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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Increasing population and urbanization pose a huge challenge for municipal authorities to select suitable landfill site to dispose the increasing quantities of solid waste. Wrong landfill siting can result in social, environmental and economic cost. Therefore, suitable approaches are required to select landfill sites because that can enhance sound waste disposal practice in the fast-growing urban areas. The Geographic Information System based Multi-criteria Decision Analysis has been used in this chapter to examine the essentials of an effective site selection. GIS-based MCDA is an intelligent system that transforms spatial data into valuable information which can be used to make critical decisions. The analytical hierarchy process is utilized to assist the prioritization process. In Langkawi, disposal of municipal solid waste into open sites could lead to different adverse impacts on public health and the physical environment. This paper represents simple but effective method to assist landfill site selection efforts in the Langkawi.

The place now used as the landfill in Mashhad (due to the increasing population and expanding city, the place used as the landfill is old) does not follow some of the environmental and socio-political standards and criteria. It makes problems include the location of the landfill site which is close to population centers where there is fertile agricultural land. As a result, there is danger of leachate leakage and pollution of groundwater and land. Therefore the area of landfill needs assessment and investigation during four seasons. With the purpose of evaluation the groundwater chemical properties in landfill of Mashhad, in northeast of Iran, groundwater samples including seven samples from wells and also one sample as evidence were taken. These samples were analyzed and studied in laboratories with ASTM method. The obtained results explained that some quantities of heavy metals elements were more than the permissible levels in water samples near the Mashhad landfill and it may be argued that samples are contaminated in relation to the heavy metals. Some indices that including the metal and heavy metal contamination index explained the water samples from wells contain heavy metal pollution. The methods were applied for water metal index to evaluate the groundwater correlation in study area for metal pollution through measured and comparison by the standard amount.