Computer-based fuzzy logic for forecasting the population census of Edo State, Nigeria

Author:

Atajeromavwo E. J.1,Daniel Ukpenusio1,Duke Ogorodi1,Ekruyota O. G.1,Yoro Rume2

Affiliation:

1. Delta State University of Science and Technology

2. Dennis Osadebe University

Abstract

Although the National Population Commission's forecasting efforts have become more accurate over the years, this work aims to use fuzzy logic to predict population growth in a quicker, simpler, more accurate, and more effective way. To accomplish this goal, various data collection technologies were employed to compile data from secondary sources, including the National Population Commission of Nigeria. A thorough literature evaluation on population forecasts and censuses has already been published. Implementing a proactive population forecast was built with a stated goal in mind. Python 3 was chosen as a reliable programming language for ODEINT (Ordinary Differential Equation Integration) for Natural Growth Model and Fuzzy Time Series library functions. Due to a performance accuracy of 99.6%, the model created for population census forecasting projects the future population at a dependable time.

Publisher

i-manager Publications

Reference14 articles.

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2. Agog, N. S., Bako, S. S., Bamanga, M. A., Peter, M., & Byeli, U. (2020). On logistic growth model for forecasting Nigeria's population. Science World Journal, 15(4), 112-115.

3. How Many Nigerians? An Analysis of Nigeria's Census Problems, 1901–63

4. Amune, E.O., & Mgbeafulike, I. J. (2019). The Design and Implementation of Secured Population Forecasting System for Census Management (Master thesis, Nnandi Azikwe University, Akwa, Anambra State, Nigeria).

5. Marco P. (2020). Time Series Forecasting with SARIMA in Python Hands-on tutorial on time series modeling with SARIMA using Python. Retrieved from https://towardsdatascience.com/time-series-forecasting-with-sarima-inpython-cda5b793977b

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