Optimizing PM2.5 Forecasting Accuracy with Hybrid Meta-Heuristic and Machine Learning Models
It addresses air pollution forecasting for public health by improving prediction accuracy, though it is incremental as it combines existing methods.
This study tackled the problem of inaccurate PM2.5 forecasting by developing a hybrid approach that optimizes Support Vector Regression hyperparameters with meta-heuristic algorithms, resulting in models with high accuracy, such as R-squared values around 0.94 and low errors like RMSE of approximately 0.24.
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.