Abstract
Municipal solid waste management is a serious environmental issue concerning developed as well as developing countries worldwide. A successful waste management system requires accurate planning as well as waste generation and collection prediction data with precision. A number of socio-economic factors are responsible for generation of municipal solid waste. In this study the socio-economic factors (such as population, urban population, literate population, and per capita income) have been identified which are responsible for generation of municipal solid waste in Gurugram district (Haryana State, India). In this research work artificial neural network models have been developed (1) to predict the collected municipal solid waste of Gurugram district for five years (2017–2021) and (2) to observe the socio-economic factors effect individually and collectively on waste collection of Gurugram district. The results have been validated by minimum value of mean squared error and maximum value of coefficient of correlation R between observed and predicted municipal solid waste. The artificial neural network model based on individual factor per capita income has shown highest coefficient of correlation R (0.89) (between observed and predicted municipal solid waste) and least value of mean squared error (0.036). The artificial neural network model based on all the factors such as population, urban population, literate population, and per capita income has shown highest coefficient of correlation R (0.915) and least value of mean squared error (0.029). It is observed that expected collected waste by sanitation worker of Municipal Corporation of Gurugram would be approximately 1247096.43 Metric tons within period 2017–2021 and expected generated waste would be approximately 1781566.32 Metric tons within period 2017–2021. It is expected that the proposed research work will be helpful for the authorities of Municipal Corporation of Gurugram.
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References
Adamovic, V.M., Antanasijevic, D.Z., Ristic, M.D., Peric-Grujic, A. A., Pocajt, V.V.: Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis. Environ Sci Pollut Res, 24, 299–311, (2016). https://doi.org/10.1007/s11356-016-7767-x
Asnani, P.U: India Infrastructure Report 2006: 8. Solid Waste Management (2006). http://www.iitk.ac.in/3inetwork/html/reports/IIR2006/Solid_Waste.pdf. Accessed 2 January 2018
Bayar, S., Demir, I., Engin, G.O.: Modeling leaching behavior of solidified wastes using back-propagation neural networks. Ecotoxicology and Environmental Safety 72, 843–850 (2009). https://doi.org/10.1016/j.ecoenv.2007.10.019
Census of India 2011, Haryana, Series 7, Part XII-A, District Census Handbook Gurgaon. Directorate of Census Operations, Haryana http://www.censusindia.gov.in/2011census/dchb/DCHB_A/06/0618_PART_A_DCHB_GURGAON.pdf. (2011). Accessed 2 January 2018
Census of India, 2011. Ministry of Home Affairs, Government of India, New Delhi, India. http://censusindia.gov.in/2011-common/censusdataonline.html (2011). Accessed 2 January 2018
Census of India, 2011. http://www.census2011.co.in/census/district/225-gurgaon.html (2011). Accessed 2 January 2018.
Chau, K.W., Wu, C.L.: A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinformatics 12, 458–473 (2010). https://doi.org/10.2166/hydro.2010.032
Chen, X., Geng, Y., Fujita, T.: An overview of municipal solid waste management in China. Waste Management 30, 716–724 (2010). https://doi.org/10.1016/j.wasman.2009.10.011
Chiemchaisri, C., Juanga, J. P., Visvanathan, C.: Municipal solid waste management in Thailand and disposal emission inventory. Environ Monit Assess 135, 13–20 (2007). https://doi.org/10.1007/s10661-007-9707-1
Denafas, G., Ruzgas, T., Martuzevicius, D., Shmarin, S., Hoffmann, M., Mykhaylenko, V., Ogorodnik, S., Romanov, M., Neguliaeva, E., Chusov, A., Turkadze, T., Bochoidze, I., Ludwig, C.: Seasonal variation of municipal solid waste generation and composition in four East European cities. Resour. Conserv. Recycl. 89, 22–30 (2014).
Economic Profile of NCR, Final Report. http://ncrpb.nic.in/pdf_files/FinalReportofstudyofeconomicprofile_17122015.pdf.(2015). Accessed 06.08.2017
Grossmann, D., Hudson, J. F., Marks, D. H.: Waste generation models for solid waste collection. Journal of the Environmental Engineering Division 100, 1219–1230 (1974)
Jahandideh, S., Jahandideh, S., Asadabadi, E.B., Askarian, M., Movahedi, M.M., Hosseini, S., Jahandideh, M.: The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation. Waste Manage 29, 2874–2879 (2009)
Kalogirou, S.A.: Artificial intelligence for the modeling and control of combustion processes: a review. Progress in Energy and Combustion Science 29, 515–566 (2003).
Liu, C., Wu, X. W.: Factors influencing municipal solid waste generation in China: a multiple statistical analysis study. Waste Management and Research 29, 371–378 (2010). https://doi.org/10.1177/0734242X10380114
Lomeling, D., Kenyi, S.W.: Forecasting solid waste generation in Juba Town, South Sudan using Artificial Neural Networks (ANNs) and Autoregressive Moving Averages (ARMA). Journal of Environment and Waste Management 4, 211–223, (2017)
Louis, G.E.: A historical context of municipal solid waste management in the United States. Waste Management and Research 22, 306–322 (2004).
Medina, M.: The effect of income on municipal solid waste generation rates for countries of varying levels of economic development: A model. Journal of Resource Management and Technology, 24,149–155 (1997)
Meyers, G.D., Mcleod, G., Anbarci, M. A.: An international waste convention: measures for achieving sustainable development. Waste Management and Research 24, 505–513 (2006)
Noori, R., Abdoli, M.A., Farokhnia, A., Abbasi, M.: Results uncertainty of solid waste generation forecasting by hybrid of wavelet transform-ANFIS and wavelet transform-neural network. Expert Syst. Appl. 36, 9991–9999 (2009)
Pfammatter, R., Schertenleib, R.: Nongovernmental refuse collection in low-income urban areas (Lessons learned from selected schemes in Asia, Africa and Latin America. SANDEC Report No.1/96). Water and Sanitation in Developing Countries. Dübendorf, Switzerland: EAWAG/SANDEC (1996)
Population Forecasting. NPTEL IIT Kharagpur web courses. http://nptel.ac.in/courses/105105048/M5L5.pdf. Accessed 2 January 2018.
Rimaityte, I., Ruzgas, T., Denafas, G., Racys, V., Martuzevicius, D.: Application and evaluation of forecasting methods for municipal solid waste generation in an Eastern-European city. Waste Management and Research 30, 89–98 (2012)
Roychowdhury, A.: Gurugram a framework for sustainable development. Centre for Science and Environment, New Delhi, India. http://www.cseindia.org/userfiles/gurugram-a-framework-for-sustainable-development-update.pdf (2017). Accessed 2 January 2018
Saeed, M.O., Hassan, M.N., Mujeebu, M.A.: Assessment of municipal solid waste generation and recyclable materials potential in Kuala Lumpur, Malaysia. Waste Management 29, 2209–2213 (2009)
Sangwan, R.S.: Urbanization in Haryana during post-independence period: trends and patterns. Radix International Journal of Research in Social Science 2, 1–17 (2013)
Shan, C.S.: Projecting municipal solid waste: the case of Hong Kong SAR. Resour. Conserv. Recycl. 54, 759–768 (2010)
Sinha, A., Enayetullah, M. I.: Community based solid waste management: The Asian experience. Dhaka. Waste Concern & USAID (2000)
Suthar, S., Singh, P.: Household solid waste generation and composition in different family size and socio-economic groups: a case study. Sustain. Cities Soc. 14, 56–63 (2015)
Tilak, J.B.G: Educational Planning at Grassroots. In: Nangia, S.B. (eds) APH Publishing Corporation, New Delhi, India (2008)
Wertz, K. L.: Economic factors influencing household’s production of refuse. Journal of Environmental Management and Economics 2, 263–272(1976)
Acknowledgements
The authors are very thankful to Dr. David Lomeling (Department of Agricultural Sciences, College of Natural Resources and Environmental Studies (CNRES), University of Juba, Juba South Sudan) for his valuable and informative suggestions in completion of this study.
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Satija, A., Singh, D., Singh, V.K. (2019). Modeling the Socio-Economic Waste Generation Factors Using Artificial Neural Network: A Case Study of Gurugram (Haryana State, India). In: Singh, V., Gao, D., Fischer, A. (eds) Advances in Mathematical Methods and High Performance Computing. Advances in Mechanics and Mathematics, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-030-02487-1_4
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