Forecasting the Sri Lankan Population with the Gompertz and Verhulst Logistic Growth Models

Abstract

Population growth is one of the major problems in the world. The population forecast is predicted by each and every country from an early age. Further, the population growth and the size of the country are major factors which affect its economy and policies. Hence, it has emphasized the importance of predicting the future population of Sri Lanka. Therefore, this study mainly focuses on proposing population growth models to predict the population growth of Sri Lanka. The Verhulst logistic growth model and the Gompertz growth model are used to predict the population of Sri Lanka using population data from the census population and the mid-year population, the birth rate and the death rate obtained from the Department of Registrar General of Sri Lanka from 1990 to 2018. The explicit solutions for each model are derived by using mathematical techniques of differentiation and integration. The carrying capacity; i.e., the maximum number of the population an environment can support, indefinitely was calculated using the two models. The percentages of Root Means Square Error (RMSE), Mean Absolute Percentage Deviation (MAPD) and Symmetric Mean Absolute Percentage Error (SMAPE) were calculated to measure the prediction accuracy between the actual data and the predicted data of the proposed models. Then the Sri Lankan population from 2019 to 2048 was predicted using the proposed models. Results show the prediction accuracy of Gompertz model to be higher compared to the one of Verhulst logistic growth model. This study provides a deep insight into the population prediction in Sri Lanka, a country with limited resources, and in an area teeming with conflicts.