IJBTT

Population Forecasting System Using Machine Learning Algorithm
	© 2020 by IJCTT Journal
	Volume-68 Issue-12
	Year of Publication : 2020
	Authors : Dr. Nwanze Ashioba, Ndubuife Nonso Daniel
	DOI :  10.14445/22312803/IJCTT-V68I12P109

How to Cite?

Dr. Nwanze Ashioba, Ndubuife Nonso Daniel, "Population Forecasting System Using Machine Learning Algorithm," International Journal of Computer Trends and Technology, vol. 68, no. 12, pp. 40-43, 2020. Crossref, 10.14445/22312803/IJCTT-V68I12P109

Abstract
In every nation, there has been a platform to ascertain its citizens` exact number, population growth rate and make plans and decisions using the population information. The government spent a lot of resources on census enumeration. Unfortunately, in Nigeria, census enumeration has been embroiled in controversies. To overcome these problems, the existing systems face, the researchers have designed and developed a population forecasting system using a machine learning algorithm. The researchers adopted the Object-Oriented analysis and design methodology in developing the Population Forecasting System. The results have shown that Linear Regression Model has lower percentage error margins (between 0.76% and 1.09%) than the Average Projection Model and the Nature Fund Growth model with a percentage error margin between 4.73% - 1.43% and 0.9% - 1.89%, respectively.

Keywords
Population, Population Forecasting, Population Estimation, Machine Learning Algorithm.

Reference
[1] N. Keilman, D. Pham, and A. Hetland. Why population forecasts should be probabilistic – illustrated by the case of Norway. Demographic Research, 6, (2002) 409–453.
[2] S.O. Fulurunso, A.T. Akinwale, O.E. Asiribo, and T. A. Adeyemo. Population Prediction Using Artificial Neural Network. African Journal of Mathematical Computer Science Reserve, 3(2010) 155-162.
[3] I.An Ilegbinosa, O.L Moses, and Praise. Population and its Impact on the Level of Unemployment in the least Developed Countries; An Appraisal of the Nigerian Economy. Arts and Social Sciences Journal. 6, (2014) 277-289.
[4] B. Lovindraj, M. Boyjoo, D, Bundhoo, O. Ramgoolam, and I. Seburn (2015) Population Forecasting Using Geometric Increase Method. https://slideplayer.com/slide/5903511 Retrieved 21st August 2020.
[5] C. Lin, W. Liu, Y. Tian and S. Wang Fine-Scale Population Estimation by 3D Resolution of Urban Residential Buildings. Sensors (Basel)16(10) (2016) 1755.
[6] H. Aidi, C. Emecheta, and I.M. Ngwudiobi (2016). Population Dynamic and Economic Growth in Nigeria; Journal of Economic and Sustainable Development 7(15) 1-23.
[7] C. Lin, W. Liu, Y. Tian and S. Wang Fine-Scale Population Estimation by 3D Resolution of Urban Residential Buildings. Sensors (Basel)16(10) (2016) 1755.
[8] N.I John. The Population Question in Nigeria. Asian Research Journal of Mathematics, 5 (2017) 1-10.
[9] O. Odunayo, E. Oduntan, and O. Olawumi, (2018). Using predictive Machine Learning Regression Model to Predict the Population of Nigeria. Anale. Seria Informatica.. XVI Fasc. 2-2018. Annals. Computer Science Series. 16th Tome 2nd Fasc.-2018.
[10] P. Brijesh, Gunjan Singh and Sandeep Singh. Population Projection using Mathematical Curves: Will Indian Population Stabilize by 2050. Demography India, (Special Issue) (2018) 25-35.
[11] V. S. Fatih, T.T. Ahmet and C. Ferhan Machine Learning Algorithm to Forecast the Population: Turkey Example. International Engineering and Technology Management Summit 2019 – ETMS. www.researchgate.net/publication/33714439
[12] R. Monorom, J. L. Soliman, N. S. A. Lopez, A. M. Fillone, & J. B. M. Biona Development of Small Area Population Estimation Models for a Developing, Densely Populated Metropolitan Area and its Applications: A case study of Metro Manila. Engineering and Applied Science Research, 47(2), (2020) 206-215.
[13] Z. Jabrayilova Development of Intelligent Demographic Forecasting System. Eastern-European Journal of Enterprise Technologies. 101 (2019) 18-25.
[14] O.M. Olayiwola, G. O. Lawal, A. A. Amalare, S. A. Agboluaje and J. O. Fantola, Modeling Nigeria Population Growth Rate. Journal of Advances in Mathematics (2015) 10 (6).