Longterm forecasting of solid waste generation by the artificial neural networks

@inproceedings{Abdoli2012LongtermFO,
  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},
  year={2012}
}
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… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 30 REFERENCES

Prediction of solid waste generation via grey fuzzy dynamic modeling, Resources

H. W. Chen, N. B. Chang
  • Conservation and Recycling,
  • 2000
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Comparison of neural network and principal component regression analysis to predict the solid waste generation in Tehran

R. Noori, M. A. Abdoli, M. Jalili Ghazizade, R. Samieifard
  • Iranian Journal of Public Health,
  • 2009
VIEW 2 EXCERPTS