Predicting municipal solid waste using a coupled artificial neural network with archimedes optimisation algorithm and socioeconomic components
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 …
environmental pollutants that are a threat to ecology and human health. Predicting SW …
Predictive analysis of municipal solid waste generation using an optimized neural network model
Developing successful municipal waste management planning strategies is crucial for
implementing sustainable development. The research proposed the application of an …
implementing sustainable development. The research proposed the application of an …
Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction
Artificial neural networks (ANNs) are suitable for modeling solid waste generation. In the
present study, four training functions, including resilient backpropagation (RP), scale …
present study, four training functions, including resilient backpropagation (RP), scale …
Forecasting municipal solid waste generation using artificial intelligence modelling approaches
M Abbasi, A El Hanandeh - Waste management, 2016 - Elsevier
Municipal solid waste (MSW) management is a major concern to local governments to
protect human health, the environment and to preserve natural resources. The design and …
protect human health, the environment and to preserve natural resources. The design and …
Prediction of municipal solid waste generation for optimum planning and management with artificial neural network—case study: Faridabad City in Haryana State …
Accurate prediction of municipal solid waste generation has an important role in future
planning and waste management system. The characteristics of the generated solid waste …
planning and waste management system. The characteristics of the generated solid waste …
Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes
Reliable prediction of municipal solid waste (MSW) generation rates is a significant element
of planning and implementation of sustainable solid waste management strategies. In this …
of planning and implementation of sustainable solid waste management strategies. In this …
Forecasting municipal solid waste generation using artificial intelligence models—a case study in India
Proper management of municipal solid waste is one of the prime matters of concern for
metropolitan cities. To be able to successfully manage the solid waste generated, we need …
metropolitan cities. To be able to successfully manage the solid waste generated, we need …
Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation
MS Jassim, G Coskuner… - Waste Management & …, 2022 - journals.sagepub.com
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 …
state-of-the-art tools to dynamically model complex relationships. The design and operation …
An evolutionary machine learning approach for municipal solid waste generation estimation utilizing socioeconomic components
Municipal solid waste generation is an important parameter in waste management with
significant impacts on environment. There are many components directly influencing solid …
significant impacts on environment. There are many components directly influencing solid …
Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case …
S Azadi, A Karimi-Jashni - Waste management, 2016 - Elsevier
Predicting the mass of solid waste generation plays an important role in integrated solid
waste management plans. In this study, the performance of two predictive models, Artificial …
waste management plans. In this study, the performance of two predictive models, Artificial …