Evaluation of waste management using clustering algorithm in megacity Istanbul

Yıl 2020, Cilt: 3 Sayı: 3, 102 - 112, 30.09.2020

Didem Güleryüz

https://doi.org/10.35208/ert.764363

Cited By: 6

Öz

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 (IMM) and Turkey Statistical Institute (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.

Anahtar Kelimeler

Waste management, Clustering, K-means, Data mining

Kaynakça

• [1] Agovino M, Ferrara M, Garofalo A. An exploratory analysis on waste management in Italy: A focus on waste disposed in landfill. Land Use Policy 2016;57:669–81. https://doi.org/10.1016/j.landusepol.2016.06.027.
• [2] IMM. IMM open data portal. 2020 n.d. https://data.ibb.gov.tr/ (accessed June 13, 2020).
• [3] Wilson DC. Development drivers for waste management. Waste Manag Res 2007;25:198–207. https://doi.org/10.1177/0734242X07079149.
• [4] Cheng S, Chan CW, Huang GH. Using Multiple Criteria Decision Analysis For Supporting Decisions Of Solid Waste Management. J Environ Sci Heal Part A 2002;37:975–90. https://doi.org/10.1081/ESE-120004517.
• [5] Rathi S. Alternative approaches for better municipal solid waste management in Mumbai, India. Waste Manag 2006;26:1192–200. https://doi.org/10.1016/j.wasman.2005.09.006.
• [6] Benítez SO, Lozano-Olvera G, Morelos RA, Vega CA de. Mathematical modeling to predict residential solid waste generation. Waste Manag 2008;28:7–13. https://doi.org/10.1016/j.wasman.2008.03.020.
• [7] Zhang DQ, Tan SK, Gersberg RM. Municipal solid waste management in China: Status, problems and challenges. J Environ Manage 2010;91:1623–33. https://doi.org/10.1016/j.jenvman.2010.03.012.
• [8] Manaf LA, Samah MAA, Zukki NIM. Municipal solid waste management in Malaysia: Practices and challenges. Waste Manag 2009;29:2902–6. https://doi.org/10.1016/j.wasman.2008.07.015.
• [9] Sharma N, Litoriya R, Sharma A. Application and Analysis of K-Means Algorithms on a Decision Support Framework for Municipal Solid Waste Management, 2020, p. 267–76. https://doi.org/10.1007/978-981-15-3383-9_24.
• [10] Shi W, Zeng W. Application of k-means clustering to environmental risk zoning of the chemical industrial area. Front Environ Sci Eng 2014;8:117–27. https://doi.org/10.1007/s11783-013-0581-5.
• [11] ECER B, AKTAŞ A. Clustering of European Countries in terms of Healthcare Indicators. Int J Comput Exp Sci Eng 2019;5:23–6. https://doi.org/10.22399/ijcesen.416611.
• [12] Dorn T, Nelles M, Flamme S, Jinming C. Waste disposal technology transfer matching requirement clusters for waste disposal facilities in China. Waste Manag 2012;32:2177–84. https://doi.org/10.1016/j.wasman.2012.05.038.
• [13] Otoo D, Amponsah SK, Sebil C. Capacitated clustering and collection of solid waste in kwadaso estate, Kumasi. J Asian Sci Res J 2014;4(8):460–72.
• [14] Lin C, Wu EMY, Lee CN, Kuo SL. Multivariate statistical factor and cluster analyses for selecting food waste optimal recycling methods. Environ Eng Sci 2011;28:349–56. https://doi.org/10.1089/ees.2010.0158.
• [15] Parfitt JP, Lovett AA, Sünnenberg G. A classification of local authority waste collection and recycling strategies in England and Wales. Resour Conserv Recycl 2001;32:239–57. https://doi.org/10.1016/S0921-3449(01)00064-7.
• [16] You H, Ma Z, Tang Y, Wang Y, Yan J, Ni M, et al. Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators. Waste Manag 2017;68:186–97. https://doi.org/10.1016/j.wasman.2017.03.044.
• [17] Niska H, Serkkola A. Data analytics approach to create waste generation profiles for waste management and collection. Waste Manag 2018;77:477–85. https://doi.org/10.1016/j.wasman.2018.04.033.
• [18] Márquez MY, Ojeda S, Hidalgo H. Identification of behavior patterns in household solid waste generation in Mexicali’s city: Study case. Resour Conserv Recycl 2008;52:1299–306. https://doi.org/10.1016/j.resconrec.2008.07.011.
• [19] Song J, Liao Y, He J, Yang J, Xiang B. Analyzing complexity of municipal solid waste stations using approximate entropy and spatial clustering. J Appl Sci Eng 2014;17:185–92. https://doi.org/10.6180/jase.2014.17.2.09.
• [20] Caruso G, Gattone SA. Waste management analysis in developing countries through unsupervised classification of mixed data. Soc Sci 2019;8. https://doi.org/10.3390/socsci8060186.
• [21] TURKSTAT. Turkish Statistical Institute 2020. http://www.tuik.gov.tr/Start.do (accessed June 13, 2020).
• [22] Rahman MM, Ghasemi Y, Suley E, Zhou Y, Wang S, Rogers J. Machine Learning Based Computer Aided Diagnosis of Breast Cancer Utilizing Anthropometric and Clinical Features. Irbm 2020. https://doi.org/10.1016/j.irbm.2020.05.005.
• [23] R Gowda S, R S. Data Mining with Big Data, 2017, p. 246–50. https://doi.org/10.1109/ISCO.2017.7855990.
• [24] Wu X, Kumar V, Ross QJ, Ghosh J, Yang Q, Motoda H, et al. Top 10 algorithms in data mining. vol. 14. 2008. https://doi.org/10.1007/s10115-007-0114-2.
• [25] Berkhin P. A Survey of Clustering Data Mining Techniques. In: Kogan J, Nicholas C, Teboulle M, editors. Group. Multidimens. Data Recent Adv. Clust., Berlin, Heidelberg: Springer Berlin Heidelberg; 2006, p. 25–71. https://doi.org/10.1007/3-540-28349-8_2.
• [26] Likas A, Vlassis N, J. Verbeek J. The global k-means clustering algorithm. Pattern Recognit 2003;36:451–61. https://doi.org/10.1016/S0031-3203(02)00060-2.
• [27] Purnima B, Arvind K. EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN. Int J Comput Appl 2014;105:17–24.