Anomaly based novel multi-source unsupervised transfer learning approach for carbon emission centric GDP prediction

Abstract

Historically, non-renewable fuels are predominant energy sources which drive various industrial and economic activities of a nation. Moreover, it has been well established in literature that carbon emission due to fuel consumption has proportional effect on the economic growth of a nation. There are many war-torn/isolated countries in the world whose gross domestic product (GDP) values are unavailable for certain years in the data repository of the World Bank. Since, hardly any work addresses this research gap of estimating missing GDP of such nations. This paper aims to predict the missing per capita GDP of war-torn/isolated countries using only their carbon emission data. The available data of these countries are not sufficient enough to train a robust predictive model to estimate their missing GDPs. Therefore, this paper proposes multi-source unsupervised transfer learning (UTL), which is an emerging yet under-explored area of transfer learning, to enlarge the training datasets in order to build a robust GDP prediction model. Thus, it exploits multi-source UTL to generate a set of distinct training domains from the collection of carbon emission dataset of various developing countries as well as a mixture of developed and developing countries by suitably detecting and removing the anomalies using isolation forest. To demonstrate the working of the proposed approach, extensive simulations are carried out by considering the carbon emission data sets of six war-torn/isolated countries viz., Afghanistan, Myanmar, Syria, Vietnam, Yemen and Iraq. Here, five different training domains were generated which are then empirically evaluated using three different kind of predictive regression models. The domain with best resulting accuracy is chosen to predict the missing per capita GDP of these war-torn/isolated nations. It is found that the proposed approach performed exceptionally well in increasing the size of relevant training dataset and hence provides a robust GDP prediction model. The strength and weakness of the proposed approach are also discussed by emphasizing the constraints that needs to be adhered while using the proposed UTL approach.