Back to Results
First PageMeta Content
Computational statistics / Cybernetics / Science / Waste Management / Inc / Forecasting / Artificial neural network / Waste / Macroeconomic model / Computational neuroscience / Statistics / Neural networks


A hierarchical systems modelling approach based on neural networks for forecasting global waste generation: A case study of Chile
Add to Reading List

Document Date: 2013-01-15 18:46:48

Open Document

File Size: 259,37 KB

Share Result on Facebook

City

Philadelphia / Seattle / Santiago / Taipei City / London / /

Company

International Thomson Computer Press / Neural Networks / Group 1 Group 2 Group / Forecasting Waste Generation Using Artificial Neural Networks / Waste Management / Domiciliary Solid Waste Using Artificial Neural Networks / Artificial Neural Networks Artificial Neural Networks / /

Country

Taiwan / Jordan / United States / Mexico / Kuwait / New Zealand / Chile / /

/

Event

FDA Phase / /

Facility

National Institute of Statistics / Lincoln University / /

IndustryTerm

search method / typical feed forward network / communal analysis tool / search process / statistical tools / electricity consumption / hierarchical systems / recurrent networks / hierarchical communal analysis tool / recurrent network / feed forward network / forward networks / /

Organization

United States Environmental Protection Agency / Labour Force / Air & Waste Management Association / Lincoln University / Lincoln / Central Bank of Chile / National Commission for the Environment / S. Foundation of Neural Networks / US Federal Reserve / Lincoln University / Environmental Engineering Division / Centre for Advanced Computational Solutions / National Institute of Statistics / /

Person

Ali Khan / Conservation / Recycling Impacts / Cluster / Commune / /

Position

Global waste generation model / representative commune / feed forward / representative / representative of Group / local representative / model for the represented commune / /

Product

Figure / /

ProvinceOrState

Indiana / /

Region

Southeastern United States / /

Technology

neural network / Environmental Engineering / /

URL

http /

SocialTag