Rainfall forecasting ia important for many catchment management applications, in particular for flood warning systems. The variability of rainfall in spsce and time, howeve r, renders quantitative forecasting of rainfall extremely difIicult. The depth of rainfall and its diiribution in the temporal
β¦ LIBER β¦
Application of artificial neural networks for classifying lake eutrophication status
β Scribed by R. O. Strobl; F. Forte; L. Pennetta
- Book ID
- 108956436
- Publisher
- John Wiley and Sons
- Year
- 2007
- Tongue
- English
- Weight
- 209 KB
- Volume
- 12
- Category
- Article
- ISSN
- 1320-5331
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
An application of artificial neural netw
β
Kin C. Luk; J.E. Ball; A. Sharma
π
Article
π
2001
π
Elsevier Science
π
English
β 963 KB
Application of artificial neural network
β
Brian M. Reeder; M. Paul Gough
π
Article
π
1996
π
Elsevier Science
π
English
β 914 KB
Application of artificial neural network
β
S. Malinov; W. Sha
π
Article
π
2004
π
Elsevier Science
π
English
β 145 KB
Application of artificial neural network
β
Rafal Bator; Stanislaw Sieniutycz
π
Article
π
2006
π
John Wiley and Sons
π
English
β 204 KB
Application of artificial neural network
β
Tomislav Rolich; Anica Hursa Ε ajatoviΔ; Daniela Zavec PavliniΔ
π
Article
π
2010
π
The Korean Fiber Society
π
English
β 606 KB
Application of artificial neural network
β
Ruisheng Zhang; Aixia Yan; Mancang Liu; Han Liu; Zhide Hu
π
Article
π
1999
π
Elsevier Science
π
English
β 164 KB
## Ε½ . Ε½ . Artificial neural networks ANN with extended delta-bar-delta EDBD learning algorithms were used to predict the retention indices of alkylbenzenes. The data used in this paper include 96 retention indices of 32 alkylbenzenes on three different stationary phases. Four parameters: temperat