Quantile Regression and Deep Learning Models for Air Quality Analysis and Prediction in Delhi City

Abstract

Quantile regression models have gained popularity among researchers these days.The mean regression model estimates the mean of yi given x. But in some applications,estimation of the quantiles of yi given x is not very useful. This thesispresents a data-driven analysis and prediction of air quality in Delhi metro cityusing quantile regression and deep learning models.The main objectives are to investigate the monthly trend and correlation ofPM2.5, PM10, NO2 and SO2 concentration and temperature, to compare differentregression models such as linear, quadratic, kernel, and quantile regression toestimate the PM2.5, PM10, NO2 and SO2 concentration using the temperaturevariables, and to compare different deep learning models such as gated recurrentunits (GRUs), vanilla(LSTM), simple long short-term memory (LSTM) networks,convolutional neural network - long short-term memory (CNN-LSTM) networks,and support vector regression (SVR) for time series forecasting of pollution levels.The data used in this study is the Delhi air quality data from 2015 to 2020, whichcontains various pollutants and environmental factors.The results show that quantile regression is more flexible, robust, and informativethan other models, and can capture the variability and diversity of thePM2.5, PM10, NO2 and SO2 distribution over distinct quantiles or percentiles.The results also show that deep learning models are effective and powerful toolsfor time series forecasting on pollution data. Among them, the SVR model is superiorto other models. The study aims to contribute to the scientific knowledgeand practical solutions for air quality prediction and analysis.