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.
journal_name
Environ Monit Assessjournal_title
Environmental monitoring and assessmentauthors
Younes MK,Nopiah ZM,Basri NE,Basri H,Abushammala MF,Maulud KNdoi
10.1007/s10661-015-4977-5subject
Has Abstractpub_date
2015-12-01 00:00:00pages
753issue
12eissn
0167-6369issn
1573-2959pii
10.1007/s10661-015-4977-5journal_volume
187pub_type
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