Artificial Neural Networks in Hydrology

337 Pages·2000·13.06 MB·English

by  R. S. Govindaraju

ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY
Water Science and Technology Library
VOLUME36
Editor-in-ChieJ
V. P. Singh, Louisiana State University, Baton Rouge, 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
B. M. Sahni, Aurangabad, India
S.E. Serrano, Lexington, U.S.A.
D. Stephenson, Johannesburg, SouthAfrica
The titZes published in this series are listed at the end ofthis voZume.
ARTIFICIAL NEURAL
NETWORKS IN HYDROLOGY
edited by
R.S. GOVINDARAJU
Purdue University,
West Lafayette, Indiana, U.S.A.
and
A. RAMACHANDRA RAO
Purdue University,
West Lafayette, Indiana, U.S.A.
Springer-Science+Business Media, B.V.
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 978-90-481-5421-0 ISBN 978-94-015-9341-0 (eBook)
DOI 10.1007/978-94-015-9341-0
Printed on acid-free paper
All Rights Reserved
© 2000 Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers in 2000.
Softcover reprint of the hardcover 1s t edition 2000
No part of the material protected by this copyright notice may be reproduced or
utilized in any form or by any means, electronic or mechanical,
including photocopying, recording or by any information storage and
retrieval system, without written permission from the copyright owner.
Table of Contents
ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY
ACKNOWLEDGEMENTS xi
LIST OF CONTRIBUTORS xiii
INTRODUCTION
R.S. Govindaraju and A. Ramachandra Rao
Background and Motivation
Scope of this Book
1. EFFECTIVE AND EFFICIENT MODELING FOR STREAMFLOW FORECASTING 7
H. V. Gupta, K. Hsu and S. Sorooshian
1.1 Introduction
1.2 Multi-Iayer Feedforward Neural Networks
1.2.1 N etwork structure
1.2.2 Network training
1.2.3 Gradient-based training methods
1.2.4 Global search algorithrns
1.2.5 Linear least square simplex (LLSSIM)
1.3 Streamflow Forecasting Using TFNNs
1.4 Discussion
1.5 Appendix
2. STREAMFLOW FORECASTING BASED ON ARTIFICIAL NEURAL NETWORKS 23
J.D. Salas, M. Markus and A.S. Tokar
2.1 Introduction
2.2 Forecasting Based on Artificial Neural Networks
2.2.1 Basic concepts
2.2.2 Artificial neural network approximation models
2.2.3 Training based on back-propagation method
2.2.4 Validation (testing)
2.2.5 Forecast uncertainty
2.2.6 An example of streamflow forecasting based on ANNs
2.3 Daily Streamflow Forecasting for Litde Patuxent River
2.3.1 ANN forecasting model
2.3.2 Results and discussion
2.4 Monthly Streamflow Forecasting Based on Artificial Neural Networks
3. REAL TIME FORECASTING USING NEURAL NETWORKS 53
M. C. Deo and K. Thirumalaiah
3.1 Introduction
3.2 The Network and Its Training
3.2.1 Back-propagation algorithm
3.2.2 Conjugate gradient algorithm
3.2.3 Cascade correlation algorithm
3.3 Forecasting ofFlood Runoff
3.3.1 Use of flow time history
3.3.2 Use ofrain gauge data
3.4 Forecasting of Water Levels
3.4.1 Forecasting at site Jagdalpur
3.4.2 Forecasting at site Kunta
3.4.3 Forecasting at site Koida
vi
3.5 Catchment-Scale Forecasting
3.5.1 Software development
3.5.2 Data
3.5.3 Gauge network
3.5.4 NN training
3.5.5 MR and AR models
3.5.6 Forecast
4. MODULAR NEURAL NETWORKS FOR WATERSHED RUNOFF 73
B. Zhang and R.S. Govindaraju
4.1 Introduction
4.2 Modular Neural Networks
4.2.1 Motivation for the use ofmodular neural networks
4.2.2 Architecture ofmodular neural networks
4.2.3 Mathematical model for the modular network
4.2.4 Determination of posterior probabilities
4.2.5 Training the expert networks
4.2.6 Training the gating network
4.2.7 Updating the variances during training
4.3 Description of Study Watersheds
4.4 Identification of Input Parameters
4.5 Results and Discussion
4.6 Conclusion
5. RADIAL-BASIS FUNCTION NETWORKS 93
R. S. Govindaraju and B. Zhang
5.1 Introduction
5.2 Radial-Basis Functions for Exact Interpolation
5.3 Generalized Radial-Basis Function Networks
5.4 Training Strategies
5.4.1 Random selection of centers
5.4.2 Supervised selection of centers
5.4.3 Orthogonal least squares
5.4.4 Clustering algorithms
5.4.5 Self-organizing feature maps
5.5 Applications
5.6 Geostatistics and RBF Networks
5.6.1 Similarities and differences
5.6.2 Interpolation of hydraulic conductivity
5.7 Limitations ofRBF Networks
5.8 Conclusions
6. ARTIFICIAL NEURAL NETWORKS IN SUBSURFACE CHARACTERIZATION 111
D. M. Rizzo and D. E. Dougherty
6.1 Introduction
6.2 Data Management
6.3 Relationship between ANNs and Classical Spatial Statistics
6.4 Traditional Approach to Statistical Pattern Completion
6.4.1 Bayesian classifiers
6.4.2 Fisher's linear discriminant analysis
6.4.3 K-nearest neighbor and K-means classifiers
6.4.4 Regression and kriging
6.5 Re\ationship of Counterpropagation to Classical Statistics
6.5.1 Kohonen's weights and nearest neighbor classifiers
6.5.2 Grossberg weights and the Bayes classifier
vii
6.6 Application Using Counterporpagation Characterization of Aquifer Properties
Using 'Hard' and 'Soft' Data
6.6. I Sequential training to incorporate mixed data
6.7 Relating Backpropagation to Classical Statistics
6.8 Applications Using Backpropagation: Regionalized Cokriging
6.8.1 Training Phase
6.8.2 Interpolation phase
6.9 Summary
7. OPTIMAL GROUNDWATER REMEDIATION USING ARTIFICIAL NEURAL 135
NETWORKS
L.L. Rogers, V. M. Johnson and F.U Dowla
7.1 Background in Simulation-management Modeling and Motivation for Using
Artificial Neural Networks (ANNs)
7.2 Methodology
7.2.1 Creation of the knowledge base
7.2.2 Training and testing of ANNs
7.2.3 Search for optimal weil combinations
7.2.4 Verify optimal combinations with the simulator
7.3 Application to Field-scale Site
7.3.1 Site description and groundwater model
7.3.2 Management questions and results
7.3.3 Location analysis
8. ADAPTIVE NEURAL NETWORKS IN REGULATION OF RIVER FLOWS 153
J.M. Reddy and B. M. Wilamowski
8.1 Problem Statement
8.2 ANN Approach
8.2.1 Drawbacks of earlier methods
8.2.2 Potential of ANNs
8.2.3 Real-time data acquisition systems
8.2.4 On-line system identification and adaptive regulation of river flows
8.3 Application of ANN to River Flow Regulation
8.3.1 Application of ANN to water resources problems
8.3.2 Application to North Platte river in Wyoming
8.3.3 Application to Arkansas river in Colorado
8.4 Conclusions
9. IDENTIFICATION OF POLLUTION SOURCES VIA NEURAL NETWORKS 179
G.M. Brion and S. Lingireddy
9.1 Introduction
9.2 Suitability ofNeural Networks for Watershed management
9.3 Sources of Encysted Protozoa
9.4 Protozoan Indicators
9.5 Watershed Management Tools
9.5.1 Neural Network Models
9.5.2 Relative merits ofneural network models
9.6 Case Study-Source Identification Model
9.6.1 Site description
9.6.2 Data collection
9.6.3 Neural network architecture
9.6.4 Training schemes
9.6.5 Model predictions
9.6.6 Model sensitivity
9.6.7 Portability to other watersheds
9.7 Future Trends
viii
10. SPATIAL ORGANIZATION AND CHARACTERIZATION OF SOlL PHYSICAL
PROPERTIES USING SELF-ORGANIZING MAPS 199
S. Islam alld R. Kothari
10.1 Introduction
10.2 SelfOrganizing Maps (SOM): A BriefOverview
10.3 Spatiai Characterization ofSoil Moisture Using SOM
10.4 Identification of Soil Texture Uusing SOM
10.5 Discussion
11. RAINFALL ESTIMATION FROM SATELLITE lMAGERY 209
K.-L. Hsu, H V. Gupta, X Gao alld S. Sorooshiall
11.1 Introduction
11.2 Satellite-Based Rainfail Estimates
11.3 Artificiai Neural Network Models
11.3.1 Modified counter propagation model (MCPN)
11.3.2 Network training
11.4 Data Preprocessing
11.5 Rainfall Estimation over the Land Surface
11.5.1 Rainfail estimation from GOES imagery
11.5.2 Data used, model architecture, and training aigorithm parameters
11.5.3 Results
11.6 Rainfall Estimation over Oceans Using Multi-Satellite Imagery
11.6.1 Enhanced GOES-infrared based rainfall estimates over oceans
11.6.2 Data used and model architecture
11.6.3 Results
11. 7 Conc1usion
12. STREAMFLOW DATA INFILLING TECHNIQUES BASED ON CONCEPTS OF
GROUPS AND NEURAL NETWORKS 235
USo Pallu, M Khalil alld A. Elshorbagy
12.1 Introduction
12.1.1 Groups and groupings in hydrologie data
12.2 Assessment ofCurrent Data Infilling Methods and Techniques
12.2.1 Single-valued data approach
12.2.2 Group-valued data approach
12.2.3 Concluding remarks on the status of existing data infilling techniques
12.3 Conceptualization of Stochastic Data Infilling Process
12.3.1 Basics of data gaps
12.3.2 Group-based stochastic data infilling: problem definition
12.4 Relevance of Artificial Neural Networks in Hydrologic Data Analysis
12.4.1 Neural networks and hydrologie time series
12.5 Development of Stochastic Data Infilling Models
12.5.1 Multi-Iayer feed-forward autovariate series model (M-ASM)
12.5.2 Mulit-Iayer feed-forward bivariate series model (M-BSM)
12.6 Model Performance Indicators
12.7 Evaluation Basis of Stochastic Data Infilling Models
12.7.1 Comparison between MR-andANN-based models
12.7.2 Comparison between MR-based, PR-based, andANN-based models
12.8 Applications ofStochastic Data Infilling Models to Monthly Streamflows
12.8.1 Selection of watersheds and preparation of streamflow data sets
12.8.2 Seasonality and normality of streamflow data sets
12.8.3 Performance assessment of stochastic data infilling models
12.8.4 Comparison between ANN-, PR-, and MR-based models
12.9 Conclusions and Recommendations
ix
13. SPATIAL ANALYSIS OF HYDROLOGIC AND ENVIRONMENTAL DATA BASED ON
ARTIFICIAL NEURAL NETWORKS 259
H. -SO Shin and J.D. Salas
13.1 Introduction
13.1.1 General
13.1.2 Traditional approaches and limitations
13.1.3 Review of ANNs for spatial data analysis
13.2 ANN Methods for Spatial Analysis
13.2.1 Point estimator or interpolator
13.2.2 Higher order moment estimators
13.2.3 Posterior probability estimator and Bayesian classifier
13.3 Spatial Analysis Neural Network (SANN)
13.3.1 SANN structure
13.3.2 Training, interpolation, and classification
13.3.3 Elementary example of SANN: Walker lake data
13.4 Spatial Analysis ofPrecipitation Based on SANN
13.4.1 Introduction and data description
13.4.2 Spatial analysis ofprecipitation
13.5 Spatial Analysis of Groundwater Contamination
13.5.1 Introduction and data description
13.5.2 Identification of areas of groundwater contamination
13.6 Final Remarks
14. APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO FORECASTING OF
SURFACE WATER QUALITY VARIABLES: ISSUES, APPLICATIONS AND
CHALLENGES 287
H.R. Maier and G. C. Dandy
14.1 Water Quality Models in Context
14.1.1 Process based models
14.1.2 Statistical models
14.1.3 Artificial neural network models
14.2 Developing a Neural Network Model
14.3 Data Pre-processing
14.3.1 Range
14.3.2 Normality
14.3.3 Stationarity
14.4 Determination ofModel Input Parameters
14.4.1 Determination of input variables
14.4.2 Determination ofinput lags
14.5 Applications
14.5.1 Case studies: the river Murray
14.5.2 Forecasting salinity concentrations at Murray bridge
14.5.3 Forecasting blue-green algal concentration at Morgan
14.6 Taking Stock: Past, Present and Future
15. LONG RANGE PRECIPITATION PREDICTION IN CALIFORNIA; A LOOK INSIDE
THE "BLACK BOX" OF A TRAINED NETWORK 311
D. Silverman and J.A. Dracup
15.1 Introduction
15.2 Backpropagation Networks
15.3 Teleconnections
15.4 Datasets
15.4.1 ENSO
15.4.2700 mb height data
x
15.5 Network Deve10pment
15.5.1 Deve10pment ofa predictive neural network
15.5.2 Development ofthe networks for data extraction
15.6 Methods ofNetwork Analysis-Opening the B1ack Box
15.6.1 Variable removal
15.6.2 Hinton diagrams
15.6.3 Sensitivity analysis
15.6.4 Stereotypical inputs (Masters, 1993)
15.7 Discussion
15.8 Further Reading