Aysun ÖZKAN, Kemal ÖZKAN, Şahin IŞIK, Mufide BANAR

Kentsel katı atık bileşiminin tahmini için farklı eğri uydurma modellerinin değerlendirilmesi

Bu çalışmada, Eskişehir/Türkiye’deki kentsel katı atık (MSW) bileşiminin tahmini için bir metodoloji uygulanmıştır. Bu amaçla, MSW örnekleri toplanmış ve numuneler yiyecek atığı, kâğıt-karton, plastik, metal, cam olarak el ile ayrılmıştır. Bu çalışmada, MSW bileşiminin tahmini için 2B eğri uydurma fonksiyonları kullanılmıştır. Önerilen sistemin performansını yorumlamak için, Kök Ortalama Karesel Hata (RMSE) ve Toplam Karesel Hata (SSE) değerleri değerlendirme ölçütleri olarak seçilmiştir. Sonuçlara göre, polinom eğri uydurma modelinin atık bileşiminin tahmini için daha uygun olduğu belirlenmiştir. Ayrıca, sosyoekonomik yapının atık bileşimi üzerindeki farklı doğrusal olmayan modellerle etkisi gözlenmiştir. Çalışmanın katkısıyla, benzer faktörlere sahip olan diğer şehirlerin MSW kompozisyonunu tahmin etmek mümkün olacaktır.

Anahtar Kelimeler:

Eğri uydurma, Modelleme, Kentsel katı atık, Sosyoekonomik faktörler, Atık bileşimi

Evaluation of different curve fitting models for prediction of municipal solid waste composition

In this study, the methodology was applied for the prediction of municipal solid waste (MSW) composition in the Eskişehir/Turkey. For this purpose, MSW samples were collected, and the samples were separated by food wastes, paper-cardboard, plastics, glass, metals as manually. In the present study, the concept of 2D curve fitting functions was adopted for the forecasting of MSW composition. To comment on the performance of proposed system, the Root Mean Square Error (RMSE) and Sum of Squared Error (SSE) metrics were chosen as statistical residual evaluation metrics. According to results, it is seen that the polynomial curve fitting model is most suitable. Also, the effect of socio-economic structure on the waste composition by different nonlinear models was observed. With contribution of study, it would be possible to forecast the MSW composition of other cities which have similar factors.

Keywords:

Curve fitting, Modeling, Municipal solid waste, Socioeconomic factors, Waste composition,

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[1] Chung SS. “Projecting municipal solid waste: The case of Hong Kong SAR”. Resources Conservation and Recycling, 54(11), 759-768, 2010.
[2] Box GE, Jenkins GM. Time Series Analysis: Forecasting and Control. 5th ed. Holden-Day, USA, Wiley, 2015.
[3] Bridgwater A. “Refuse composition projections and recycling technology”. Resources Conservation and Recycling, 12(3-4), 159-174, 1986.
[4] Mwenda A, Kuznetsov D, Mirau S. “Time series forecasting of solid waste generation in Arusha City-Tanzania”. Mathematical Theory and Modelling, 4(8), 29-39, 2014.
[5] Sodanil M, Chatthong P. “Artificial neural network-based time series analysis forecasting for the amount of solid waste in Bangkok”. Digital Information Management (ICDIM) 2014 Ninth International Conference on. IEEE, Phitsanulok, Thailand, 29 September-1 October 2014.
[6] Navarro-Esbrı J, Diamadopoulos E, Ginestar D. “Time series analysis and forecasting techniques for municipal solid waste management”. Resources Conservation and Recycling, 35(3), 201-214, 2002.
[7] Bach H, Mild A, Natter M, Weber A. “Combining socio-demographic and logistic factors to explain the generation and collection of waste paper”. Resources Conservation and Recycling, 41(1), 65-73, 2004.
[8] Ying L, Weiran L, Jingyi L. “Forecast the output of municipal solid waste in Beijing satellite towns by combination models”. Electric Technology and Civil Engineering (ICETCE) 2011 International Conference on IEEE, Lushan, China, 22-24 April 2011.
[9] Xiang Z, Daoliang L. “Forecasting municipal solid waste generation based on grey fuzzy dynamic modeling”. IASME/WSEAS International Conference on Energy, Environment, Ecosystems and Sustainable Development, Agios Nikolaos, Greece, 24-26 July 2007.
[10] Dai C, Li Y, Huang G. “A two-stage support-vector-regression optimization model for municipal solid waste management-a case study of Beijing, China”. Journal of Environmental Management, 92(12), 3023-3037, 2011.
[11] Intharathirat R, Salam PA, Kumar S, Untong A. “Forecasting of municipal solid waste quantity in a developing country using multivariate grey models”. Waste Management, 39, 3-14, 2015.
[12] Denafas G, Ruzgas T, Martuzevičius D, Shmarin S, Hoffmann M, Mykhaylenko V, Ogorodnik S, Romanov M, Neguliaeva E, Chusov A. “Seasonal variation of municipal solid waste generation and composition in four East European cities”. Resources Conservation and Recycling, 89, 22-30, 2014.
[13] Zade MJG, Noori R. “Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad”. International Journal of Environmental Research, 2(1), 13-22, 2007.
[14] Kumar JS, Subbaiah KV, Rao P. “Prediction of municipal solid waste with RBF net work-a case study of Eluru, AP, India”. International Journal of Innovation, Management and Technology, 2(3), 238-243, 2011.
[15] Patel V, Meka S. “Forecasting of municipal solid waste generation for medium scale towns located in the state of Gujarat, India”. International Journal of Innovative Research in Science, Engineering and Technology, 2(9), 4707-4716, 2013.
[16] Abbasi M, Abduli M, Omidvar B, Baghvand A. “Forecasting municipal solid waste generation by hybrid support vector machine and partial least square model”. International Journal of Environmental Research, 7(1), 27-38, 2012.
[17] Özkan K, Işık Ş, Özkan A, Banar M. “Prediction for the solid waste composition by use of different curve fitting models: A case study”. Innovations in Intelligent SysTems and Applications (INISTA) 2015 International Symposium on IEEE, Madrid, Spain, 2-4 September 2015.
[18] Onel O, Niziolek AM, Hasan MF, Floudas CA. “Municipal solid waste to liquid transportation fuels-Part I: Mathematical modeling of a municipal solid waste gasifier”. Computers and Chemical Engineering, 71(4), 636-647, 2014.
[19] Yang N, Damgaard A, Kjeldsen P, Shao LM, He PJ. “Quantification of regional leachate variance from municipal solid waste landfills in China”. Waste Management, 46, 362-372, 2015.
[20] Chapra SC, Canale RP. Numerical Methods for Engineering. United Kingdom, 7th ed. Tata McGraw Hill Education, 2015.
[21] Chai T, Draxler R. “Root mean square error (RMSE) or mean absolute error (MAE)?-Arguments against avoiding RMSE in the literature”. Geoscientific Model Development, 7(3), 1247-1250, 2014.
[22] Banar M, Özkan A. “Characterization of the municipal solid waste in Eskisehir City, Turkey”. Environmental Engineering Science, 25(8), 1213-1220, 2008.
[23] Özkan A. Utilization of Different Decision Making Techniques for Development of Municipal Solid Waste Management Systems. pHD Thesis, Anadolu University, Eskisehir, Turkey, 2008.
[24] Jalili Ghazi Zadeh M, Noori R “Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad”. International Journal of Environmental Research, 2(1), 13-22, 2008.
[25] Kannangara M, Dua R, Ahmadi L, Bensebaa F "Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches". Waste Management, 74, 3-15, 2018.
[26] Araiza-Aguilar JA, Rojas-Valencia MN, Aguilar-Vera RA "Forecast generation model of municipal solid waste using multiple linear regression". Global Journal of Environmental Science and Management, 6(1), 1-14, 2020.