Conventional treatment and recycling methods of organic solid waste contain inherent flaws,
such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past …

Artificial neural networks (ANNs) have recently attracted significant attention in
environmental areas because of their great self-learning capability and good accuracy in …

Efficient management of solid waste is essential to lessen its potential health and
environmental impacts. However, the current solid waste management practices encounter …

Solid Waste (SW) is one of the critical challenges of urban life. These SWs are considered
environmental pollutants that are a threat to ecology and human health. Predicting SW …

Future projections of municipal solid waste (MSW) generation trends can resolve data
inadequacy in formulating a sustainable MSW management framework. Artificial neural …

Artificial neural networks (ANNs) are suitable for modeling solid waste generation. In the
present study, four training functions, including resilient backpropagation (RP), scale …

Population growth and the acceleration of urbanization have led to a sharp increase in
municipal solid waste production, and researchers have sought to use advanced technology …

Sustainable planning of waste management is contingent on reliable data on waste
characteristics and their variation across the seasons owing to the consequential …

The waste management processes typically involve numerous technical, climatic,
environmental, demographic, socio-economic, and legislative parameters. Such complex …

Waste management is one of the primary problem that the world faces irrespective of the
case of developed or developing country. The key issue in the waste management is that the …