Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning

Abstract

Air pollution consists of harmful gases and fine Particulate Matter (PM2.5) 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 PM2.5 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 PM2.5 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 PM2.5 pollutant concentration (PClow-PChigh) range observed for these models is 0-18.583 μg/m3, 18.583-25.023 μg/m3, 25.023-28.234μg/m3, and 28.234-49.032 μg/m3, respectively, so these models can both predict the PM2.5 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 PM2.5 pollutant with a better performance with a reduced error rate than the existing models.

Air quality forecasting‐driven cloud resource allocation for sustainable energy consumption: An ensemble classifier approach

“…Meanwhile, the forecasted results contain numerous uncertainties in predicting spatial interpolation because of non-uniform distribution sampling points and ground monitoring stations. 3,4 So, in recent times, machine learning (ML) based air quality prediction techniques have gained more interest among research communities for accurate forecasts of air quality. An automated learning model with incremental update ability is more suitable for forecasting nonlinear, irregular, and non-stationary sequences of fine air particulate matter.…”

“…In general, the ground air quality monitoring stations erected with multiple sensors continuously monitor the PM2.5 level of atmosphere. Meanwhile, the forecasted results contain numerous uncertainties in predicting spatial interpolation because of non‐uniform distribution sampling points and ground monitoring stations 3,4 . So, in recent times, machine learning (ML) based air quality prediction techniques have gained more interest among research communities for accurate forecasts of air quality.…”

Interpretable Deep Learning Models: Enhancing Transparency and Trustworthiness in Explainable AI

“…The rapid advancement of artificial intelligence (AI) and deep learning has led to significant improvements in the performance of various machine learning models across a wide range of applications, such as computer vision, natural language processing, and medical diagnosis [1,4,9,15]. However, as these models become more complex and sophisticated, their decision-making processes become increasingly opaque, often referred to as "black-box" models [7,8].…”

“…The growing awareness of the importance of interpretability and trustworthiness in AI has motivated researchers to develop methods and techniques that aim to explain and understand the predictions made by complex machine learning models. This field of research is known as Explainable AI (XAI) [2,9,11]. XAI aims to provide human users with insights into the decision-making process of AI systems, enabling them to trust, validate, and potentially challenge the outcomes produced by these models [1,6,12].…”

Retracted: Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: The presence of these indicators undermines our confidence in the integrity of the article's content and we cannot, therefore, vouch for its reliability.…”