ﭘﯿﺶ ﺑﯿﻨﯽ ﮐﻤﯿﺖ ﭘﺴﻤﺎﻧﺪ ﺷﻬﺮي ﺑﺎ اﺳﺘﻔﺎده از ﻣﺪلﻫﺎي ﻫﻮﺷﻤﻨﺪ و آﻧﺎﻟﯿﺰ ﻋﺪم ﻗﻄﻌﯿﺖ آن ﻫﺎ

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

ﭼﮑﯿﺪه زﻣﯿﻨﻪ و ﻫﺪف: اوﻟﯿﻦ ﻗﺪم ﺑﺮاي ﻃﺮاﺣﯽ ﺳﯿﺴﺘﻢ ﻫﺎي ﻣﺪﯾﺮﯾﺖ ﭘﺴﻤﺎﻧﺪ ﺷﻬﺮي، آﮔﺎﻫﯽ ﮐﺎﻣﻞ از ﮐﻤﯿﺖ ﭘﺴﻤﺎﻧﺪ ﺗﻮﻟﯿﺪي ﻣﯽﺑﺎﺷﺪ. ﭘﯿﺶ ﺑﯿﻨﯽ ﮐﻤﯿﺖ ﺗﻮﻟﯿﺪ ﭘﺴﻤﺎﻧﺪ ﺑﻪ دﻟﯿﻞ ﺗﺎﺛﯿﺮ ﻋﻮاﻣﻞ ﻣﺘﻨﻮع و ﺧﺎرج از ﮐﻨﺘﺮل، ﯾﮑﯽ از ﭘﯿﭽﯿﺪه ﺗﺮﯾﻦ ﻣﺴﺎﯾﻞ ﻣﻬﻨﺪﺳﯽ ﻣﯽ ﺑﺎﺷﺪ. ﺑﻪ ﻫﻤﯿﻦ ﺧﺎﻃﺮ، ﻟﺰوم اﺳﺘﻔﺎده از ﻣﺪل ﻫﺎﯾﯽ ﮐﻪ ﻗﺎﺑﻠﯿﺖ ﻣﺪل ﺳﺎزي ﭘﺪﯾﺪه ﻫﺎي ﭘﯿﭽﯿﺪه را دارﻧﺪ، ﺑﻪﺧﻮﺑﯽ روﺷﻦ ﻣﯽ ﺑﺎﺷﺪ. ﻫﺪف از اﯾﻦ ﻣﻄﺎﻟﻌﻪ، ﭘﯿﺶﺑﯿﻨﯽ ﮐﻤﯿﺖ ﭘﺴﻤﺎﻧﺪ ﺑﺎ اﺳﺘﻔﺎده از ﻣﺪلﻫﺎي ﻫﻮﺷﻤﻨﺪ، ﻣﻘﺎﯾﺴﻪ ﻋﻤﻠﮑﺮد و آﻧﺎﻟﯿﺰ ﻋﺪم ﻗﻄﻌﯿﺖ آنﻫﺎ ﻣﯽﺑﺎﺷﺪ روش ﺑﺮرﺳﯽ: در اﯾﻦ ﻣﻄﺎﻟﻌﻪ، ﺷﻬﺮ ﻣﺸﻬﺪ ﺑﻪ ﻋﻨﻮان ﻣﻄﺎﻟﻌﻪ ﻣﻮردي اﻧﺘﺨﺎب ﺷﺪ و از ﺳﺮي زﻣﺎﻧﯽ ﺗﻮﻟﯿﺪ ﭘﺴﻤﺎﻧﺪ در ﻓﺎﺻﻠﻪ زﻣﺎﻧﯽ ﺳﺎل ﻫﺎي 1380 ﺗﺎ 1390 ﺑﺮاي ﭘﯿﺶ ﺑﯿﻨﯽ ﻫﻔﺘﮕﯽ اﺳﺘﻔﺎده ﮔﺮدﯾﺪ. ﺟﻬﺖ ﻣﺪلﺳﺎزي از ﻣﺪلﻫﺎي ﻫﻮﺷﻤﻨﺪ ﺷﺒﮑﻪ ﻋﺼﺒﯽ، ﻣﺎﺷﯿﻦ ﺑﺮدار ﭘﺸﺘﯿﺒﺎن، ﺳﯿﺴﺘﻢ اﺳﺘﻨﺘﺎﺟﯽ ﺗﻄﺒﯿﻘﯽ ﻧﺮوﻓﺎزي و ﮐﺎ ﻧﺰدﯾﮏﺗﺮﯾﻦ ﻫﻤﺴﺎﯾﻪ اﺳﺘﻔﺎده ﮔﺮدﯾﺪ. ﭘﺲ از ﺑﻬﯿﻨﻪﺳﺎزي ﭘﺎراﻣﺘﺮﻫﺎي ﻫﺮ ﻣﺪل، ﺑﺎ اﺳﺘﻔﺎده از از ﺷﺎﺧﺺﻫﺎي آﻣﺎري، ﻋﻤﻠﮑﺮد ﻣﺪلﻫﺎ ﻣﻮرد ﻣﻘﺎﯾﺴﻪ ﻗﺮار ﮔﺮﻓﺖ. در ﻧﻬﺎﯾﺖ، آﻧﺎﻟﯿﺰ ﻋﺪم ﻗﻄﻌﯿﺖ ﻧﺘﺎﯾﺞ ﻣﺪلﺳﺎزي ﺑﺎ ﮐﻤﮏ روش ﻣﻮﻧﺖ ﮐﺎرﻟﻮ اﻧﺠﺎم ﮔﺮﻓﺖ. ﯾﺎﻓﺘﻪﻫﺎ: ﻧﺘﺎﯾﺞ ﻧﺸﺎن داد ﮐﻪ ﺿﺮﯾﺐ اﻃﻤﯿﻨﺎن )R2( ﻣﺪل ﻫﺎي ﺷﺒﮑﻪ ﻋﺼﺒﯽ، ﺳﯿﺴﺘﻢ اﺳﺘﻨﺘﺎﺟﯽ ﺗﻄﺒﯿﻘﯽ ﻧﺮوﻓﺎزي، ﻣﺎﺷﯿﻦ ﺑﺮدار ﭘﺸﺘﯿﺒﺎن و ﮐﺎ ﻧﺰدﯾﮏﺗﺮﯾﻦ ﻫﻤﺴﺎﯾﻪ ﺑﻪ ﺗﺮﺗﯿﺐ 0/72 ،0/69 ،0/67 و 0/64 ﻣﯽ ﺑﺎﺷﺪ. آﻧﺎﻟﯿﺰ ﻋﺪم ﻗﻄﻌﯿﺖ ﻧﯿﺰ ﻧﺘﺎﯾﺞ اﯾﻦ ﻣﻘﺎﯾﺴﻪ را ﺗﺎﯾﯿﺪ ﻧﻤﻮد و ﻧﺸﺎن داد ﻣﺪل ﻣﺎﺷﯿﻦ ﺑﺮدار ﭘﺸﺘﯿﺒﺎن در ﺑﯿﻦ ﺳﺎﯾﺮ ﻣﺪل ﻫﺎ، ﻋﺪم ﻗﻄﻌﯿﺖ ﮐﻤ ﺘﺮي داﺷﺘﻪ و ﻧﺴﺒﺖ ﺑﻪ داده ﻫﺎي ورودي ﮐﻤ ﺘﺮﯾﻦ ﺣﺴﺎﺳﯿﺖ را دارد. ﺑﺤﺚ و ﻧﺘﯿﺠﻪ ﮔﯿﺮي: ﻣﺪل ﻫﺎي ﻫﻮﺷﻤﻨﺪ از ﺗﻮاﻧﺎﯾﯽ رﺿﺎﯾﺖ ﺑﺨﺸﯽ ﺑﺮاي ﭘﯿﺶﺑﯿﻨﯽ ﮐﻤﯽ ﭘﺴﻤﺎﻧﺪ ﺑﺮﺧﻮردارﻧﺪ و در ﺑﯿﻦ ﻣﺪل ﻫﺎي ﻫﻮﺷﻤﻨﺪ ﻣﻮرد ﻣﻄﺎﻟﻌﻪ، ﻣﺪل ﻣﺎﺷﯿﻦ ﺑﺮدار ﭘﺸﺘﯿﺒﺎن ﺑﻬﺘﺮﯾﻦ ﻧﺘﺎﯾﺞ را از ﺧﻮد ﻧﺸﺎن داد. ﻫﻤﭽﻨﯿﻦ، ﻋﺪم ﻗﻄﻌﯿﺖ ﻧﺘﺎﯾﺞ ﻣﺪل ﻣﺎﺷﯿﻦ ﺑﺮدار ﭘﺸﺘﯿﺒﺎن در ﺑﯿﻦ ﺳﺎﯾﺮ ﻣﺪل ﻫﺎ، ﻋﺪم ﻗﻄﻌﯿﺖ ﮐﻢﺗﺮي ﺑﺮﺧﻮردار ﺑﻮد.

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.