Forecasting Solid Waste Generation Rates

Design of efficient solid waste management (SWM) systems requires accurate estimates of waste generation rates. Waste generation rates can be measured by direct sampling and several methods like database mining and sample surveys have been used in different locations. Another approach is to use different types of models to predict or forecast waste generation rates based on knowledge of impacting factors. These models include statistical models like multiple linear regression, econometric models, system dynamics methods, time-series analysis, factor analysis and Geographical Information Systems (GIS)-based methods. More non-conventional methods like artificial neural networks (ANN), fuzzy logic (FL) and support vector machine (SVM) methods are becoming popular. A brief literature review of these methods used by researchers for forecasting solid waste generation rates is provided in this chapter.