Various Models Used in Analysing Municipal Solid Waste Generation–A Review

Abstract

At present, factors such as growth in population, economic development, urbanization and improved standard of living increase the quantity and complexity of generated Municipal Solid Waste. The different approaches for developing models for forecasting municipal solid waste generation have been classified into conventional and non-conventional or artificial intelligence models. While the conventional models include sample survey, system dynamics, econometric models, time series analysis, factor driven models and multiple linear regression models, the non-conventional models include artificial neural networks, Fuzzy logic models and Adaptive Neuro Fuzzy Inference System models. In this review, various factors considered for modelling, locations of study, sources of data and various studies conducted by researchers have been tabulated in detail for identifying the major factors and models used in developed and developing countries. Non-conventional models are being preferred because of their capacity to analyse dynamic data and for their prediction accuracy.