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Prediction of municipal solid waste generation for optimum planning and management with artificial neural network—case study: Faridabad City in Haryana State (India)

Author

Listed:
  • Dipti Singh

    (Gautam Buddha University)

  • Ajay Satija

    (Inderprastha Engineering College)

Abstract

Accurate prediction of municipal solid waste generation has an important role in future planning and waste management system. The characteristics of the generated solid waste are different at different places (municipality to municipality or country to country). The accurate prediction of municipal solid waste (MSW) generation becomes a crucial task in modern era. Its prediction requires accurate MSW data. The aim of the present study is to design the time series model for predicting monthly based municipal solid waste generation in Faridabad city of Haryana State (India) using artificial neural network (ANN) time series autoregressive approach. The collected municipal solid waste observations have been arranged monthly from 2010 to 2014. The 60 months data set is divided into 42 training data sets, 9 testing data sets and 9 validating data sets. Various structures of ANN have been investigated by changing the number of hidden layer neurons. Finally best optimized structure of neural network is found. The proposed model is validated by the minimum value of performance parameters such as mean square error 0.0003714, root mean square error 0.01927 and the high value of the coefficient of regression 0.8385. On the bases of these performance parameters it is concluded that the proposed ANN model gives accurate predictive results.

Suggested Citation

  • Dipti Singh & Ajay Satija, 2018. "Prediction of municipal solid waste generation for optimum planning and management with artificial neural network—case study: Faridabad City in Haryana State (India)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 91-97, February.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-016-0484-5
    DOI: 10.1007/s13198-016-0484-5
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    Cited by:

    1. Johanna Karina Solano Meza & David Orjuela Yepes & Javier Rodrigo-Ilarri & María-Elena Rodrigo-Clavero, 2023. "Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    2. Vladimir Simic & Ali Ebadi Torkayesh & Abtin Ijadi Maghsoodi, 2023. "Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest recursive feature elimination algorithm," Annals of Operations Research, Springer, vol. 328(1), pages 1105-1150, September.

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