PYTHON-BASED POPULATION FORECASTING FOR DELTA STATE: TRAPEZOIDAL AND INTEGRATION METHOD

Authors

  • Edafe John Atajeromavwo
  • Rume Elizabeth Yoro
  • Okiemute Dickson Ofuyekpone
    Department of Materials and Metallurgical Engineering, Delta State University of Science and Technology, Ozoro
  • Daniel Ukpenusiowho
  • Charles Ugbosu

Keywords:

Forecasting of Population, Trapezoidal method of Numerical Analysis of Python, Integration Method, Population Exponential Model and Algorithm

Abstract

The paper focuses on the development of Population Forecasting of Using Population Exponential Model Python with case Study of Delta State Population Census 2006. The focus is to generate manual estimation using the Population Exponential Model; to compare the result estimation with python library. Also, to formulate of an algorithm for area of population of Delta State; visualize the result of prediction of Population with graph and bar chart to see the trend of prediction population trend of Delta State. Population Area under curve of Integration Method and Trapezoidal Method for Delta State for 20 years is obtained. The performance accuracy of 99.9 percent of the population model of Exponential Model of Python with Trapezoidal and Integration Method. Moreover, the calculated sensitivity of the adopted models are in alignment.

Dimensions

Atajeromavwo E.J., A. A. Nwabudike A. A, Osazuwa L.O and Akazue E. C. (2021), “Comparative Analysis of Population Model Using Discrete and Natural Growth Model”: Case Study of Delta State Polytechnic, Ogwashi Uku, Nigeria. Journal of the Nigerian Association of Mathematical Physics, Vol.67 (July to September 2021 Issues) Pp. 23-30.

Awariefe C. & Ogumeyo S.A. (2023). Prediction Accuracy of Nigerian Military Expenditure: Mlr, Arimax, and Ann Models in Statistical and Machine Learning Frameworks. FUDMA Journal of Sciences (FJS), Vol. 7, No. 6, December (Special Issue), 2023, Pp 149 – 156.

Dudnyk O, & Sokolovska Z.M (2023), “Forecasting development trends in the information technology industry using fuzzy logic”, Eastern-European Journal of Enterprise Technologies, 1(13 (121)):74-85, 2023. doi: 10.15587/1729-4061.2023.267906.

Hare J.A, Alexander M. A, Fogarty M.J, Williams M.J., & Scott J.D (2010), “Forecasting the dynamics of a coastal fishery species using a coupled climate–population model. Ecological Applications”, 20(2), 452–464, doi:10.1890/08-1863.1

Jaatinen. K, M. Westerbom, A. Norkko, O. Mustonen, & D. N. Koons, “Detrimental impacts of climate change may be exacerbated by density-dependent population regulation in blue mussels”, Journal of Animal Ecology, 90, 562–573, 2021. https://doi.org/10.1111/1365-2656.13377

Jiayi. L, (2021) “Exploiting Regressive Model for Population Prediction in China”, Highlights in Science, Engineering and Technology, 39:167-175, doi: 10.54097/hset.v39i.6520.

Mazzuco. S, & Keilman. N (2020), “Developments in Demographic Forecasting. The Springer Series on Demographic Methods and Population Analysis”. Doi: 10.1007/978-3-030-42472-5

Miller, D. H., Tietge, J. E., McMaster, M. E., Munkittrick, K. R., Xia, X., Griesmer, D. A. & Ankley, G. T. (2015). Linking mechanistic toxicology to population models in forecasting recovery from chemical stress: A case study from Jackfish Bay, Ontario, Canada. Environmental Toxicology and Chemistry, 34(7), 1623–1633, 2015, doi:10.1002/etc.2972

Munazilla, N & Farikhin, A. (2023). A modified of the generalized fuzzy logical relationship method with high order fuzzy time series based on frequency density partition. Nucleation and Atmospheric Aerosols 2023, doi: 10.1063 he male population outclassed the 2006 to of male.0105677.

Sameer, A.S., Yusuf, M. & Ukafor, U.I. (2022). An application of time independent fourier amplitude model on forecasting the united state population. FUDMA Journal of Sciences, 6(1):54-59, 2022. doi: 10.33003/fjs-2022-0601-881.

Sanderson, W.C. & Scherbov. S. (2007). A new perspective on population aging. Demographic Research, 16, 27–58, 2007. https://doi.org/10.4054/DemRes.2007.16.2.

Sanderson W.C, & Scherbov, S. (2017). A unifying framework for the study of population aging. Vienna Yearbook of Population Research, 2016(14), 7–39, https://doi.org/10.1553/ populationyearbook2016s007.

Sanderson. W.C., & Scherbov, S. (2019). Prospective longevity: A new vision of population aging. Cambridge: Harvard University Press.

Solomon M. K., Babangida Z., Jibril A., Samson I., Luqman Y & Yusuf K. I. (2024). Enhanced Approach For Change Of Course Of Study Using Fuzzy Logic. FUDMA Journal of Sciences (FJS), Vol. 8 No. 2, April, 2024, Pp. 323 – 330

Stefano, P., Damiano V., Francesca B. & Marco M. (2022). The clinical meaning of the area under a receiving operating characteristic curve for the evaluation performance of disease markers. Epidemiol Health 2022; 44:e2022088. Published online 2022 Oct 17. Doi:10.4178/epih.e2022088

Ward, E. J., Holmes, E. E., Thorson, J. T. & B. Collen. (2014). Complexity is costly: a meta-analysis of parametric and non-parametric methods for short-term population forecasting. Oikos, 123(6), 652–661.

Xianfang, T., Xianlong. Ru.C.Z, Yazhao.J. (2022). Research and Simulation of Population Forecast Based on BP Neural Network. 302-305, 2022, doi: 10.1109/EIECT58010.2022.00066.

Zhanarka, B., Ibraeva, G., Bektemyssova, A.R (2023), Fuzzy model for time series forecasting. Scientific journal of Astana IT University, 93-102,doi: 10.37943/13eotu7482.

Zylstra. E.R and Zipkin E.F. (2021). Accounting for sources of uncertainty when forecasting population responses to climate change. Journal of Animal Ecology, vol. 90, no. 3, pp. 558–561, Mar. 2021, doi: 10.1111/1365-2656.13443

Published

30-06-2024

How to Cite

PYTHON-BASED POPULATION FORECASTING FOR DELTA STATE: TRAPEZOIDAL AND INTEGRATION METHOD. (2024). FUDMA JOURNAL OF SCIENCES, 8(3), 112-118. https://doi.org/10.33003/fjs-2024-0803-2372

Issue

Vol. 8 No. 3 (2024): FUDMA Journal of Sciences - Vol. 8 No. 3 (Special Issue)

Section

Research Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

PYTHON-BASED POPULATION FORECASTING FOR DELTA STATE: TRAPEZOIDAL AND INTEGRATION METHOD. (2024). FUDMA JOURNAL OF SCIENCES, 8(3), 112-118. https://doi.org/10.33003/fjs-2024-0803-2372

Most read articles by the same author(s)