title: Prediction of municipal solid waste generation using nonlinear autoregressive network.
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.
authors.affiliation: Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia. mohyoumoh@hotmail.com.
authors.forename: Mohammad K
authors.initials: MK
authors.lastname: Younes
authors.affiliation: Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
authors.forename: Z M
authors.initials: ZM
authors.lastname: Nopiah
authors.affiliation: Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
authors.forename: N E Ahmad
authors.initials: NE
authors.lastname: Basri
authors.affiliation: Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
authors.forename: H
authors.initials: H
authors.lastname: Basri
authors.affiliation: Department of Civil Engineering, Middle East College, Knowledge Oasis Muscat, P.B. No. 79, Al Rusayl, 124, Sultanate of Oman.
authors.forename: Mohammed F M
authors.initials: MF
authors.lastname: Abushammala
authors.affiliation: Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.|Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
authors.forename: K N A
authors.initials: KN
authors.lastname: Maulud
mesh_terms: D000465:Algorithms; D003906:Developing Countries; D004784:Environmental Monitoring / Q000379:methods; D005544:Forecasting; D059066:Gross Domestic Product; D008296:Malaysia; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011158:Population Growth; D062611:Solid Waste / Q000032:analysis / Q000706:statistics & numerical data*; D018505:Waste Management / Q000379:methods*
chemical_list: D062611:Solid Waste
keywords: ANN forecasting; Artificial neural network; Solid waste forecasting; Solid waste management