Application of artificial neural network in prediction of municipal solid waste generation (case study: Saqqez City in Kurdistan province)

• المؤلفون الرئيسيون: Shahabi, Himan, Khezri, Saeed, Ahmad, Baharin, Zabihi, Hasan
• التنسيق: مقال
• منشور في: 2012
• الموضوعات: TD Environmental technology. Sanitary engineering
• الوصول للمادة أونلاين: http://eprints.utm.my/id/eprint/46614/; http://dx.doi.org/10.5829/idosi.wasj.2012.20.02.3769

Over the years, the management of municipal solid waste (MSW) has been improved to some extent through installation of various schemes, development of new treatment technologies and implementation of economic instruments. Despite such progress, solid waste problems still impose an increasing pressure on cities and remain one of the major challenges in urban environmental management. Although approximating of waste generation in its management is important, the prediction of its production is a difficult job due to the effect of various factors on it. Artificial intelligence is an exciting and relatively new application of computers. It provides new opportunities for harnessing the scarce and often scattered pieces of valuable knowledge and experience in solid waste management which at present is in the possession of the privileged few. While conventional algorithmic programming replaced much of the sophisticated and repetitive analytical work of the solid waste practitioner, artificial intelligence is poised to take over the no-less important tasks of the ill-structured and lessdeterministic parts of the planning, design and management processes. In this research with application of feed forward artificial neural network, we proposed an appropriate model to predict weight of waste generation in Saqqez city of Iran. For this purpose, we used time series of generated waste of Saqqez which have been arranged weekly, from 2004 to 2007. After performing of the mentioned model, determination coefficient (R ) and mean absolute relative error (MARE) in neural network for test have been achieved to be 2 equal to 0.648 and 2.17% respectively.