Evaluation of waste management using clustering algorithm in megacity Istanbul

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Environmental Research and Technology
Volume:3 Issue:3
Evaluation of waste management using clustering algorithm in megacity Istanbul

Evaluation of waste management using clustering algorithm in megacity Istanbul

Pages : 102-112

View : 9 | Download : 12

Publication Date : 2020-09-30

Article Type : Research Paper

Abstract :Industrialization and urbanization are increasing with the effect of globalization worldwide. The waste management problems are rising with the rising population rate, industrialization, and economic developments in the cities, which turned into environmental problems that directly affect human health. This study aims to examine waste management performance in the districts located in the city of Istanbul. To ensure that the districts are clustered in terms of the similarities and differences base on waste management. On this occasion, the authorized unit managers of the districts in the same cluster will be able to establish similar management policies and make joint decisions regarding waste management. In addition, the division of districts into clusters according to the determining indicators can provide information about the locations of waste storage centers. Also, these clusters will form the basis for the optimization constraints required to design appropriate logistics networks. Waste management performance of 39 districts in Istanbul in 2019 was compared by taking into consideration domestic waste, medical waste, population, municipal budget, and mechanical sweeping area. The data were obtained from The Istanbul Metropolitan Municipality insert ignore into journalissuearticles values(IMM); and Turkey Statistical Institute insert ignore into journalissuearticles values(TURKSTAT);. One of the non-hierarchical clustering methods, the K-means clustering method, was applied using IBM SPSS Modeler data mining software to determine the relations between 39 districts. As a result, the waste management performance of the districts was evaluated according to the statistical data, similarities and differences were revealed by using the determined indicators.
Keywords : Waste management, Clustering, K means, Data mining

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