AOD Forecasting using Prophet Model Across Four Major Urban Areas in India

Abstract

Accurate aerosol optical depth (AOD) forecasting aids in the air quality analysis as it is an important parameter that provides a quantitative measure of aerosols existing in the atmosphere. To an extent, to date, AOD forecasting has been limited to conventional statistical methods such as simple linear regression, multiple linear regression, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Prophet models have gained popularity recently due to their simple interpretable parameters and efficiency in dealing with strong seasonal time-series data. This paper aims at the development and analysis of Prophet Model for the AOD forecasting across Delhi, Mumbai, Kolkata and Trivandrum in India based on the monthly mean AOD <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">550nm</inf> data obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) over 19 years (2001 to 2019). Conventional SARIMA model is used to validate the performances of the Prophet model. The forecasts for the upcoming 5 years were implemented at a 95 percent confidence interval. The model evaluation was carried out based on the performance metrics and the execution time taken. The performance metrics considered are the correlation coefficient (R), root mean square error (RMSE) and mean absolute percentage error (MAPE). The Prophet Model performed reasonably better than the SARIMA model with the lowest MAPE and RMSE in all four regions. The correlation obtained by the Prophet Model are 0.83, 0.79, 0.522, and 0.55 and by SARIMA are 0.81, 0.76, 0.520, and 0.51 across Delhi, Mumbai, Kolkata and Trivandrum respectively. Prophet based models took only a few seconds to execute, unlike SARIMA which took a minimum of half an hour.