𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Using recurrent neural networks to reconstruct rainfall-runoff processes

✍ Scribed by Tsung-yi Pan; Ru-yih Wang

Book ID
102860595
Publisher
John Wiley and Sons
Year
2005
Tongue
English
Weight
513 KB
Volume
19
Category
Article
ISSN
0885-6087

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

Many novel techniques for reconstructing rainfall‐runoff processes require hydrometeorologic and geomorphologic information for modelling. However, certain information is not always measurable. In this paper, we employ a special recurrent neural network to reconstruct the rainfall‐runoff process by using collected rainfall data. In addition, we propose an indirect system identification to overcome the drawback of a traditional, time‐consuming trial‐and‐error search. The indirect system identification is an efficient method to recognize the structure of a recurrent neural network. The unit hydrograph can be derived directly from the weights of the network due to a state‐space form embedded in the recurrent neural network. This improves the link between the weights of the network and the physical concepts that most neural networks fail to connect.

The case studies of 41 events from 1966 to 1997 have been implemented in Taiwan's Wu‐Tu watershed, where the runoff path‐lines are short and steep. Two recurrent neural networks and one state‐space model are utilized to simulate the rainfall‐runoff processes for comparison. The results are validated by four criteria: coefficient of efficiency; peak discharge error; time to peak arrival error; total discharge volume error. The resulting data from the recurrent neural network reveal that the neural network proposed herein is appropriate for hydrological systems. Copyright © 2005 John Wiley & Sons, Ltd.

📜 SIMILAR VOLUMES

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

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