PM₁₀ Prediction Model by Support Vector Regression Based on Particle Swarm Optimization

Abstract:

This paper proposes a prediction model for the PM₁₀ forecasting in Bangkok. Particulate matter (PM₁₀) with aerodynamic diameter up to 10 m (PM10) is targeted because these small particles effects people’s health and it constitutes major con_{Subscript text}cern for the air quality of Bangkok. Support vector regression (SVR) has been successfully employed to solve regression problem of nonlinearity. The determination for hyper-parameters including kernel parameters and the regularization is important to the performance of SVR. Particle swarm optimization (PSO) is a method for finding a solution of stochastic global optimizer based on swarm intelligence. Using the interaction of particles, PSO searches the solution space intelligently and finds out the best one. Thus, the proposed forecasting model based on the global optimization of PSO and local accurate searching of SVR is applied to forecast PM10 in this paper. The results of this research show the practical prediction model of PM10 based on PSO-SVR is established with C = 5009, ε = 0.0011, σ = 0.1072. The mean squared error (MSE) of the prediction model using PSO-SVR is about 8.654610-11. Practical results indicate that the application of the PSO-SVR method to temperature forecasting of PM10 is feasible and effective. The results show that the model is effective and highly accurate in the forecasting of PM₁₀.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 3693-3698
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.3693
• Online since: November 2011
• Authors: Saowalak Arampongsanuwat, Phayung Meesad
• Keywords: Particle Swarm Optimization Algorithm (PSO), PM₁₀, Prediction, Support Vector Regression (SVR)

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