer and Information Sciences, 34(10), pp.8777-8793.
[21] Gupta, N.S., Mohta, Y., Heda, K., Armaan, R., Valarmathi, B. and Arulkumaran, G., 2023. Prediction of air quality index using machine learning techniques: a comparative analysis. Journal of Environmental and Public Health, 2023, pp.1-26.
