A hybridization of MODWT-SVR-DE model emphasizing on noise reduction and optimal parameter selection for prediction of CO2 emission in Thailand

Abstract

AbstractIn present, Thailand is a major manufacturing base in Southeast Asia, which tremendously produces many sources of greenhouse gases (GHGs). The emission CO2 become a spotlight issue since Thailand legislated for controlling industrial carbon footprint, which resulted in carbon credit charged. In order to control and monitor those effects of GHGs, future accuracy of CO2 emission and CO2 emission equivalence play a significant role and can support critical making on suitable control and monitoring. In this research, a hybridization of MODWT-SVR-DE model is developed and proposed that employs maximal overlap discrete wavelet transform (MODWT) with first decomposition to reduce fluctuation of CO2 emission and CO2 emission equivalence. Afterward, the support vector regression is used to formulate complex model. Meanwhile, differential evolution is used to search given parameters of support vector regression. Moreover, the proposed model is compared to conventional forecasting models (i.e. ARIMA, Holt and simple exponential smoothing [SES]) and hybrid model of support vector regression and differential evolution. The empirical results indicated that the proposed model outperforms all candidate models and provides significant difference than candidate models at 0.05 significance levels. Consequently, the proposed model is able to be accurately applied in order to monitor the environment and support policy planners to take step with the helpful guideline.

Keywords

• Noise reduction
• time series analysis
• optimal parameter selection
• environment monitoring
• Zhou Zude
• Wuhan University of Technology