Performance Assessment of Computational Intelligence Techniques in Solid Waste Generation Forecasting (Case Study)

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Minousepehr Mohammad; ALIZADEH MOHAMMAD REZA; Talebbeydokhti Nasser

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Journal Paper

Paper Information

Journal: JOURNAL OF CIVIL AND ENVIRONMENTAL ENGINEERING (UNIVERSITY OF TABRIZ) Year:2018 | Volume:48 | Issue:1 (90) Page(s): 67-75

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Information Journal Paper

Title

Performance Assessment of Computational Intelligence Techniques in Solid Waste Generation Forecasting (Case Study)

Author(s)

Minousepehr Mohammad | ALIZADEH MOHAMMAD REZA | Talebbeydokhti Nasser | Issue Writer Certificate

Keywords

Solid waste Generation forecasting

Multi-layer perceptron

Support vector machines

M5P model trees

Abstract

1. Introduction: Knowing the quantity of generated solid waste play a very significant role in solid waste management programs in a region. Due to lack of measured data as well as unavoidable errors in measurements, assessment of volume of generated solid waste is always challenging. Also, field measurement and continues monitoring of the volume of solid waste is usually costly, difficult and time-consuming. Accurate prediction of solid waste generation can be regarded as a key factor in future solid waste management system planning. Conventional forecasting methods in Solid waste Generation forecasting frequently use the demographic and socioeconomic factors in a per capita basis. In most cases, insufficient funds, the limited measuring equipment, lack of appropriate management systems and due to the lack of recorded data for the volume of generated solid waste cause many problems in integrated solid waste systems management (Dyson and Chang, 2005). In this study, three computational intelligence techniques including M5P model trees, Support vector machines (SVM) and Multi-layer perceptron (MLP) artificial neural network are utilized to predict solid waste generation in Hormozgan Province, Iran. After a sensitivity analysis, four more influential factors including elevation, population, urban development index (measures the level of development in cities based on infrastructure, the municipality established year, the metropolitan area, population, city product and income, health and education) and the frequency of garbage collection were considered in developing models. The performance of proposed models in Solid waste Generation forecasting are assessed via different error evaluation indices and finally the results are compared...

Cites

The future status of solid waste generation in Tehran metropolis with regression analysis method based on population

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APA: Copy

Minousepehr, Mohammad, ALIZADEH, MOHAMMAD REZA, & Talebbeydokhti, Nasser. (2018). Performance Assessment of Computational Intelligence Techniques in Solid Waste Generation Forecasting (Case Study). JOURNAL OF CIVIL AND ENVIRONMENTAL ENGINEERING (UNIVERSITY OF TABRIZ), 48(1 (90) ), 67-75. SID. https://sid.ir/paper/340552/en

Vancouver: Copy

Minousepehr Mohammad, ALIZADEH MOHAMMAD REZA, Talebbeydokhti Nasser. Performance Assessment of Computational Intelligence Techniques in Solid Waste Generation Forecasting (Case Study). JOURNAL OF CIVIL AND ENVIRONMENTAL ENGINEERING (UNIVERSITY OF TABRIZ)[Internet]. 2018;48(1 (90) ):67-75. Available from: https://sid.ir/paper/340552/en

IEEE: Copy

Mohammad Minousepehr, MOHAMMAD REZA ALIZADEH, and Nasser Talebbeydokhti, “Performance Assessment of Computational Intelligence Techniques in Solid Waste Generation Forecasting (Case Study),” JOURNAL OF CIVIL AND ENVIRONMENTAL ENGINEERING (UNIVERSITY OF TABRIZ), vol. 48, no. 1 (90) , pp. 67–75, 2018, [Online]. Available: https://sid.ir/paper/340552/en

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