• ISSN: 2010-0248 (Print)
    • Abbreviated Title: Int. J. Innov.  Manag. Technol.
    • Frequency: Quarterly
    • DOI: 10.18178/IJIMT
    • Editor-in-Chief: Prof. Jin Wang
    • Managing Editor: Ms. Nancy Y. Liu
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HOME > Archive > 2011 > Volume 2 Number 3 (Jun. 2011) >
IJIMT 2011 Vol.2(3): 238-243  ISSN: 2010-0248
DOI: 10.7763/IJIMT.2011.V2.138

Prediction of Municipal Solid Waste with RBF Net Work- A Case Study of Eluru, A.P, India

J. Sudhir Kumar, K. Venkata Subbaiah, and P. V. V.Prasada Rao

Abstract—This paper is an attempt made to estimate the quantity of Municipal solid waste that can be generated inthe ELURU city, A.P, INDIA from 2010 to 2026. The prediction of municipal solid waste generation plays an important role in solid waste management. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast growing regions is quite challenging. In addition to population growth and migration, underlying economic development, household size, employment changes, and the impact of waste recycling would influence the solid waste generation interactively. The development of a reliable model for predicting the aggregate impact of economic trend, population changes, and recycling impact on solid waste generation would be a useful advance in the practice of solid waste management. The four input variables considered in the ANN model to predict MSW in the study area are Population of MCE, MSW generated at MCE, Percentage of urban population of the nation and GDP per capita of the nation. A radial basis function network is an artificial neural network that uses radial basis functions as activation functions. It is a linear combination of radial basis functions. They are used in function approximation, time series prediction, and control. In the absence of adequate past data on waste generation rates, it is extremely difficult to decide upon the methodology to make any kind of projections for the future. Hardly any primary survey studies have been made in the study area, which indicate the actual waste quantam generated. As a result, except for data points from 1961 to 2001 population based on census, Municipal solid waste generated at MCE from 1961 to 2001 and 2009 based on the data collected from the MCE, urban population percentage in the total population as per census for the above period based on national scenario and the year wise data of GDP per capitaon the national scenario, there is no data available on the basis. The estimates of waste quantum for period from 2010 to 2026, shows that if the growth of population, growth of percentage increase in per capita waste generation, growth of urban population and future estimate of GDP per capita are considered as per the nation projections, the MSW in the study area can be expected by radial basis function ANN model using MAT Lab Version 7.8.0.347 as around 39,670MT per year in MCE by 2026.

Index Terms—Municipal Solid Waste (MSW), Artificial Neural Net Works (ANN), Radial Basis Function (RBF),Prediction.

Mr.Jasti Sudhir Kumar, is with the Department of Mechanical Engineering, Sir C.R.Reddy College of Engg., Eluru, West Godavari District, Andhra Pradesh,INDIA-534007
K,Venkata Subbiah, is with the Department of Mechanical Engineering,A.U.College of Engg (A)., Andhra University, Visakhapatnam, Andhra Pradesh, INDIA
P.V.V.Prasada Rao, is with the Department of Environmental Sciences,Andhra University, Visakhapatnam, Andhra Pradesh, INDIA

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Cite: J. Sudhir Kumar, K. Venkata Subbaiah, and P. V. V.Prasada Rao, "Prediction of Municipal Solid Waste with RBF Net Work- A Case Study of Eluru, A.P, India," International Journal of Innovation, Management and Technology vol. 2, no. 3, pp. 238-243, 2011.

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