Volume 12, Issue 3 (12-2019)                   ijhe 2019, 12(3): 489-500 | Back to browse issues page

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1- Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2- Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran , shahinrafiee@ut.ac.ir
Abstract:   (1412 Views)
Background and Objective: Knowledge about the quantity of municipal solid waste (MSW) generation plays a key role in formulating policies of waste management. So far, different methods have been applied to estimate the quantity of waste generation. In this study, eight specific forms of mathematical functions were evaluated to predict waste generation by the regression analysis method based on population.
Materials and Methods: The significance test of each model and the existence necessity of predictor parameters were performed using the F- and t-statistic, respectively. The statistical indicators of determination coefficient ( ), adjusted determination coefficient ( ), root mean square error (RMSE), mean bias error (MBE) and mean percentage error (MPE) were used for model’s goodness of fit. The predicted determination coefficient ( ) was calculated to assess the predictive ability of models by method of Leave-one-out cross validation.
Results: The results showed that polynomial models of second order and more are not significant (at 0.01 level) despite good accuracy and are not suitable for long-term prediction. Linear, power and exponential models are best with equal to 0.942, 0.932 and 0.936 and  equal to 0.904, 0.893 and 0.898 respectively. However, the uncertainty was greater in the exponential model.
Conclusion: The status of waste generation was investigated in four scenarios based on growth rate of population (increasing, fixing and decreasing births) at Tehran metropolis in 2021-2051. In all scenarios, annual generation and per capita of waste are increased to 2051. The daily waste generation will increase to 12317 ton in 2051.
 
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Type of Study: Research | Subject: General
Received: 2019/04/29 | Accepted: 2019/08/5 | Published: 2019/12/21