Artificial Neural Networks for the Prediction of Channel Geomorphic Condition and Stream Sensitivity: Fid-Bau Portal

Artificial Neural Networks for the Prediction of Channel Geomorphic Condition and Stream Sensitivity

Besaw, L. / Rizzo, D.M. / Kline, M. / American Society of Civil Engineers; Environmental and Water Resources Institute (U.S.)

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Artificial Neural Networks for the Prediction of Channel Geomorphic Condition and Stream Sensitivity

Besaw, L. / Rizzo, D.M. / Kline, M. / American Society of Civil Engineers; Environmental and Water Resources Institute (U.S.)

Artificial Neural Networks for the Prediction of Channel Geomorphic Condition and Stream Sensitivity

Besaw, L. / Rizzo, D.M. / Kline, M. / American Society of Civil Engineers; Environmental and Water Resources Institute (U.S.)

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Title:

Artificial Neural Networks for the Prediction of Channel Geomorphic Condition and Stream Sensitivity

Contributors:

Besaw, L. (author) / Rizzo, D.M. (author) / Kline, M. (author) / Kabbes, K.C. / American Society of Civil Engineers; Environmental and Water Resources Institute (U.S.)

Conference:

World environmental and water resources congress ; 2006 ; Tampa, FL

Published in:

World environmental and water resources congress ; 624

WORLD ENVIRONMENTAL AND WATER RESOURCES CONGRESS

Publication date:

2007-01-01

Size:

624 pages

Remarks:

Held on CD-ROM; Shelved in Conference Records

ISBN:

9780784409275

Type of media:

Conference paper

Type of material:

Language:

English

Keywords:

Environmental resources , Water resources

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