DETERMINING THE MOST RELEVANT INPUT PARAMETER SET BY USING EXTREME LEARNING MACHINE

Öz In this work, Extreme Learning Machine (ELM) algorithm is used to estimate the GDP per capita. The amount of electricity production, from four different sources, is chosen as input parameters. To find out the most relevant input data for a reasonable estimation of GDP, different sources introduced separately to ELM. By following the coefficient of determination of estimation, by trial and error, results are obtained. The residuals are also given to show that model perform well. Renewable energy sources produce the best results in the estimation of GDP.

Anahtar Kelimeler:

Gross Domestic Product Estimation, Extreme Learning Machine, Electricity Production sources

PDF

___

Rakic, G., Milenkovic, D., Vujovic, S., Vujovic, T., Jovic, S., Information system for e-GDP based on computational intelligence approach, Physica A: Statistical Mechanics and its Applications, 513 (2019), 418–423. https://doi.org/ 10.1016/j.physa.2018.09.010

Markovic, D., Petkovic, D., Nikolic, V., Milovancevc, M., Petkovic, B., Soft computing prediction of economic growth based in science and technology factors, Physica A: Statistical Mechanics and its Applications, 465 (2017), 217-220. https://doi.org/10.1016/j.physa.2016.08.034.

Alshehry, A.S., Belloumi, M., Energy consumption, carbon dioxide emis- sions and economic growth: The case of Saudi Arabia, Renewable and Sustainable Energy Reviews, 41 (2015), 237-247. https://doi.org/10.1016/j.rser.2014.08.004.

Mbarek, M.B., Abdelkafi, I., Feki, R., Nonlinear causality between renewable energy, economic growth, and unemployment: Evidence from Tunisia, Journal of Knowledge Economy, 19 (2018), 694-702. https://doi.org/10.1007/s13132-016-0357-9.

Huang, G.B., Zhu, Q.Y., Siew, C.K., Extreme learning machine: Theory and applications, Neurocomputing, 70 (2006), 489-501. https://doi.org/10.1016/j.neucom.2005.12.126.

World Bank Open Data, https://data.worldbank.org.

Kankal, M., Uzlu, E., Neural network approach with teachinglearning- based optimization for modeling and forecasting long-term electric energy demand in Turkey, Neural Computing Application, 28 (2017), 737-747. https://doi.org/10.1007/s00521-016-2409-2.

Cogoljevic, D., Alizamir, M., Piljan, I., Piljan, T., Prljicc, K. , Zimonjic, S., A machine learning approach for predicting the relationship between energy resources and economic development, Physica A: Statistical Mechanics and its Applications, 495 (2018), 211-214. https://doi.org/10.1016/j.physa.2017.12.082.

Milacic, L., Jovic, S., Vuovcc, T., Miljkov, J., Application of artificial neu- ral network with extreme learning machine for economic growth estimation, Physica A: Statistical Mechanics and its Applications, 465 (2017), 285-288. https://doi.org/10.1016/j.physa.2016.08.040.