Solid waste forecasting using modified ANFIS modeling

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R2). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R2 were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R2 = 0.98. Implications: To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.

Original languageEnglish
Pages (from-to)1229-1238
Number of pages10
JournalJournal of the Air and Waste Management Association
Volume65
Issue number10
DOIs
Publication statusPublished - 3 Oct 2015

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solid waste
modeling
developing world
prediction
economics
waste management

ASJC Scopus subject areas

  • Management, Monitoring, Policy and Law
  • Waste Management and Disposal

Cite this

Solid waste forecasting using modified ANFIS modeling. / Younes, Mohammad K.; Mohd Nopiah, Zulkifli; Ahmad Basri, Noor Ezlin; Basri, Hassan; Abushammala, Mohammed F M; Abdul Maulud, Khairul Nizam.

In: Journal of the Air and Waste Management Association, Vol. 65, No. 10, 03.10.2015, p. 1229-1238.

Research output: Contribution to journalArticle

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