A GWO Optimized SVR for Time Series Prediction of Black Carbon across a Tropical Coastal Station in India

Black carbon (BC), a potent component of air pollution, is particularly concerning due to its multifaceted role in contributing to climate change and health. Thus, accurate BC mass concentration prediction is crucial. Recently, machine learning (ML) methods for predicting BC concentrations are gathering momentum due to the challenges of employing physics-based models, which necessitate thorough process comprehension. The main objective of this paper is to develop a grey wolf optimization (GWO)-based support vector regression (SVR) model that utilizes the GWO’s optimization capabilities to enhance the SVR to predict near-surface BC mass concentrations. The study utilizes the primary data of nearsurface BC gathered at the national network of aerosol observatories, named the Aerosol Radiative Forcing over India NETwork (ARFINET) under ISRO from 2002 to 2020 across the Trivandrum region of India. The proposed model’s performance, evaluated against the seasonal autoregressive integrated moving average (SARIMA) and conventional SVR models through metrics such as root mean square error (RMSE), mean absolute error percentage (MAPE), and the coefficient of determination $\left(R^{2}\right)$, demonstrates superior predictive accuracy. It achieves the lowest RMSE and MAPE and the highest $R^{2}$ during both training and testing phases, indicating its effectiveness over existing methods. Furthermore, the model forecasts BC concentrations for the next three years. The outcomes highlight the proposed model’s potential for effectively predicting BC mass concentrations and serve as a viable tool for future air quality control efforts.