Due to the relatively complex construction and demolition waste (C&DW) spectrum and
texture, it is difficult to identify C&DW by simply constructing a remote sensing index …

The main aim of this work was to compare six machine learning (ML)-based models to
predict the municipal solid waste (MSW) generation from selected residential areas of …

Prediction of the accurate amount of solid waste is difficult work because several parameters
affect it. There is a high degree of fluctuation in the prediction of amount of solid waste …

The quantity of urban solid waste continuously increases every year due to a number of
variables, including population growth, financial situation, and consumption patterns,. A …

Municipal Solid Waste (MSW) management enact a significant role in protecting public
health and the environment. The main objective of this paper is to explore the utility of using …

As a result of land resources constraining in China, demolition and reconstruction of existing
buildings become an important means to meet the requirement of urban renewal, in which a …

Quantitative prediction of municipal solid waste generation plays an important role in the
optimization and programming of municipal solid waste management system. Being aware …

The evolution of machine learning (ML) algorithms provides researchers and engineers with
state-of-the-art tools to dynamically model complex relationships. The design and operation …

The construction industry generates a substantial volume of solid waste, often destinated for
landfills, causing significant environmental pollution. Waste recycling is decisive in …

Among all construction activities, demolition normally generates the largest proportion of
construction and demolition (C&D) waste, to which requires more importance being attached …