Optimizing PM2.5 Forecasting Accuracy with Hybrid Meta-Heuristic and Machine Learning Models

TL;DR

This paper presents a hybrid meta-heuristic and machine learning approach to improve hourly PM2.5 air quality forecasting accuracy by optimizing SVR hyperparameters with GWO and PSO algorithms.

Contribution

It introduces a novel hybrid method combining meta-heuristic algorithms with SVR to enhance air pollution prediction accuracy, addressing missing data and baseline factors.

Findings

PSO-SVR achieved R2 of 0.9401

GWO-SVR achieved R2 of 0.9408

Models demonstrated robust and accurate predictions

Abstract

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.