Forecasting Municipal Solid Waste Quantity by Intelligent Models and Their Uncertainty Analysis

Subject Areas : waste

Maryam Abbasi ¹ (Assistant Professor, Department of Environment, Faculty of Civil Engineering, Water and Environment, Shahid Beheshti University, Tehran, Iran *(Corresponding Author))
Malihe Fallah Nezhad ² (PhD, Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran)
Rooholah Noori ³ (Assistant Professor, Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran)
Maryam Mirabi ⁴ (Assistant Professor, Department of Environment, Faculty of Civil Engineering, Water and Environment, Shahid Beheshti University, Tehran, Iran)

Received: 2017-04-23 Accepted : 2017-06-21 Published : 2020-03-20

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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