𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Artificial neural network model for synthetic streamflow generation

✍ Scribed by Juran Ali Ahmed; Arup Kumar Sarma

Publisher
Springer Netherlands
Year
2006
Tongue
English
Weight
430 KB
Volume
21
Category
Article
ISSN
0920-4741

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Rainfall-runoff models using artificial
Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction
✍ Dae-Il Jeong; Young-Oh Kim 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 865 KB

## Abstract Previous ensemble streamflow prediction (ESP) studies in Korea reported that modelling error significantly affects the accuracy of the ESP probabilistic winter and spring (i.e. dry season) forecasts, and thus suggested that improving the existing rainfall‐runoff model, TANK, would be cr

Counterpropagation fuzzy–neural network
Counterpropagation fuzzy–neural network for streamflow reconstruction
✍ Fi-John Chang; Hsiang-Fan Hu; Yen-Chang Chen 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 192 KB

## Abstract This study combines neural networks and fuzzy arithmetic to present a counterpropagation fuzzy–neural network (CFNN) for streamflow reconstruction. The CFNN has a rule‐based control, a modified self‐organizing counterpropagation network, and a fuzzy control predictor. It can generate ru

A multivariate non-parametric model for
A multivariate non-parametric model for synthetic generation of daily streamflow
✍ Wensheng Wang; Jing Ding 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 208 KB

## Abstract A __p__‐order multivariate kernel density model based on kernel density theory has been developed for synthetic generation of multivariate variables. It belongs to a kind of data‐driven approach and is able to avoid prior assumptions as to the form of probability distribution (normal or