Visualisation of Hidden Neuron Behaviour in a Neural Network Rainfall-Runoff Model
This chapter applies graphical and statistical methods to visualise hidden neuron behaviour in a trained neural network rainfall-runoff model developed for the River Ouse catchment in northern England. The methods employed include plotting individual part
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Water Science and Technology Library VOLUME 68
Editor-in-Chief V.P. Singh, Texas A&M University, College Station, U.S.A. Editorial Advisory Board M. Anderson, Bristol, U.K. L. Bengtsson, Lund, Sweden J. F. Cruise, Huntsville, U.S.A. U. C. Kothyari, Roorkee, India S. E. Serrano, Philadelphia, U.S.A. D. Stephenson, Johannesburg, South Africa W. G. Strupczewski, Warsaw, Poland
For other titles published in this series, go to www.springer.com/series/6689
Robert J. Abrahart · Linda M. See · Dimitri P. Solomatine (Eds.)
Practical Hydroinformatics Computational Intelligence and Technological Developments in Water Applications
123
Editors Robert J. Abrahart University Nottingham Dept. Geography University Park Nottingham United Kingdom NT7 2QW [email protected]
Linda M. See University of Leeds School of Geography Fac. Earth and Environment Woodhouse Lane Leeds United Kingdom LS2 9JT [email protected]
Dimitri P. Solomatine UNESCO - IHE Institute for Water Education 2601 DA Delft The Netherlands and Water Resources Section Delft University of Technology The Netherlands [email protected]
ISBN: 978-3-540-79880-4
e-ISBN: 978-3-540-79881-1
Library of Congress Control Number: 2008928293 c 2008 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: Boekhorst Design b.v. Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com
Contents
Part I Hydroinformatics: Integrating Data and Models 1
Some Future Prospects in Hydroinformatics . . . . . . . . . . . . . . . . . . . . . M.B. Abbott
2
Data-Driven Modelling: Concepts, Approaches and Experiences . . . . 17 D. Solomatine, L.M. See and R.J. Abrahart
3
Part II Artificial Neural Network Models 3
Neural Network Hydroinformatics: Maintaining Scientific Rigour . . 33 R.J. Abrahart, L.M. See and C.W. Dawson
4
Neural Network Solutions to Flood Estimation at Ungauged Sites . . . 49 C.W. Dawson
5
Rainfall-Runoff Modelling: Integrating Available Data and Modern Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 S. Srinivasulu and A. Jain
6
Dynamic Neural Networks for Nonstationary Hydrological Time Series Modeling . . . . . . . . . . . . . . . . . . .
