COMBINING ARTIFICIAL NEURAL NETWORK- GENETIC ALGORITHM AND RESPONSE SURFACE METHOD TO PREDICT WASTE GENERATION AND OPTIMIZE COST OF SOLID WASTE COLLECTION AND TRANSPORTATION PROCESS IN LANGKAWI ISLAND, MALAYSIA
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Abstract
Solid waste management is an important component in the environmental management system.
Due to high fluctuations of the amount of the produced waste in langkawi because of tourism in area, the use of
neural networks is appropriate method to predict the amount of the produced waste based on non-linear and complex
relationships between inputs and outputs. Collection and transportation of solid waste devote most part of
municipality budget about 60% in area. The purposes of this research are to develop a model to predict the
generation of solid waste and to reduce the cost of collection and transportation for solid waste management. This
research has used the artificial neural network (ANN) and response surface model (RSM) to predict solid waste
generation and to optimize the cost of waste collection and transportation. The authors believe that this approach
will assist the authorities to determine the amount or quantity of solid waste generated over time. It will also assist
the authorities to optimize cost, design appropriate and cost effective measures to collect and transport solid waste.
This will improve environmental conditions and the cost saved could be used to provide other important services.
We used time-series data with multiple input variables to perform the analyses. The results showed that use of
variety of inputs data decreased the number neurons in hidden layer, which reduced the calculations performance
and point of dimensionality, and increased accuracy in prediction the amount of produced waste; and whereas there
is an increase in solid waste generation from 7825.7 tons (T) in 2009 to 8030.68 T in 2011; cost reduction amount is
10.64%. The methodology or an adapted form of the methodology can be applied to other fields, subject to a study
of the requirements in each place.
Keywords: Solid Waste Management- ANN-GA - RSM- Langkawi Island
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