✦ LIBER ✦

Forecasting flows in Apalachicola River using neural networks

✍ Scribed by Wenrui Huang; Bing Xu; Amy Chan-Hilton

Publisher: John Wiley and Sons
Year: 2004
Tongue: English
Weight: 292 KB
Volume: 18
Category: Article
ISSN: 0885-6087
DOI: 10.1002/hyp.1492

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

Forecasting river flow is important to water resources management and planning. In this study, an artificial neural network (ANN) model was successfully developed to forecast river flow in Apalachicola River. The model used a feed‐forward, back‐propagation network structure with an optimized conjugated training algorithm. Using long‐term observations of rainfall and river flow during 1939–2000, the ANN model was satisfactorily trained and verified. Model predictions of river flow match well with the observations. The correlation coefficients between forecasting and observation for daily, monthly, quarterly and yearly flow forecasting are 0·98, 0·95, 0·91 and 0·83, respectively. Results of the forecasted flow rates from the ANN model were compared with those from a traditional autoregressive integrated moving average (ARIMA) forecasting model. Results indicate that the ANN model provides better accuracy in forecasting river flow than does the ARIMA model. Copyright © 2004 John Wiley & Sons, Ltd.

📜 SIMILAR VOLUMES

Forecasting discharge in Amazonia using

Forecasting discharge in Amazonia using artificial neural networks

✍ Cíntia Bertacchi Uvo; Ute Tölle; Ronny Berndtsson 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 167 KB 👁 2 views

Forecasting futures trading volume using

Forecasting futures trading volume using neural networks

✍ Iebeling Kaastra; Milton S. Boyd 📂 Article 📅 1995 🏛 John Wiley and Sons 🌐 English ⚖ 828 KB

Enforced self-organizing map neural netw

Enforced self-organizing map neural networks for river flood forecasting

✍ Fi-John Chang; Li-Chiu Chang; Yan-Shiang Wang 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 336 KB

## Abstract Self‐organizing maps (SOMs) have been successfully accepted widely in science and engineering problems; not only are their results unbiased, but they can also be visualized. In this study, we propose an enforced SOM (ESOM) coupled with a linear regression output layer for flood forecast

Improving peak flow estimates in artific

Improving peak flow estimates in artificial neural network river flow models

✍ K. P. Sudheer; P. C. Nayak; K. S. Ramasastri 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 171 KB

## Abstract In this paper, the concern of accuracy in peak estimation by the artificial neural network (ANN) river flow models is discussed and a suitable statistical procedure to get better estimates from these models is presented. The possible cause for underestimation of peak flow values has bee

Real-time recurrent learning neural netw

Real-time recurrent learning neural network for stream-flow forecasting

✍ F.-John Chang; Li-Chiu Chang; Hau-Lung Huang 📂 Article 📅 2002 🏛 John Wiley and Sons 🌐 English ⚖ 148 KB

## Abstract Various types of neural networks have been proposed in previous papers for applications in hydrological events. However, most of these applied neural networks are classified as static neural networks, which are based on batch processes that update action only after the whole training da

Forecasting daily foreign exchange rates

Forecasting daily foreign exchange rates using genetically optimized neural networks

✍ Ashok K. Nag; Dr Amit Mitra 📂 Article 📅 2002 🏛 John Wiley and Sons 🌐 English ⚖ 97 KB

## Abstract Forecasting currency exchange rates is an important financial problem that has received much attention especially because of its intrinsic difficulty and practical applications. The statistical distribution of foreign exchange rates and their linear unpredictability are recurrent themes