Prediction of municipal solid waste generation using nonlinear autoregressive network

Mohammad K. Younes, Z. M. Nopiah, N. E.Ahmad Basri, H. Basri, Mohammed F.M. Abushammala, K. N.A. Maulud

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

Original languageEnglish
Pages (from-to)753
Number of pages1
JournalEnvironmental Monitoring and Assessment
Volume187
Issue number12
DOIs
Publication statusPublished - 1 Dec 2015

Fingerprint

Nonlinear networks
Solid Waste
Municipal solid waste
Solid wastes
municipal solid waste
solid waste
Gross Domestic Product
prediction
Developing countries
Mean square error
Developing Countries
developing world
Waste Management
Electricity
Strategic planning
Unemployment
Population Growth
Malaysia
Waste management
unemployment

Keywords

  • ANN forecasting
  • Artificial neural network
  • Solid waste forecasting
  • Solid waste management

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Prediction of municipal solid waste generation using nonlinear autoregressive network. / Younes, Mohammad K.; Nopiah, Z. M.; Basri, N. E.Ahmad; Basri, H.; Abushammala, Mohammed F.M.; Maulud, K. N.A.

In: Environmental Monitoring and Assessment, Vol. 187, No. 12, 01.12.2015, p. 753.

Research output: Contribution to journalArticle

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