Forecasting generation waste using artificial neural networks

Elmira Shamshiry; Behzad Nadi; Mazlin Bin Mokhtar; Ibrahim Komoo; Halimaton Saadiah Hashim; Nadzri Y. Ahya

Forecasting generation waste using artificial neural networks

Elmira Shamshiry
, Behzad Nadi
, Mazlin Bin Mokhtar
, Ibrahim Komoo
, Halimaton Saadiah Hashim
, Nadzri Y. Ahya

Universiti Kebangsaan Malaysia
Ministry Of Housing and Local Government

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

Abstract

Municipal solid waste (MSW) is the natural result of human activities. MSW generation modeling is major significant in municipal solid waste management system planning. Predicting the amount of generated waste is difficult task because it is affect by various parameters. In this research, Artificial Neural Network (ANN) was trained and tested to weekly waste generation (WWG) model in Sari's city of Iran. Input data is consisting WWG observation and the number of trucks, personnel and fuel cost were obtained from Sari Recycling and Material Conversion Organization. The gathering data related to monitoring 2006 to 2008.

Original language	English
Title of host publication	Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011
Pages	770-777
Number of pages	8
Publication status	Published - 2011
Externally published	Yes
Event	2011 International Conference on Artificial Intelligence, ICAI 2011 - Las Vegas, NV, United States Duration: 18 Jul 2011 → 21 Jul 2011

Publication series

Name	Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011
Volume	2

Conference

Conference	2011 International Conference on Artificial Intelligence, ICAI 2011
Country/Territory	United States
City	Las Vegas, NV
Period	18/07/11 → 21/07/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production

Keywords

Artificial neural network
Prediction
Sensitive analysis
Weekly waste generation

Cite this

@inproceedings{697cc2e1bd334c459bf73f78ecc9dc42,

title = "Forecasting generation waste using artificial neural networks",

abstract = "Municipal solid waste (MSW) is the natural result of human activities. MSW generation modeling is major significant in municipal solid waste management system planning. Predicting the amount of generated waste is difficult task because it is affect by various parameters. In this research, Artificial Neural Network (ANN) was trained and tested to weekly waste generation (WWG) model in Sari's city of Iran. Input data is consisting WWG observation and the number of trucks, personnel and fuel cost were obtained from Sari Recycling and Material Conversion Organization. The gathering data related to monitoring 2006 to 2008.",

keywords = "Artificial neural network, Prediction, Sensitive analysis, Weekly waste generation",

author = "Elmira Shamshiry and Behzad Nadi and \{Bin Mokhtar\}, Mazlin and Ibrahim Komoo and Hashim, \{Halimaton Saadiah\} and Ahya, \{Nadzri Y.\}",

year = "2011",

language = "English",

isbn = "9781601321855",

series = "Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011",

pages = "770--777",

booktitle = "Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011",

note = "2011 International Conference on Artificial Intelligence, ICAI 2011 ; Conference date: 18-07-2011 Through 21-07-2011",

}

Shamshiry, E, Nadi, B, Bin Mokhtar, M, Komoo, I, Hashim, HS & Ahya, NY 2011, Forecasting generation waste using artificial neural networks. in Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011. Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011, vol. 2, pp. 770-777, 2011 International Conference on Artificial Intelligence, ICAI 2011, Las Vegas, NV, United States, 18/07/11.

Forecasting generation waste using artificial neural networks. / Shamshiry, Elmira; Nadi, Behzad; Bin Mokhtar, Mazlin et al.
Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011. 2011. p. 770-777 (Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Forecasting generation waste using artificial neural networks

AU - Shamshiry, Elmira

AU - Nadi, Behzad

AU - Bin Mokhtar, Mazlin

AU - Komoo, Ibrahim

AU - Hashim, Halimaton Saadiah

AU - Ahya, Nadzri Y.

PY - 2011

Y1 - 2011

N2 - Municipal solid waste (MSW) is the natural result of human activities. MSW generation modeling is major significant in municipal solid waste management system planning. Predicting the amount of generated waste is difficult task because it is affect by various parameters. In this research, Artificial Neural Network (ANN) was trained and tested to weekly waste generation (WWG) model in Sari's city of Iran. Input data is consisting WWG observation and the number of trucks, personnel and fuel cost were obtained from Sari Recycling and Material Conversion Organization. The gathering data related to monitoring 2006 to 2008.

AB - Municipal solid waste (MSW) is the natural result of human activities. MSW generation modeling is major significant in municipal solid waste management system planning. Predicting the amount of generated waste is difficult task because it is affect by various parameters. In this research, Artificial Neural Network (ANN) was trained and tested to weekly waste generation (WWG) model in Sari's city of Iran. Input data is consisting WWG observation and the number of trucks, personnel and fuel cost were obtained from Sari Recycling and Material Conversion Organization. The gathering data related to monitoring 2006 to 2008.

KW - Artificial neural network

KW - Prediction

KW - Sensitive analysis

KW - Weekly waste generation

UR - https://www.scopus.com/pages/publications/84866056658

M3 - Conference contribution

AN - SCOPUS:84866056658

SN - 9781601321855

T3 - Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011

SP - 770

EP - 777

BT - Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011

T2 - 2011 International Conference on Artificial Intelligence, ICAI 2011

Y2 - 18 July 2011 through 21 July 2011

ER -

Forecasting generation waste using artificial neural networks

Abstract

Publication series

Conference

UN SDGs

Keywords

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Cite this