Detection of Appropriate Model for Nigeria Population Growth Using Root Mean Square Error (RMSE)
Oladimeji Olanrewaju Adedipupo,
Akomolafe Abayomi Ayodele,
Lasisi Taiwo Abideen,
Ojo Thompson Olabode,
Adesina Oluwaseun Ayobami,
Egbedokun Gabriel Olumide
Issue:
Volume 7, Issue 3, September 2022
Pages:
46-51
Received:
7 June 2022
Accepted:
29 June 2022
Published:
17 August 2022
DOI:
10.11648/j.ijssam.20220703.11
Downloads:
Views:
Abstract: Nigeria, a developing nation is experiencing the overwhelming effects of her exponentially ever-increasing population. This paper is aimed at projecting the future population of Nigeria using the exponential and geometric growth models from 1991 and 2006 population censuses of Nigeria. The resultant effects are clearly evident for all stakeholders to see and feel. Researches have been carried out to study, explain and recommend likely solutions to the population growth of Nigeria. The data used in this research paper were extracted from the National Population Commission of Nigeria bulletin which was secondary data. The forecast for Geometric and Exponential models were made using last Nigeria population census 2006 as base population and projection were made from 2012 to 2022 and adopt the Root Mean Square Error (RMSE) to detect which of the methods adopted is better for population projection. RMSE of both geometric and exponential population projections are 1,832,610,950 and 1,930,404,821 respectively. The multiple bar chart was drawn which indicated higher increase in population projection for both geometric and exponential growth models.
Abstract: Nigeria, a developing nation is experiencing the overwhelming effects of her exponentially ever-increasing population. This paper is aimed at projecting the future population of Nigeria using the exponential and geometric growth models from 1991 and 2006 population censuses of Nigeria. The resultant effects are clearly evident for all stakeholders ...
Show More
Stability Analysis and Implementation of a New Fully Explicit Fourth-Stage Fourth-Order Runge-Kutta Method
Esekhaigbe Aigbedion Christopher,
Okodugha Edward
Issue:
Volume 7, Issue 3, September 2022
Pages:
52-59
Received:
26 April 2022
Accepted:
12 May 2022
Published:
31 August 2022
DOI:
10.11648/j.ijssam.20220703.12
Downloads:
Views:
Abstract: The essence of this paper is to analyze the stability and implementation of a newly derived explicit fourth-stage fourth-order Runge-Kutta method. Efforts will be made to carry out a comparative analysis with an existing classical fourth stage fourth order explicit Runge Kutta method. The implementation on initial-value problems revealed that the method compared favorably well with the existing classical fourth stage fourth order explicit Runge Kutta method. The stability analysis revealed that the method is absolute stable, and capable in handling initial value problems in ordinary differential equations. The Taylor series expansion was carried out on the general explicit fourth stage fourth order Runge Kutta scheme, and then, parameters and coefficients were varied with the expansion to generate a set of linear / nonlinear equations which were resolved to generate the method. This approach has shown that when parameters are properly varied, and all equations in the set, whether lineaer / nonlinear, it will definitely give birth to a method that will improve results.
Abstract: The essence of this paper is to analyze the stability and implementation of a newly derived explicit fourth-stage fourth-order Runge-Kutta method. Efforts will be made to carry out a comparative analysis with an existing classical fourth stage fourth order explicit Runge Kutta method. The implementation on initial-value problems revealed that the m...
Show More
Euler’s Method for Solving Logistic Growth Model Using MATLAB
Desta Sodano Sheiso,
Mekashew Ali Mohye
Issue:
Volume 7, Issue 3, September 2022
Pages:
60-65
Received:
2 June 2022
Accepted:
29 August 2022
Published:
16 September 2022
DOI:
10.11648/j.ijssam.20220703.13
Downloads:
Views:
Abstract: This paper introduces Euler’s explicit method for solving the numerical solution of the population growth model, logistic growth model. The Euler method is a first-order method, which means that the local error (error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size. Euler's method is a numerical method that you can use to approximate the solution to an initial value problem with a differential equation that can't be solved using a more traditional method, like the methods we use to solve separable, exact, or linear differential equations. To validate the applicability of the method on the proposed equation, a model example has been solved for different values of parameters. Using this balance law, we can develop the Logistic Model for population growth. For this model, we assume that we add population at a rate proportional to how many are already there. The numerical results in terms of point wise absolute errors presented in tables and graphs show that the present method approximates the exact solution very well. We discuss and explain the solution of logistic growth of population, the kinds of problems that arise in various fields of sciences and engineering. This study aims to solve numerically Euler’s method for solving using the Matlab.
Abstract: This paper introduces Euler’s explicit method for solving the numerical solution of the population growth model, logistic growth model. The Euler method is a first-order method, which means that the local error (error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step s...
Show More
