Machine Learning of the Reverse Migration Models for Population Prediction: A Review

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Nur Huzeima Mohd Hussain, et. al.

Abstract

Human migration from rural to urban has historically been prominent in the urbanisation process which associated with economic development that leads to city growth.  However, the dwindling supply of natural resources and pressure from the pandemic has threatened economic growth and resulted in changes in human migration; urban to rural.  This anecdotal evidence of reverse migration need to be examined and predict related to challenges and expansion of sustainable development The prediction of human migration; related to population size and growth are important for various policy on strategy, planning and industry. Moreover, predicting population mobility can sense the law of migratory flow in advance, and take effective preventive measures, such as crowd evacuation and epidemic diseases. However, migration predictions are notorious for bearing high error, time consuming, complexity and challenging. Therefore, aligning with IR 4.0, this study adopted a significant way to minimize the prediction errors by using a machine learning approach that can predict data in an intelligent way within a broad dataset. This paper present the investigation of the significant models of machine learning in developing reverse migration prediction.  Thus,  aims of this study is to identify the machine learning models for reverse migration through systematic literature review (SLR) screening.As SLR has recognised to presents a reliable review, this paper measures both, the review from Scopus and Google scholar to determining the signature algorithm for the models.  The findings highlighted the decision tree, random forest and linear regression to be the propose algorithms that pursuit the development of the machine learning models for reverse migration in Malaysia.

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