Optimal Policy-Making for Municipal Waste Management Based on Predictive Model Optimization

Shabir Ahmad, Imran, Naeem Iqbal, Faisal Jamil, Do Hyeun Kim

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


Waste management is an issue of grave concern in the modern urban scenario with the exponentially rising population. Over the past few decades the Korean government has established several policies to tackle challenges pertaining to solid waste management. To devise a policy it is necessary to investigate the trends and behaviour of people towards waste disposal. This article fills this gap by proposing a systematic approach of analyzing the solid waste data based on waste profiles of residential grids in Jeju Island. The solid waste data along with predictive analytics help the municipality to devise customized policies for different residential grids. We define policy in terms of the number of waste collection human resources cost waste carrier's vehicle cost and fuel cost. Thus the paper aims to suggest the number of resources which lead to a minimum cost and also ensure a certain level of hygiene in the area. The analysis is carried out on the solid waste dataset of 2017-2019 generated from different residential grids. The analysis coupled with prediction algorithms allows the policy-makers to generate a waste profile specific to a residential grid. The optimization algorithm then proposes minimum resources which are enough to ensure hygiene standard of the area based on the waste amount and frequency inside the grid. The results of different areas are illustrated and the minimum cost is suggested which enables the policy-makers to not only allocate optimal resources but also helps in ensuring a green and clean environment.

Original languageEnglish
Article number9284435
Pages (from-to)218458-218469
Number of pages12
JournalIEEE Access
Early online date7 Dec 2020
Publication statusPublished - 16 Dec 2020
Externally publishedYes


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