𝔖 Bobbio Scriptorium
✦   LIBER   ✦

USING ARTIFICIAL NEURAL NETWORKS TO ESTIMATE MISSING RAINFALL DATA

✍ Scribed by Robert J. Kuligowski; Ana P Barros


Book ID
111428256
Publisher
American Water Resources Association
Year
1998
Tongue
English
Weight
191 KB
Volume
34
Category
Article
ISSN
1093-474X

No coin nor oath required. For personal study only.


πŸ“œ SIMILAR VOLUMES


Classification of rainfall variability b
✍ Silas Chr. Michaelides; Constantinos S. Pattichis; Georgia Kleovoulou πŸ“‚ Article πŸ“… 2001 πŸ› John Wiley and Sons 🌐 English βš– 769 KB

## Abstract In this paper, the usefulness of artificial neural networks (ANNs) as a suitable tool for the study of the medium and long‐term climatic variability is examined. A method for classifying the inherent variability of climatic data, as represented by the rainfall regime, is investigated. T

Rainfall-induced landslide hazard assess
✍ H. B. Wang; K. Sassa πŸ“‚ Article πŸ“… 2006 πŸ› John Wiley and Sons 🌐 English βš– 804 KB

## Abstract In Japan, landslides triggered by heavy rainfall tend to occur during the annual rainy season from early June until the middle of July; these landslides constitute a major hazard causing significant property damage and loss of life. This paper proposes the use of back propagation neural

Rainfall-runoff modelling using artifici
✍ A. R. Senthil Kumar; K. P. Sudheer; S. K. Jain; P. K. Agarwal πŸ“‚ Article πŸ“… 2005 πŸ› John Wiley and Sons 🌐 English βš– 206 KB

## Abstract Growing interest in the use of artificial neural networks (ANNs) in rainfall‐runoff modelling has suggested certain issues that are still not addressed properly. One such concern is the use of network type, as theoretical studies on a multi‐layer perceptron (MLP) with a sigmoid transfer