Neural Networks for Hydrological Modeling

by Robert Abrahart (Editor), P.E. Kneale (Editor), Linda M. See (Editor)

Overview

A new approach to the fast-developing world of neural hydrological modelling, this book is essential reading for academics and researchers in the fields of water sciences, civil engineering, hydrology and physical geography. Each chapter has been written by one or more eminent experts working in various fields of hydrological modelling.  The b

Product Details

ISBN-13:	9781135291006
Publisher:	CRC Press
Publication date:	05/15/2004
Sold by:	Barnes & Noble
Format:	eBook
Pages:	316
File size:	4 MB

About the Author

Abrahart, Robert; Kneale, P.E.; See, Linda M.

Table of Contents

1. Why Use Neural Networks? 2. Neural Network Modelling: Basic Tools and Broader Issues 3. Single Network Modelling Solutions 4. Hybrid Neural Network Modelling Solutions 5. The Application of Time Delay Neural Netowrks to River Level Forecasting 6. The Application of Cascade Correlation Neural Networks to River Flow Forecasting 7. The Use of Partial Recurrent Neural Networks for Autoregressive Modelling of Dynamic Hydrological Systems 8. RLF1/ Flood Foecasting via the Internet 9. Railfall-Runoff Modelling 10. A Neural Network Approach to Rainfall Forecasting in Urban Environments 11. Water Quality and Ecological Management in Freshwaters 12. Neural Network Modelling of Sediment Supply and Transfer 13. Nowcasting products from Meteorological Satellite Imagery 14. Mapping Land Cover from Remotely Sensed Imagery for Input to Hydrological Models 15. Towards a Hydrological Research Agenda Index