Research Article Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning D. Kothandaraman , 1 N. Praveena , 2 K. Varadarajkumar , 3 B. Madhav Rao, 4 Dharmesh Dhabliya, 5 Shivaprasad Satla, 6 and Worku Abera 7 1 School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India 2 Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India 3 Department of Computer Science and Engineering, Malla Reddy University, Hyderabad, 500043 Telangana, India 4 Department of Computer Science and Engineering, SIR C R Reddy College of Engineering, Eluru, India 5 Department of Computer Engineering, Vishwakarma Institute of Information Technology, India 6 Department of Computer Science and Engineering, Malla Reddy Engineering College, Secunderabad, 500100 Telangana, India 7 Department of Food Process Engineering, College of Engineering and Technology, Wolkite University, Wolkite, Ethiopia Correspondence should be addressed to D. Kothandaraman; kothanda_raman_d@srecwarangal.ac.in, N. Praveena; praveena.4u@gmail.com, and Worku Abera; worku.abera@wku.edu.et Received 28 March 2022; Revised 24 April 2022; Accepted 6 May 2022; Published 26 June 2022 Academic Editor: Lakshmipathy R Copyright © 2022 D. Kothandaraman et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Air pollution consists of harmful gases and fine Particulate Matter (PM 2.5 ) which affect the quality of air. This has not only become the key issues in scientific research but also turned to be an important social issues of the public’s life. Therefore, many experts and scholars at different R&Ds, universities, and abroad are involved in lot of research on PM 2.5 pollutant predictions. In this scenario, the authors proposed various machine learning models such as linear regression, random forest, KNN, ridge and lasso, XGBoost, and AdaBoost models to predict PM 2.5 pollutants in polluted cities. This experiment is carried out using Jupyter Notebook in Python 3.7.3. From the results with respect to MAE, MAPE, and RMSE metrics, among the models, XGBoost, AdaBoost, random forest, and KNN models (8.27, 0.40, and 13.85; 9.23, 0.45, and 10.59; 39.84, 1.94, and 54.59; and 49.13, 2.40, and 69.92, respectively) are observed to be more reliable models. The PM 2.5 pollutant concentration (PC low -PC high ) range observed for these models is 0-18.583 μg/m 3 , 18.583-25.023 μg/m 3 , 25.023-28.234μg/m 3 , and 28.234- 49.032 μg/m 3 , respectively, so these models can both predict the PM 2.5 pollutant and can forecast the air quality levels in a better way. On comparison between various existing models and proposed models, it was observed that the proposed models can predict the PM 2.5 pollutant with a better performance with a reduced error rate than the existing models. 1. Introduction Nowadays, accurate air pollution prediction and forecast become a challenging and significant task due to increased air pollution which acts as a fundamental problem in many parts of the world. Generally, the pollution is divided into two types: (1) natural pollution because of volcanic erup- tions and forest fires resulting in emission of SO 2 , CO 2 , CO, NO 2 , and sulfate as air pollutants and (2) man-made pollution because of some human activities such as burning of oils, discharges from industrial production processes, and transportation emissions that have PM 2.5 as its major air pollutant [1] which has received much attention due to their destructive effects on human health, other kinds of creatures, and environment [2]. Various studies testify that air pollu- tion leads to respiratory and cardiovascular disease leading to death of animals and plants, acid rain, climate change, global warming, etc. thus making economic loses and the human life of a society difficult to survive in the world [3]. Regarding the effects of PM 2.5 investigated over the last 25 Hindawi Adsorption Science & Technology Volume 2022, Article ID 5086622, 15 pages https://doi.org/10.1155/2022/5086622