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

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 wasteand whereas there is an increase in solid waste generation from 7825.7 tons (T) in 2009 to 8030.68 T in 2011cost 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