𝔖 Bobbio Scriptorium
✦   LIBER   ✦

A comparative analysis of training methods for artificial neural network rainfall–runoff models

✍ Scribed by Sanaga Srinivasulu; Ashu Jain

Publisher
Elsevier Science
Year
2006
Tongue
English
Weight
212 KB
Volume
6
Category
Article
ISSN
1568-4946

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

A data-driven algorithm for constructing
A data-driven algorithm for constructing artificial neural network rainfall-runoff models
✍ K. P. Sudheer; A. K. Gosain; K. S. Ramasastri 📂 Article 📅 2002 🏛 John Wiley and Sons 🌐 English ⚖ 104 KB

## Abstract A new approach for designing the network structure in an artificial neural network (ANN)‐based rainfall‐runoff model is presented. The method utilizes the statistical properties such as cross‐, auto‐ and partial‐auto‐correlation of the data series in identifying a unique input vector th

Rainfall-runoff modelling using artifici
Rainfall-runoff modelling using artificial neural networks: comparison of network types
✍ A. R. Senthil Kumar; K. P. Sudheer; S. K. Jain; P. K. Agarwal 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 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

Integrating hydrometeorological informat
Integrating hydrometeorological information for rainfall-runoff modelling by artificial neural networks
✍ Yen-Ming Chiang; Fi-John Chang 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 213 KB

The major purpose of this study is to effectively construct artificial neural networks-based multistep ahead flood forecasting by using hydrometeorological and numerical weather prediction (NWP) information. To achieve this goal, we first compare three mean areal precipitation forecasts: radar/NWP m

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

Identification of physical processes inh
Identification of physical processes inherent in artificial neural network rainfall runoff models
✍ Ashu Jain; K. P. Sudheer; Sanaga Srinivasulu 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 286 KB

## Abstract The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes

Rainfall-runoff modelling using artifici
Rainfall-runoff modelling using artificial neural networks technique: a Blue Nile catchment case study
✍ Mamdouh A. Antar; Ibrahim Elassiouti; Mohamed N. Allam 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 240 KB 👁 1 views

A rainfall-runoff model based on an artificial neural network (ANN) is presented for the Blue Nile catchment. The best geometry of the ANN rainfall-runoff model in terms of number of hidden layers and nodes is identified through a sensitivity analysis. The Blue Nile catchment (about 300 000 km 2 ) i