The use of machine learning techniques in waste management studies is increasingly
popular. Recent literature suggests k-fold cross validation may reduce input dataset partition …

To mitigate the greenhouse gas effect, accurate and precise landfill gas prediction models
are required for more precise prediction of the amount and recovery time of methane gas …

This work develops an artificial neural network (ANN) model using genetic algorithms to
estimate the annual amount (kg/inhabitant/year) of separately-collected household …

Efficient and effective solid waste management requires sufficient ability to predict the
operational capacity of a system correctly. Waste prediction models have been widely …

Municipal waste disposal behaviors in Regina, the capital city of Saskatchewan, Canada
have significantly changed during the COVID-19 pandemic. About 7.5 year of waste …

The main objective of this study was to develop models for accurate prediction of municipal
solid waste (MSW) generation and diversion based on demographic and socio-economic …

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

As one of the most popular non-linear models, artificial neural network (ANN) has been
successfully applied in the prediction of municipal solid waste (MSW). Despite its high …

Accurate prediction of municipal solid waste's quality and quantity is crucial for designing
and programming municipal solid waste management system. But predicting the amount of …

Municipal solid waste (MSW) amount has direct influence on MSW management, policy-
decision making, and MSW treatment methods. Machine learning has great potential for …