A novel GDP prediction technique based on transfer learning using CO2 emission dataset
A novel GDP prediction technique based on transfer learning using CO2 emission dataset
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DOI：
10.1016/j.apenergy.2019.113476
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发表时间：
2019-11-01
期刊：
影响因子：
11.2
通讯作者：
Muhuri, Pranab K.
中科院分区：
文献类型：
--
作者：
Kumar, Sandeep;Muhuri, Pranab K.
在过去的150年里，大气中的二氧化碳浓度从百万分之280增加到百万分之400。由于温室效应，这导致全球平均气温上升近0.7摄氏度。然而，最繁荣的国家是温室气体（特别是二氧化碳）排放量最高的国家。这表明气体排放量与各州的国内生产总值（GDP）之间存在密切关系。这种关系是高度不稳定和非线性的，因为它依赖于技术进步和不断变化的国内和国际监管政策和关系。为了分析这种巨大的非线性关系，软计算技术已经非常有效，因为它们可以预测多变量参数的紧凑解决方案，而无需对内部系统功能进行任何明确的洞察。本文提出了一种新的基于迁移学习的GDP预测方法，我们称之为“用于GDP预测的领域自适应迁移学习”。在所提出的方法中，不同国家的人均GDP是通过一个基于任何发达或发展中经济体数据训练的模型，使用其二氧化碳排放量来预测的。结果比较考虑三个著名的回归方法，如广义回归神经网络，极端学习机和支持向量回归。然后，所提出的方法被用来可靠地估计失踪的人均国内生产总值的一些饱受战争蹂躏和孤立的国家。
In the last 150 years, CO2 concentration in the atmosphere has increased from 280 parts per million to 400 parts per million. This has caused an increase in the average global temperatures by nearly 0.7 degrees C due to the greenhouse effect. However, the most prosperous states are the highest emitters of greenhouse gases (especially CO2). This indicates a strong relationship between gaseous emissions and the gross domestic product (GDP) of the states. Such a relationship is highly volatile and nonlinear due to its dependence on the technological advancements and constantly changing domestic and international regulatory policies and relations. To analyse such vastly nonlinear relationships, soft computing techniques has been quite effective as they can predict a compact solution for multi-variable parameters without any explicit insight into the internal system functionalities. This paper reports a novel transfer learning based approach for GDP prediction, which we have termed as 'Domain Adapted Transfer Learning for GDP Prediction. In the proposed approach per capita GDP of different nations is predicted using their CO2 emissions via a model trained on the data of any developed or developing economy. Results are comparatively presented considering three well-known regression methods such as Generalized Regression Neural Network, Extreme Learning Machine and Support Vector Regression. Then the proposed approach is used to reliably estimate the missing per capita GDP of some of the war-torn and isolated countries.