Forecasting And Prediction Of Air Pollutants Concentrates Using Machine Learning Techniques: The Case Of India

Abstract

Air quality index (AQI) is a number used by government agencies to communicate to the public how polluted the air currently. It is based on several factors like SO2, NO2, O3, RSPM/PM10, and PM2.5. Several methods were developed in the past by various researchers/environmental agencies for the determination of AQI. Still, there is no universally accepted method that exists, which is appropriate for all situations. We have developed a prediction model that is confined to standard classification or regression models. These prediction models have ignored the co-relation between sub-models in different time slots. The paper focusses on a refined model for inferring air pollutants based on historical and current meteorological datasets. Also, the model is designed to forecast AQI for the coming months, quarters or years where the emphasis is on how to improve its accuracy and performance. The algorithms are used on Air Pollution Geocodes Dataset (2016-2018), and results calculated for 196 cities of India on various classifiers. Accuracy of 94%-96% achieved from Linear Robust Regression, which increases to 97.92% after application of KNN and 97.91% after SVM and 97.47 after 5th epoch of ANN. Decision Tree Classifier has given the best accuracy of 99.7%, which increases by 0.02% on the application of the Random Forest Classifier. Forecasting achieved by Moving Average Smoothing using R-ARIMA, which offers daily values for the coming 45days or monthly data of AQI for the next year.

Prediction of concentration of SOx, NOx and PM10 due to vehicular emission using CART (Regression Tree) analysis

“…A terminal node is a node at which no further split will occur. It concludes that splitting terminates if there is no change by splitting them [19,20]. CART can deal with any type of variable (numeric, binary, categorical).…”

Comparative Analysis of Machine Learning Techniques for Predicting Air Pollution

“…Sethi et al [12] proposed a method to predict the concentration of PM2. Enebish et al, [8] Kiftiyani & Nazhifah [21] Masood & Ahmad [9] Usmani [10] Bozdag [11] Sethi et al [12] Sharma et al [22] Juarez & Petersen [23] Sharma et al [31] Castelli et al [32] Asgari et al [33] Chen et al [34] Gocheva-Ilieva et al [35] Lee et al [13] Doresawamy et al [14] Ma et al [15] Bhalgat et al [24] Shen et al [25] Ma et al [40] Bouzoukis et al [36] Bouzoukis et al [36] Masmoudi et al [37] Khan et al [26] Kanjo [27] Lepperod [29] Peng et al [38] Zhang et al [17] Rubal et al [28] Liu et al [39] Fu et al [ LR gave promising results, and after that, CBL was applied to selected features in which RF improved the veracity in predicting PM2.5…”

“…The results indicated that air quality prediction accuracy was more than 80%. Sharma et al[31] developed a model to forecast AQI, including the impurities named PM2.5, PM10, O3, NO2, and SO2. and calculated results for 196 cities in India on different classifiers.…”

Machine learning algorithms to forecast air quality: a survey

“…2021 ; Sharma et al. 2021 ). Unfortunately, their scope is rather limited, the total number of analysed papers is very small and the considered classification categories are very restricted.…”