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

Elmira Shamshiry, Mazlin Mokhtar, Abdul Mumin Abdulai, Ibrahim Komoo, Nadzri Yahaya

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)118-140
Number of pages23
JournalMalaysian Journal of Science
Volume33
Issue number2
Publication statusPublished - 2014

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genetic algorithm
artificial neural network
solid waste
cost
waste management
methodology
waste collection
method
environmental management
tourism
environmental conditions
time series
prediction

Keywords

  • ANN-GA
  • Langkawi Island
  • RSM
  • Solid waste management

ASJC Scopus subject areas

  • General

Cite this

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title = "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",
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.",
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T1 - 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

AU - Shamshiry, Elmira

AU - Mokhtar, Mazlin

AU - Abdulai, Abdul Mumin

AU - Komoo, Ibrahim

AU - Yahaya, Nadzri

PY - 2014

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N2 - 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.

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