Transfer Learning based GDP Prediction from Uncertain Carbon Emission Data

Abstract

This paper proposes the novel way to estimate the gross domestic product (GDP) of a country from its carbon emission (CO 2 ) data. This alternative method to predict GDP is required for the war affected and non-accessible nations as the macroeconomic data available for those nations is highly unpredictable or insufficient. However, first we need to train and develop a reliable model for which we have used the Transfer learning (TL) approach. TL is applied in a way that a neural network (NN) or machine learning (ML) model is trained on the developed nation’s data and used for testing with developing nation data. The NN models used in this paper are Extreme Learning Machine (ELM) and Generalized Regression Neural Network (GRNN), while ML model is Support Vector Regression (SVR). Since the data is measurement data collected from several devices, it contains noise and hence, uncertain. Thus, first we have modelled the dataset with type-1 fuzzy sets and then with the interval type-2 fuzzy sets. The results are then compared with the crisp input data values.