Authors: Zeynep Ceylan
Addresses: Industrial Engineering Department, Faculty of Engineering, Samsun University, 55420, Samsun, Turkey
Abstract: In this study, models have been developed for predicting health expenditures of Turkey associated with greenhouse gas (GHG) emission levels using 27-year dataset between the years 1990 and 2016. The annual GHG emissions data consisting of carbon dioxide, methane, nitrous oxide, and fluorinated gases have been used as inputs. In order to increase the accuracy and reliability, three different models namely, the Bayesian optimisation-based support vector regression (BO-SVR), three-layered feed-forward back-propagation neural network (BPNN), and multivariate linear regression (MLR) models were employed. The coefficient determination (R2) for the BO-SVR, BPNN and MLR models were determined as 0.9893, 0.9796, and 0.9766 in the training phase and 0.9795, 0.9629, and 0.9529 in the testing phase, respectively. The results showed that the BO-SVR model is found to be superior for the estimation of Turkey's health expenditures.
Keywords: GHG emissions; Bayesian optimisation; SVR; health expenditures; forecasting; ANNs.
International Journal of Global Warming, 2020 Vol.20 No.3, pp.203 - 215
Received: 01 Mar 2019
Accepted: 26 Oct 2019
Published online: 03 Apr 2020 *