
Regionalization of watersheds : an approach based on cluster analysis /
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作 者:A. Ramachandra Rao and V.V. Srinivas.
分类号:
ISBN:9781402068515
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简介
Summary:
Publisher Summary 1
Clustering techniques are used to identify groups of watersheds which have similar flood characteristics. This book, the first of its kind, is a comprehensive reference on how to use these techniques for regional flood frequency analysis. It provides a detailed account of several recently developed clustering techniques, including those based on fuzzy set theory. It also brings together formerly scattered research findings on the application of clustering techniques to RFFA.
目录
1 Introduction p. 1
1.1 Regionalization for Flood Frequency Analysis p. 1
1.2 Approaches to Regionalization p. 2
1.3 Cluster Analysis in Regionalization p. 4
1.3.1 Attributes Used in Regionalization p. 4
1.3.2 Classification of Clustering Algorithms p. 6
1.3.3 Steps in Regionalization by Cluster Analysis p. 7
1.3.4 Issues in Cluster Analysis p. 9
1.4 Testing Regional Homogeneity p. 10
1.4.1 Adjusting the Regions p. 11
1.4.2 Discordancy Measure p. 12
1.5 Data Used in Examples p. 13
1.6 Organization of the Text p. 15
2 Regionalization by Hybrid Cluster Analysis p. 17
2.1 Introduction to Hybrid Cluster Analysis p. 17
2.2 Classification of Hard Clustering Algorithms p. 17
2.2.1 Hierarchical Clustering Methods p. 18
2.2.2 Partitional Clustering Methods p. 21
2.2.3 Hybrid Clustering p. 22
2.3 Clustering Algorithms and Performance Assessment p. 23
2.3.1 Hybrid Algorithm p. 23
2.3.2 Single Linkage and Complete Linkage Algorithms p. 24
2.3.3 Ward's Algorithm p. 25
2.3.4 Hard Cluster Validity Measures p. 28
2.4 Application of Hybrid Clustering Algorithms to Regionalization p. 32
2.4.1 Feature Extraction p. 32
2.4.2 Results from Clustering Algorithms p. 35
2.4.3 Validation of the Results p. 38
2.4.4 Testing the Regions for Robustness p. 48
2.4.5 Final Results p. 48
2.5 Concluding Comments p. 51
3 Regionalization by Fuzzy Cluster Analysis p. 57
3.1 Introduction p. 57
3.2 Classification of Fuzzy Clustering Algorithms p. 57
3.3 The Fuzzy C-Means Algorithm p. 59
3.3.1 Description of the Algorithm p. 59
3.3.2 Assignment of New Sites to Fuzzy Clusters p. 63
3.4 Fuzzy Cluster Validity Measures p. 63
3.5 Example of Using Fuzzy C-Means Algorithm for Regionalization p. 68
3.5.1 Feature Extraction p. 68
3.5.2 Results from Fuzzy C-means Algorithm p. 68
3.5.3 Testing the Regions for Robustness p. 98
3.6 Concluding Comments p. 102
4 Regionalization by Artificial Neural Networks p. 113
4.1 Introduction p. 113
4.2 Kohonen Self-Organizing Feature Maps (SOFMs) p. 113
4.2.1 Algorithm of Kohonen Self-Organizing Feature Map p. 114
4.3 Example of Using SOFMs for Regionalization p. 117
4.3.1 Features Used p. 117
4.3.2 Results from SOFM p. 118
4.3.3 Testing the Regions for Robustness p. 136
4.4 Regionalization by Two-Stage Clustering of SOFM p. 141
4.4.1 Introduction p. 141
4.4.2 Algorithm for Fuzzy Clustering of Kohonen SOFM p. 141
4.4.3 Example of Using Two-Level Fuzzy SOFM p. 145
4.5 Concluding Comments p. 153
5 Effect of Regionalization on Flood Frequency Analysis En-Ching Hsu and A. Ramachandra Rao and V.V. Srinivas p. 155
5.1 Introduction p. 155
5.2 Regional Index Flood Method Based on L-Moments p. 156
5.2.1 Introduction p. 156
5.2.2 Regional L-Moment Method p. 157
5.2.3 At-Site and Regional Parameter Estimation p. 158
5.3 Regional Regression Analysis p. 167
5.3.1 Introduction p. 167
5.3.2 GLS Regional Regression Results p. 168
5.4 Combination of GLS Regional Regression and L-Moment Method p. 174
5.5 Comparative Analysis p. 184
5.5.1 Split Sample Test for the First Method p. 184
5.5.2 Split Sample Test for the Second Method p. 188
5.5.3 Split Sample Test for the Third Method p. 189
5.5.4 Comparison of the Three Methods p. 192
5.6 Simple Scaling in Regionalized Watersheds p. 196
5.7 Probability Distributions for Flood Frequency Analysis in Regionalized Watersheds p. 199
5.7.1 Parameter Estimation p. 200
5.7.2 Quantile Estimation p. 201
5.7.3 Probability Distributions p. 201
5.7.4 Data Analysis p. 202
5.7.5 Dimensionless and Standardized Quantile Measures p. 208
5.8 Concluding Comments p. 211
6 Concluding Remarks p. 213
6.1 General Remarks on Clustering Approach to Regional Flood Frequency Analysis p. 213
6.2 Recent Developments p. 216
6.2.1 Tests of Regional Homogeneity p. 216
6.2.2 Methods for Characterizing Regional Frequency Distribution p. 217
6.2.3 Methods for Regional Frequency Analysis p. 217
6.2.4 Goodness-of-fit Measures for Regional Frequency Analysis p. 217
6.2.5 Non-Stationary Flood Frequency Analysis p. 218
6.2.6 Flood Frequency Analysis in Climate Change Scenarios p. 219
6.2.7 Simulation of Floods Using Output from GCMs p. 220
References p. 223
Index of Notation p. 233
Abbreviations p. 237
Index p. 239
1.1 Regionalization for Flood Frequency Analysis p. 1
1.2 Approaches to Regionalization p. 2
1.3 Cluster Analysis in Regionalization p. 4
1.3.1 Attributes Used in Regionalization p. 4
1.3.2 Classification of Clustering Algorithms p. 6
1.3.3 Steps in Regionalization by Cluster Analysis p. 7
1.3.4 Issues in Cluster Analysis p. 9
1.4 Testing Regional Homogeneity p. 10
1.4.1 Adjusting the Regions p. 11
1.4.2 Discordancy Measure p. 12
1.5 Data Used in Examples p. 13
1.6 Organization of the Text p. 15
2 Regionalization by Hybrid Cluster Analysis p. 17
2.1 Introduction to Hybrid Cluster Analysis p. 17
2.2 Classification of Hard Clustering Algorithms p. 17
2.2.1 Hierarchical Clustering Methods p. 18
2.2.2 Partitional Clustering Methods p. 21
2.2.3 Hybrid Clustering p. 22
2.3 Clustering Algorithms and Performance Assessment p. 23
2.3.1 Hybrid Algorithm p. 23
2.3.2 Single Linkage and Complete Linkage Algorithms p. 24
2.3.3 Ward's Algorithm p. 25
2.3.4 Hard Cluster Validity Measures p. 28
2.4 Application of Hybrid Clustering Algorithms to Regionalization p. 32
2.4.1 Feature Extraction p. 32
2.4.2 Results from Clustering Algorithms p. 35
2.4.3 Validation of the Results p. 38
2.4.4 Testing the Regions for Robustness p. 48
2.4.5 Final Results p. 48
2.5 Concluding Comments p. 51
3 Regionalization by Fuzzy Cluster Analysis p. 57
3.1 Introduction p. 57
3.2 Classification of Fuzzy Clustering Algorithms p. 57
3.3 The Fuzzy C-Means Algorithm p. 59
3.3.1 Description of the Algorithm p. 59
3.3.2 Assignment of New Sites to Fuzzy Clusters p. 63
3.4 Fuzzy Cluster Validity Measures p. 63
3.5 Example of Using Fuzzy C-Means Algorithm for Regionalization p. 68
3.5.1 Feature Extraction p. 68
3.5.2 Results from Fuzzy C-means Algorithm p. 68
3.5.3 Testing the Regions for Robustness p. 98
3.6 Concluding Comments p. 102
4 Regionalization by Artificial Neural Networks p. 113
4.1 Introduction p. 113
4.2 Kohonen Self-Organizing Feature Maps (SOFMs) p. 113
4.2.1 Algorithm of Kohonen Self-Organizing Feature Map p. 114
4.3 Example of Using SOFMs for Regionalization p. 117
4.3.1 Features Used p. 117
4.3.2 Results from SOFM p. 118
4.3.3 Testing the Regions for Robustness p. 136
4.4 Regionalization by Two-Stage Clustering of SOFM p. 141
4.4.1 Introduction p. 141
4.4.2 Algorithm for Fuzzy Clustering of Kohonen SOFM p. 141
4.4.3 Example of Using Two-Level Fuzzy SOFM p. 145
4.5 Concluding Comments p. 153
5 Effect of Regionalization on Flood Frequency Analysis En-Ching Hsu and A. Ramachandra Rao and V.V. Srinivas p. 155
5.1 Introduction p. 155
5.2 Regional Index Flood Method Based on L-Moments p. 156
5.2.1 Introduction p. 156
5.2.2 Regional L-Moment Method p. 157
5.2.3 At-Site and Regional Parameter Estimation p. 158
5.3 Regional Regression Analysis p. 167
5.3.1 Introduction p. 167
5.3.2 GLS Regional Regression Results p. 168
5.4 Combination of GLS Regional Regression and L-Moment Method p. 174
5.5 Comparative Analysis p. 184
5.5.1 Split Sample Test for the First Method p. 184
5.5.2 Split Sample Test for the Second Method p. 188
5.5.3 Split Sample Test for the Third Method p. 189
5.5.4 Comparison of the Three Methods p. 192
5.6 Simple Scaling in Regionalized Watersheds p. 196
5.7 Probability Distributions for Flood Frequency Analysis in Regionalized Watersheds p. 199
5.7.1 Parameter Estimation p. 200
5.7.2 Quantile Estimation p. 201
5.7.3 Probability Distributions p. 201
5.7.4 Data Analysis p. 202
5.7.5 Dimensionless and Standardized Quantile Measures p. 208
5.8 Concluding Comments p. 211
6 Concluding Remarks p. 213
6.1 General Remarks on Clustering Approach to Regional Flood Frequency Analysis p. 213
6.2 Recent Developments p. 216
6.2.1 Tests of Regional Homogeneity p. 216
6.2.2 Methods for Characterizing Regional Frequency Distribution p. 217
6.2.3 Methods for Regional Frequency Analysis p. 217
6.2.4 Goodness-of-fit Measures for Regional Frequency Analysis p. 217
6.2.5 Non-Stationary Flood Frequency Analysis p. 218
6.2.6 Flood Frequency Analysis in Climate Change Scenarios p. 219
6.2.7 Simulation of Floods Using Output from GCMs p. 220
References p. 223
Index of Notation p. 233
Abbreviations p. 237
Index p. 239
Regionalization of watersheds : an approach based on cluster analysis /
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