Forecasting MSW generation using artificial neural network time series model: a study from metropolitan city

Forecasting the quantity of municipal solid waste generation is an essential task for sustainable solid waste management and strategy implementation. The estimation of future waste generation rates can help to motivate for analyzing gaps in existing waste management and better planning strategies. Improper management and unsafe disposal of solid waste create a threat to the environment and human health. Hence, a sound forecasting of solid waste generation is very crucial for planning and management accordingly. Artificial intelligence is an excellent and new application of soft computing which is used as a forecasting tool. The main objective of this study is to apply ANN time series model along with autoregressive technique to forecast the monthly solid waste generation in Kolkata. For the same, data related to the monthly solid waste generation was gathered from 2010 to 2017. Total data of 96 months were divided into three categories, i.e., 70%, 15%, and 15% for training, validation, and testing, respectively. The model was evaluated based on performance value of mean square error, root mean square error, and regression coefficient. The ANN structure of 1-19-1 was considered as optimized model for solid waste forecasting because it has the lowest mean square error and the highest regression coefficient. The applied time series model forecasts that Kolkata will generate about 5205 MT/day municipal solid waste in 2030 which will add more than 1000 MT/day waste with the existing rate of generation. The present study helps in estimating and allocating essential resources that need in future for sound solid waste management and preparing alternative strategies to reach the sustainable goals.

[1]  Osman N. Ucan,et al.  Modeling of SO2 distribution in Istanbul using artificial neural networks , 2005 .

[2]  Babak Omidvar,et al.  Results uncertainty of support vector machine and hybrid of wavelet transform‐support vector machine models for solid waste generation forecasting , 2014 .

[3]  P. Piscitelli,et al.  Illegal Dumping of Toxic Waste and Its Effect on Human Health in Campania, Italy , 2015, International journal of environmental research and public health.

[4]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[5]  Chongxing Liu,et al.  A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China. , 2013, Waste management.

[6]  S. Meka,et al.  Forecasting of Municipal Solid Waste Generation for Medium Scale Towns Located in the State of Gujarat, India , 2013 .

[7]  Douglas J. Lober,et al.  Determinants of Per Capita Municipal Solid Waste Generation in the Southeastern United States , 1995 .

[8]  Zhifeng Liu,et al.  Recycling strategy and a recyclability assessment model based on an artificial neural network , 2002 .

[9]  Babak Omidvar,et al.  Forecasting Municipal Solid waste Generation by Hybrid Support Vector Machine and Partial Least Square Model , 2013 .

[10]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[11]  Azin Khosravi,et al.  The Iranian Vital Horoscope; Appropriate Tool to Collect Health Statistics in Rural Areas , 2009 .

[12]  Mohammad K Younes,et al.  Prediction of municipal solid waste generation using nonlinear autoregressive network , 2015, Environmental Monitoring and Assessment.

[13]  Ashu Jain,et al.  Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques , 2006 .

[14]  Mohammad Ali Abdoli,et al.  Longterm forecasting of solid waste generation by the artificial neural networks , 2012 .

[15]  Rahmah Elfithri,et al.  Modeling of methane oxidation in landfill cover soil using an artificial neural network , 2014, Journal of the Air & Waste Management Association.

[16]  M. Mohsin,et al.  Impacts of Solid Waste Management Practices on Environment and Public Health: A Case of Bahawalpur City, Pakistan , 2017 .

[17]  Seref Naci Engin,et al.  Determination of the relationship between sewage odour and BOD by neural networks , 2005, Environ. Model. Softw..

[18]  Geoffrey Hamer,et al.  Solid waste treatment and disposal: effects on public health and environmental safety. , 2003, Biotechnology advances.

[19]  Gerardo Bocco,et al.  Forecasting Generation of Urban Solid Waste in Developing Countries—A Case Study in Mexico , 2001, Journal of the Air & Waste Management Association.

[20]  Lei Huang,et al.  Estimating future generation of obsolete household appliances in China , 2012, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[21]  Hung-Yee Shu,et al.  Prediction for Energy Content of Taiwan Municipal Solid Waste Using Multilayer Perceptron Neural Networks , 2006, Journal of the Air & Waste Management Association.

[22]  B. Pijanowski,et al.  Using neural networks and GIS to forecast land use changes: a Land Transformation Model , 2002 .

[23]  Rameswar Panda,et al.  Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction , 2009 .

[24]  Maryam Abbasi,et al.  Forecasting municipal solid waste generation using artificial intelligence modelling approaches. , 2016, Waste management.

[25]  Sankar K. Pal,et al.  Multilayer perceptron, fuzzy sets, and classification , 1992, IEEE Trans. Neural Networks.

[26]  Kefa Cen,et al.  SO2 emission characteristics and BP neural networks prediction in MSW/coal co-fired fluidized beds , 2005, Journal of environmental sciences.

[27]  Wei-Zhen Lu,et al.  Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong. , 2004, Environmental research.

[28]  Roohollah Noori,et al.  Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction. , 2010, Journal of environmental management.

[29]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[30]  Subhabrata Ray,et al.  Municipal solid waste management in Kolkata, India - a review. , 2009, Waste management.

[31]  Dipti Singh,et al.  Prediction of municipal solid waste generation for optimum planning and management with artificial neural network—case study: Faridabad City in Haryana State (India) , 2018, Int. J. Syst. Assur. Eng. Manag..

[32]  Yali Xue,et al.  Prediction of Municipal Solid Waste Generation in China by Multiple Linear Regression Method , 2013 .

[33]  S. Rajashekaran,et al.  Neural Networks, Fuzzy Logic and Genetic Algorithms , 2004 .

[34]  Saeed Khezri,et al.  Application of Artificial Neural Network in Prediction of Municipal Solid Waste Generation (Case Study: Saqqez City in Kurdistan Province) , 2012 .

[35]  Changqing Dong,et al.  Predicting the heating value of MSW with a feed forward neural network. , 2003, Waste management.

[36]  Stefan Salhofer,et al.  Modelling municipal solid waste generation: a review. , 2008, Waste management.

[37]  Vivek Kumar Tiwari,et al.  Forecasting the Tropospheric Ozone using Artificial Neural Network Modelling Approach: A Case Study of Megacity Madras, India , 2013 .

[38]  Ferhat Karaca,et al.  NN-LEAP: A neural network-based model for controlling leachate flow-rate in a municipal solid waste landfill site , 2006, Environ. Model. Softw..

[39]  Silvia Curteanu,et al.  Forecasting municipal solid waste generation using prognostic tools and regression analysis. , 2016, Journal of environmental management.

[40]  Hsin-Chung Lu,et al.  Prediction of daily maximum ozone concentrations from meteorological conditions using a two-stage neural network , 2006 .

[41]  Tumpa Hazra,et al.  Solid waste management in Kolkata, India: practices and challenges. , 2009, Waste management.

[42]  Davor Z. Antanasijević,et al.  Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis , 2016, Environmental Science and Pollution Research.

[43]  S. Abul.  Environmental and health impact of solid waste disposal at Mangwaneni dumpsite in Manzini: Swaziland. , 2010 .

[44]  G. Sahoo,et al.  Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu, Hawaii , 2006 .

[45]  A. K. Rigler,et al.  Accelerating the convergence of the back-propagation method , 1988, Biological Cybernetics.

[46]  Gang Xiao,et al.  Gasification characteristics of MSW and an ANN prediction model. , 2009, Waste management.

[47]  J. Navarro-Esbrí,et al.  Time series analysis and forecasting techniques for municipal solid waste management , 2002 .

[48]  Krista L Greene,et al.  A review of national municipal solid waste generation assessments in the USA , 2012, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[49]  F. Schiller,et al.  Hidden flows and waste processing – an analysis of illustrative futures , 2010, Environmental technology.

[50]  Nemanja Stanisavljevic,et al.  Using ANN model to determine future waste characteristics in order to achieve specific waste management targets -case study of Serbia , 2011 .

[51]  R Sha'Ato,et al.  Survey of solid waste generation and composition in a rapidly growing urban area in Central Nigeria. , 2007, Waste management.

[52]  Mohammad K. Younes,et al.  Solid waste forecasting using modified ANFIS modeling , 2015, Journal of the Air & Waste Management Association.

[53]  S. Shrestha,et al.  Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan , 2007, Environ. Model. Softw..