𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Improving peak flow estimates in artificial neural network river flow models

✍ Scribed by K. P. Sudheer; P. C. Nayak; K. S. Ramasastri

Publisher
John Wiley and Sons
Year
2003
Tongue
English
Weight
171 KB
Volume
17
Category
Article
ISSN
0885-6087

No coin nor oath required. For personal study only.

✦ Synopsis

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 been attributed to the local variations in the function being mapped due to varying skewness in the data series, and theoretical considerations of the network functioning confirm this. It is envisaged that an appropriate data transformation will reduce the local variations in the function being mapped, and thus any ANN model built on the transformed series should perform better. This heuristic is illustrated and confirmed by many case studies and the results suggest that the model performance is significantly improved by data transformation. The model built on transformed data outperforms the model built on raw data in terms of various statistical performance indices. The peak estimates are improved significantly by data transformation. Copyright © 2003 John Wiley & Sons, Ltd.

📜 SIMILAR VOLUMES

Explaining the internal behaviour of art
Explaining the internal behaviour of artificial neural network river flow models
✍ K. P. Sudheer; Ashu Jain 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 307 KB

## Abstract A novel method of visualizing and understanding the internal functional behaviour of an artificial neural network (ANN) river flow model is presented. The method hypothesizes that an ANN is able to map a function similar to the flow duration curve while modelling the river flow. A mathe

Forecasting flows in Apalachicola River
Forecasting flows in Apalachicola River using neural networks
✍ Wenrui Huang; Bing Xu; Amy Chan-Hilton 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 292 KB

## 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 opti

Artificial neural network applications i
Artificial neural network applications in improved noise wave modeling of microwave FETs
✍ Zlatica D. Marinković; Olivera Pronić-Rančić; Vera Marković 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 489 KB

## Abstract An application of artificial neural networks (ANNs) for accuracy improving of the microwave FETs (MESFET/HEMT, dual‐gate MESFET) noise modeling is presented in this paper. The proposed model is based on a basic transistor noise wave model, whose noise wave temperatures are assumed to be

Predicting catchment flow in a semi-arid
Predicting catchment flow in a semi-arid region via an artificial neural network technique
✍ S. Riad; J. Mania; L. Bouchaou; Y. Najjar 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 159 KB

## Abstract A model of rainfall–runoff relationships is an essential tool in the process of evaluation of water resources projects. In this paper, we applied an artificial neural network (ANN) based model for flow prediction using the data for a catchment in a semi‐arid region in Morocco. Use of th