Forecasting Municipal Solid Waste Quantity by Intelligent Models and Their Uncertainty Analysis
Subject Areas : wasteMaryam Abbasi 1 , Malihe Fallah Nezhad 2 , Rooholah Noori 3 , Maryam Mirabi 4
1 - Assistant Professor, Department of Environment, Faculty of Civil Engineering, Water and Environment, Shahid Beheshti University, Tehran, Iran *(Corresponding Author)
2 - PhD, Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran
3 - Assistant Professor, Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran
4 - Assistant Professor, Department of Environment, Faculty of Civil Engineering, Water and Environment, Shahid Beheshti University, Tehran, Iran
Keywords: K-Nearest Neighbors, Artificial Neural Network, Adaptive neuro-fuzzy inference system, Support Vector Machine, Quantitative Waste Generation Forecasting, Uncertainty Analysis,
Abstract :
Background and Objective: The first step in design of municipal waste management systems is complete understanding of waste generation quantity. Forecasting waste generation is one of the most complex engineering problems due to the effect of various and out of control parameters on waste generation. Therefore, it is obvious that it is necessary to develop approaches to a model such complex events. The objective of this study is forecasting waste generation quantity using intelligent models as well as their comparisons and uncertainty analysis.Method: In this study, Mashhad city was selected as a case study and waste generation time series of waste generation in 1380 to 1390 were used for weekly prediction. Intelligent models including artificial neural network, support vector machine, adaptive neuro-fuzzy inference system as well as K-nearest neighbors were used for modelling. After optimizing the models’ parameters, models’ accuracy were compared by statistical indices. Finally, result uncertainty of the models was done by Mont Carlo technique.Findings: Results showed that coefficient of determination (R2) of artificial neural network adaptive neuro-fuzzy inference system, support vector machine, and K-nearest neighbor models were 0.67, 0.69, 0.72 and 0.64 respectively. Uncertainty analysis was also justified the results and demonstrates that support vector machine model had the lowest uncertainty among other models and the lowest sensitivity to input variables.Conclusion: Intelligent models were successfully able to forecast waste quantity and among the studied models, support vector machine was the best predictive model. Moreover, support vector machine produced the results with the lowest uncertainty the other models.
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16. Abbasi M, El Hanandeh A, 2016. Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Management; Vol: 56, pp. 13-22.
17. Abbass HA, 2002. An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine; Vol: 25(3), pp. 265-81.
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23. Ulam s, 1949. The Monte Carlo method. Journal of the American Statistical Association; Vol: 44 (247), pp. 335-41.
24. Vapnik V. Nature of Statistical Learning Theory. Springer. 1995.
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1. Dyson B, Chang N, 2005. Forecasting municipal solidwaste generation in a fast-grow in urban region with system dynamics modeling. Waste Management; Vol: 25, pp. 669-79.
2. Noori R, Abdoli MA, Farokhnia A, Abbasi M, 2009. Results uncertainty of solid waste generation forecasting by hybrid of wavelet transform-ANFIS and wavelet transform-neural network. Expert Syst Appl; Vol: 36(6), pp. 9991-9.
3. عبدلی، م.ع. مدیریت مواد زائد جامد: انتشارات سازمان بازیافت و تبدیل مواد، 1370.
4. Jalili GZM, Noori R, 2008. Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad. Int J Environ Res; Vol: 2(1), pp. 13-22.
5. عبدلی، م.ع.، نوری، ر.، جلیلی، م.، صالحیان، ا.. پیشبینی زباله تولیدی تهران با استفاده از شبکه عصبی مصنوعی و روشهای آماری چندمتغیره. سومین همایش ملی پسماند; تهران، 1386. ص. 72-61.
6. Jang JSR, Gulley, N. Rule extraction using generalized neural networks. Proceedings of the IFSA. World Congress 41991. pp. 82–6.
7. Aqil M, Kita, I., Yano A, Nishiyama S, 2007a. A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. Journal of Hydrology; Vol: 337, pp. 22-34.
8. Aqil M, Kita I, Yano A, Nishiyama S, 2007b. Analysis and prediction of flow from local source in a river basin using a neuro-fuzzy modeling tool. Journal of Hydrology Environmental Management; Vol: 85, pp. 215-23.
9. Wang XX, Chen, S., Lowe, D., Harris, C.J., 2006. Artificial neural networks based on principal component analysis input selection for quantification in overlapped capillary electrophoresis peaks. Chemom Intell Lab Syst Vol: 82, pp. 165-175.
10. Akcayol MA, 2004. Application of adaptive neuro-fuzzy controller for SRM. Advances in Engineering Software; Vol: 35(3-4), pp. 129-37.
11. Chang FJ, Chang, Y.T., 2006. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources; Vol: 29(1), pp. 1-10.
12. Nayak PC, Sudheer, K.P., Rangan, D.M., Ramasastri, K.S., 2004. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology; Vol: 291, pp. 1-17.
13. Chen HW, Chang N-B, 2000. Prediction analysis of solid waste generation based on grey fuzzy dynamic modeling. Resources, Conservation and Recycling; Vol: 29, pp. 1-18.
14. Abbasi M, Abduli MA, Omidvar B, Baghvand AY, Forecasting Municipal Solid waste Generation by Hybrid Support Vector Machine and Partial Least Square Model. Vol: (7), pp. 27-33.
15. Abbasi M, Abduli MA, Omidvar B, Baghvand A, 2014. Results uncertainty of support vector machine and hybrid of wavelet transform-support vector machine models for solid waste generation forecasting. Environmental Progress & Sustainable Energy; Vol: 33(1), pp. 220-8.
16. Abbasi M, El Hanandeh A, 2016. Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Management; Vol: 56, pp. 13-22.
17. Abbass HA, 2002. An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine; Vol: 25(3), pp. 265-81.
18. Benardos PG, Vosniakos GC, 2007. Optimizing feedforward artificial neural network architecture. Engineering Applications of Artificial Intelligence; Vol: 20(3), pp. 365-82.
19. Tang Z, Fishwick PA, 1993. Feedforward neural nets as models for time series forecasting. ORSA journal on computing; Vol: 5(4), pp. 374-85.
20. Buragohain M, Mahanta C, 2008. A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing; Vol: 8(1), pp. 609-25.
21. Chiu SL, 1994. model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems; Vol: 2(3), pp. 267-78.
22. Yakowitz S, 1987. Nearest-neighbour methods for time series analysis. Journal of Time Series Analysis; Vol: 8(2), pp. 235-47.
23. Ulam s, 1949. The Monte Carlo method. Journal of the American Statistical Association; Vol: 44 (247), pp. 335-41.
24. Vapnik V. Nature of Statistical Learning Theory. Springer. 1995.
