Proper implementation of landfill siting with the right regulations and constraints can prevent undesirable long-term effects. Different countries have respective guidelines on criteria for new landfill sites. In this article, we perform a comparative study of municipal solid waste landfill siting criteria stated in the policies and guidelines of eight different constitutional bodies from Malaysia, Australia, India, U.S.A., Europe, China and the Middle East, and the World Bank. Subsequently, a geographic information system (GIS) multi-criteria evaluation model was applied to determine new suitable landfill sites using different criterion parameters using a constraint mapping technique and weighted linear combination. Application of Macro Modeler provided in the GIS-IDRISI Andes software helps in building and executing multi-step models. In addition, the analytic hierarchy process technique was included to determine the criterion weight of the decision maker's preferences as part of the weighted linear combination procedure. The differences in spatial results of suitable sites obtained signifies that dissimilarity in guideline specifications and requirements will have an effect on the decision-making process.
Matched MeSH terms: Solid Waste/statistics & numerical data
Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R(2)). The model validation results are as follows: RMSE for training=0.2678, RMSE for testing=3.9860 and R(2)=0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%.
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.
Construction and demolition waste continues to sharply increase in step with the economic growth of less developed countries. Though the construction industry is large, it is composed of small firms with individual waste management practices, often leading to the deleterious environmental outcomes. Quantifying construction and demolition waste generation allows policy makers and stakeholders to understand the true internal and external costs of construction, providing a necessary foundation for waste management planning that may overcome deleterious environmental outcomes and may be both economically and environmentally optimal. This study offers a theoretical method for estimating the construction and demolition project waste generation rate by utilising available data, including waste disposal truck size and number, and waste volume and composition. This method is proposed as a less burdensome and more broadly applicable alternative, in contrast to waste estimation by on-site hand sorting and weighing. The developed method is applied to 11 projects across Malaysia as the case study. This study quantifies waste generation rate and illustrates the construction method in influencing the waste generation rate, estimating that the conventional construction method has a waste generation rate of 9.88 t 100 m(-2), the mixed-construction method has a waste generation rate of 3.29 t 100 m(-2), and demolition projects have a waste generation rate of 104.28 t 100 m(-2).
Matched MeSH terms: Solid Waste/statistics & numerical data