Abrahart R.J., Kneale P.E., See L.M. (Eds.) Neural Networks for Hydrological Modelling

Leiden: A.A. Balkema Publishers. – 2004. – 316 p. This book is intended as an introduction to those who are new to neural network hydrological modelling and as a useful update for those who have been experimenting with different tools and techniques in this area. The scope for applying neural network modelling to hydrological forecasting and prediction is considerable and it is only really in the last five to ten years that it has been tried and tested. The various chapters show that while rainfall runoff forecasting is the main area of research, neural networks are also used in ecological, fisheries, water quality, sediment, groundwater and many other water related applications. The scope is considerable because a neural network works in an equation free environment so that economic, social, hydrological and chemical data can be integrated on an equal basis. Neural networks are often denigrated as black box solutions, but they are sophisticated black boxes, which can produce very useful results. We hope that this book will encourage further users to get involved and experiment. Each of the chapters has been the subject of an independent review and we are grateful for the many comments and time involved. We are also grateful to the authors for responding to our comments and the reviewers’ input and for making the changes requested.List of Contributors.Why Use Neural Networks?
Neural Network Modelling: Basic Tools and Broader Issues.
Single Network Modelling Solutions.
Hybrid Neural Network Modelling Solutions.
The Application of Time Delay Neural Networks to River Level Forecasting.
The Application of Cascade Correlation Neural Networks to River Flow Forecasting.
The Use of Partial Recurrent Neural Networks for Autoregressive Modelling of Dynamic Hydrological Systems.
RLF/1: Flood Forecasting via the Internet.
Rainfall-Runoff Modelling.
A Neural Network Approach to Rainfall Forecasting in Urban Environments.
Water Quality and Ecological Management in Freshwaters.
Neural Network Modelling of Sediment Supply and Transfer.
Nowcasting Products from Meteorological Satellite Imagery.
Mapping Land Cover from Remotely Sensed Imagery for Input to Hydrological Models.
Towards a Hydrological Research Agenda.