Modeling the Socio-Economic Waste Generation Factors Using Artificial Neural Network: A Case Study of Gurugram (Haryana State, India)
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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.
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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