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

  • 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.

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Published
2024-06-30
How to Cite
AtajeromavwoE. J., YoroR. E., OfuyekponeO. D., UkpenusiowhoD., & UgbosuC. (2024). PYTHON-BASED POPULATION FORECASTING FOR DELTA STATE: TRAPEZOIDAL AND INTEGRATION METHOD. FUDMA JOURNAL OF SCIENCES, 8(3), 112 - 118. https://doi.org/10.33003/fjs-2024-0803-2372

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