  Timely alerts about hazardous air pollutants are crucial for public health.
However, existing forecasting models often overlook key factors like baseline
parameters and missing data, limiting their accuracy. This study introduces a
hybrid approach to address these issues, focusing on forecasting hourly PM2.5
concentrations using Support Vector Regression (SVR). Meta-heuristic
algorithms, Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO),
optimize SVR Hyper-parameters "C" and "Gamma" to enhance prediction accuracy.
Evaluation metrics include R-squared (R2), Root Mean Square Error (RMSE), and
Mean Absolute Error (MAE). Results show significant improvements with PSO-SVR
(R2: 0.9401, RMSE: 0.2390, MAE: 0.1368) and GWO-SVR (R2: 0.9408, RMSE: 0.2376,
MAE: 0.1373), indicating robust and accurate models suitable for similar
research applications.
