Longterm forecasting of solid waste generation by the artificial neural networks

  title={Longterm forecasting of solid waste generation by the artificial neural networks},
  author={Mohammad Ali Abdoli and Maliheh Falah Nezhad and Reza Salehi Sede and Sadegh Behboudian},
  journal={Environmental Progress \& Sustainable Energy},
This study presents a new approach—preprocessing for reaching the stationary chain in time series—to unravel the interpolating problem of artificial neural networks (ANN) for long‐term prediction of solid waste generation (SWG). To evaluate the accuracy of the prediction by ANN, comparison between the results of the multivariate regression model and ANN is performed. Monthly time series datasets, by the yrs 2000–2010, for the city of Mashhad, are used to simulate the generated solid waste… 

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Planning is a crucial component of short- and long-term municipal waste management. Establishing the relationships between the factors that determine the amount of waste generated by municipalities

Prediction of the Production of Separated Municipal Solid Waste by Artificial Neural Networks in Croatia and the European Union

Given that global amounts of waste are growing rapidly, it is extremely important to determine what amount of waste will be generated in the near future. Accurate waste forecasting is also important

Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis

The proposed model enhanced with structural breaks can be a viable alternative for a more accurate prediction ofMSW generation at the national level, especially for developing countries for which a lack of MSW data is notable.

Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes

The results show that the developed MLP-ANN models are effective for the prediction of WGRs from different sources and could be considered as a cost-effective approach for planning integrated MSW management systems.

Artificial neural network-based time series analysis forecasting for the amount of solid waste in Bangkok

A time series forecasting model for the amount of solid waste generated in Bangkok using artificial neural networks is developed, and a suitable model for solid waste forecasting is offered.

Forecasting MSW generation using artificial neural network time series model: a study from metropolitan city

The applied time series model forecasts that Kolkata will generate about 5205 MT/day municipal solid waste in 2030 which will add more than 1000  MT/day waste with the existing rate of generation.

Monthly and seasonal modeling of municipal waste generation using radial basis function neural network

The results suggested that soft computing methods like RBF improve the estimate of MSW generation in metropolises and RBF network can be applied for forecasting and modeling MSWgeneration on a national scale.

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 optimized artificial neural



Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction.


Results point that artificial neural network model has more advantages in comparison with traditional methods in predicting the municipal solid waste generation.

Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: A case study of Mashhad

Quantity prediction of municipal solid waste (MSW) is crucial for design and programming municipal solid waste management system (MSWMS). Because effect of various parameters on MSW quantity and its

Forecasting Municipal Solid Waste Generation in Major European Cities

An understanding of the relationships between the quantity and quality of environmentally relevant outputs from human processes and regional characteristics is a prerequisite for planning and

Forecasting Generation of Urban Solid Waste in Developing Countries—A Case Study in Mexico

Based on a study of the composition of urban solid waste and of socioeconomic variables in Morelia, Mexico, generation rates were estimated and the generation of residential and nonresidential solid waste was forecasted by means of a multiple linear regression (MLR) analysis.