OPTIMIZATION AND FORECASTING OF URBAN SOLID WASTE MANAGEMENT BY ARTIFICIAL NEURAL NETWORKS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

JAFARZADEH GHOUSHCHI SAEID; HASHEMPOUR SONA

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON MANAGEMENT AND HUMANITIES
Year:2015 | Issue:1

Download Full-Text

View:

370

Download:

114

Information Seminar Paper

Title

OPTIMIZATION AND FORECASTING OF URBAN SOLID WASTE MANAGEMENT BY ARTIFICIAL NEURAL NETWORKS

Author(s)

JAFARZADEH GHOUSHCHI SAEID | HASHEMPOUR SONA

Keywords

URBAN SOLID WASTE

ARTIFICIAL NEURAL NETWORKS

BATCH BACK PROPAGATION

SOLID WASTE MANAGEMENT

Abstract

URBAN SOLID WASTE (USW) IS THE NATURAL RESULT OF HUMAN ACTIVITIES. USW GENERATION MODELING IS OF MAJOR SIGNIFICANCE IN PROGRAMMING AND PLANNING SOLID WASTE MANAGEMENT SYSTEM. EVERY YEAR, THE MUNICIPALITY SPENDS MORE THAN %75 OF ITS BUDGET FOR COLLECTION AND TRANSPORTATION OF SOLID WASTE. WASTE DISPOSAL IS ESSENTIAL AND IS ALSO VERY EXPENSIVE. DUE TO HIGH FLUCTUATION OF THE AMOUNT OF THE PRODUCED WASTE IN URMIA OVER TIME, THE USE OF NEURAL NETWORKS IS APPROPRIATE METHOD TO OPTIMIZE AND PREDICT THE AMOUNT OF THE PRODUCED WASTE BASED ON NON-LINEAR AND COMPLEX RELATIONSHIPS BETWEEN INPUTS AND OUTPUTS. IN THIS STUDY, EXTRA PARAMETERS SUCH AS NUMBER OF LABOR, VAN AND TRUCK (WASTE COLLECTION AND TRANSPORT) WERE EMPLOYED TO ASSESS THEIR EFFECT IN IMPROVEMENT STRUCTURE OF ANN MODEL AND TRAINING PERFORMANCE OF GENERATED MODEL. THE MONITORING DATA FROM SUMMER OF 2013 ARE DESIGNED TO PROVIDE THE REQUIREMENTS OF TRAINING AND TESTING THE NEURAL NETWORK. FINALLY, WITH RESPECT TO RMSE AND R2, SUITABLE MODELS FOR OPTIMIZATION AND FORECASTING OF SOLID WASTE WERE SELECTED FOR THE STUDY. RESULTS POINT OUT THAT ARTIFICIAL NEURAL NETWORK MODEL HAS MORE ADVANTAGES IN COMPARISON WITH TRADITIONAL METHODS, IN OPTIMIZING AND PREDICTING THE MUNICIPAL SOLID WASTE GENERATION.

Cites

No record.

References

No record.

Cite

APA: Copy

JAFARZADEH GHOUSHCHI, SAEID, & HASHEMPOUR, SONA. (2015). OPTIMIZATION AND FORECASTING OF URBAN SOLID WASTE MANAGEMENT BY ARTIFICIAL NEURAL NETWORKS. INTERNATIONAL CONFERENCE ON MANAGEMENT AND HUMANITIES. SID. https://sid.ir/paper/909147/en

Vancouver: Copy

JAFARZADEH GHOUSHCHI SAEID, HASHEMPOUR SONA. OPTIMIZATION AND FORECASTING OF URBAN SOLID WASTE MANAGEMENT BY ARTIFICIAL NEURAL NETWORKS. 2015. Available from: https://sid.ir/paper/909147/en

IEEE: Copy

SAEID JAFARZADEH GHOUSHCHI, and SONA HASHEMPOUR, “OPTIMIZATION AND FORECASTING OF URBAN SOLID WASTE MANAGEMENT BY ARTIFICIAL NEURAL NETWORKS,” presented at the INTERNATIONAL CONFERENCE ON MANAGEMENT AND HUMANITIES. 2015, [Online]. Available: https://sid.ir/paper/909147/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops