A Predictive Model for the Population Growth of Refugees in Asia: A Multiple Linear Regression Approach

Abstract

Recent data provided by UNHCR indicated that 85% of the world's displaced people are hosted in developing countries, while Asia and the Pacific are homes to about 3.5 million refugees. These hosting countries are often not well equipped with the resources needed to accommodate for the huge surplus in the number of refugees. The ability to predict the population growth of refugees thus enables refugee-hosting countries and NGOs to prepare for refugee migration beforehand, resulting in better infrastructure and opportunities for the refugees expected to enter a country. Advanced analytics could assist experts to chart where refugees are likely to head next, study the signs of future influx, prepare for reroute plans and raise crisis funds. In this paper, we present a regression model that predicts the anticipated number of refugee population in 20 Asian refugee-hosting countries. Using time-series analysis, we establish the pattern of refugee growth for Asian countries with a history of an average population of 2,000 refugees within the last 25 years as well as the last decade. Our model considers several input factors affecting the refugee population growth and predicts the number of refugees between 2017 to 2022 with promising results.

A novel fractional-order discrete grey Gompertz model for analyzing the aging population in Jiangsu Province, China

“…Verhulst differential equation model is also a reliable method to describe population growth behavior (Brilhante et al , 2012). Linear regression model (Sulaiman et al , 2019), neural network model (Xiang and Liu, 2018) and the grey system model (Fan et al , 2019) are also commonly used methods. Various models have been developed based on their unique assumptions, characteristics and conditions (Tu and Chen, 2021).…”

Predicting of elderly population structure and density by a novel grey fractional-order model with theta residual optimization: a case study of Shanghai City, China

“…Population prediction takes the law of population development as the main body to determine the parameters, and the acquisition of relevant data and the selection of prediction algorithm greatly affect the accuracy of prediction results. The existing models used in population prediction research mainly include linear regression model [ 1 ], Malthus model [ 2 ], Logistic model [ 3 ], BP neural network model [ 4 ] and Grey prediction model [ 5 ]. Linear regression model requires population data to change smoothly with obvious linear trend, which is suitable for modeling the relationship between continuous dependent variable and one or more continuous independent variables.…”