Estimates of greenhouse gas emission in Turkey with grey wolf optimizer algorithm-optimized artificial neural networks

Abstract

The main purpose of this study was to predict Turkey’s future greenhouse gas (GHG) emissions using an artificial neural network (ANN) model trained by a grey wolf optimizer (GWO) algorithm. Gross domestic product, energy consumption, population, urbanization rate, and renewable energy production data were used as predictor variables. To probe the accuracy of the proposed model, the new ANN-GWO model’s performance was compared with the performance of ANN-BP (back propagation), ANN-ABC (artificial bee colony), and ANN-TLBO (teaching–learning-based optimization) models using multiple error criteria. According to calculated error values, the ANN-GWO models predicted GHG emissions more accurately than classical ANN-BP, ANN-ABC, and ANN-TLBO models. According to the average relative error values calculated for the test set, ANN-GWO performs 32.23% better than ANN-BP, 35.29% better than ANN-ABC, and 19.33% better than ANN-TLBO. Using the ANN-GWO model, GHG emissions were forecasted out to 2030 under three different scenarios. The predictions obtained, consistent with a prior forecasting study in the literature, indicated that GHG emissions are expected to outpace official predictions (model prediction range for 2030, 956.97–1170.54 Mt CO2 equivalent). The present study demonstrated that GHG emissions can be predicted accurately with an ANN-GWO model, and that the GWO optimization method is advantageous for predicting future GHG emissions.