Title Regionalization of watersheds : an approach based on cluster analysis / A. Ramachandra Rao and V.V. Srinivas

Published [Dordrecht, Netherlands] : Springer Science+Business Media B.V., ©2008

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Description 1 online resource (xi, 241 pages) : illustrations, maps Series Water science and technology library ; v. 58 Water science and technology library ; v. 58. Contents Cover -- Contents -- 1 Introduction -- 1.1 Regionalization for Flood Frequency Analysis -- 1.2 Approaches to Regionalization -- 1.3 Cluster Analysis in Regionalization -- 1.3.1 Attributes Used in Regionalization -- 1.3.2 Classification of Clustering Algorithms -- 1.3.3 Steps in Regionalization by Cluster Analysis -- 1.3.4 Issues in Cluster Analysis -- 1.4 Testing Regional Homogeneity -- 1.4.1 Adjusting the Regions -- 1.4.2 Discordancy Measure -- 1.5 Data Used in Examples -- 1.6 Organization of the Text -- 2 Regionalization by Hybrid Cluster Analysis -- 2.1 Introduction to Hybrid Cluster Analysis -- 2.2 Classification of Hard Clustering Algorithms -- 2.2.1 Hierarchical Clustering Methods -- 2.2.2 Partitional Clustering Methods -- 2.2.3 Hybrid Clustering -- 2.3 Clustering Algorithms and Performance Assessment -- 2.3.1 Hybrid Algorithm -- 2.3.2 Single Linkage and Complete Linkage Algorithms -- 2.3.3 Wards Algorithm -- 2.3.4 Hard Cluster Validity Measures -- 2.4 Application of Hybrid Clustering Algorithms to Regionalization -- 2.4.1 Feature Extraction -- 2.4.2 Results from Clustering Algorithms -- 2.4.3 Validation of the Results -- 2.4.4 Testing the Regions for Robustness -- 2.4.5 Final Results -- 2.5 Concluding Comments -- 3 Regionalization by Fuzzy Cluster Analysis -- 3.1 Introduction -- 3.2 Classification of Fuzzy Clustering Algorithms -- 3.3 The Fuzzy C-Means Algorithm -- 3.3.1 Description of the Algorithm -- 3.3.2 Assignment of New Sites to Fuzzy Clusters -- 3.4 Fuzzy Cluster Validity Measures -- 3.5 Example of Using Fuzzy C-Means Algorithm for Regionalization -- 3.5.1 Feature Extraction -- 3.5.2 Results from Fuzzy C-means Algorithm -- 3.5.3 Testing the Regions for Robustness -- 3.6 Concluding Comments -- 4 Regionalization by Artificial Neural Networks -- 4.1 Introduction -- 4.2 Kohonen Self-Organizing Feature Maps (SOFMs) -- 4.2.1 Algorithm of Kohonen Self-Organizing Feature Map -- 4.3 Example of Using SOFMs for Regionalization -- 4.3.1 Features Used -- 4.3.2 Results from SOFM -- 4.3.3 Testing the Regions for Robustness -- 4.4 Regionalization by Two-Stage Clustering of SOFM -- 4.4.1 Introduction -- 4.4.2 Algorithm for Fuzzy Clustering of Kohonen SOFM -- 4.4.3 Example of Using Two-Level Fuzzy SOFM -- 4.5 Concluding Comments -- 5 Effect of Regionalization on Flood Frequency Analysis -- 5.1 Introduction -- 5.2 Regional Index Flood Method Based on L-Moments -- 5.2.1 Introduction -- 5.2.2 Regional L-Moment Method -- 5.2.3 At-Site and Regional Parameter Estimation -- 5.3 Regional Regression Analysis -- 5.3.1 Introduction -- 5.3.2 GLS Regional Regression Results -- 5.4 Combination of GLS Regional Regression and L-Moment Method -- 5.5 Comparative Analysis -- 5.5.1 Split Sample Test for the First Method -- 5.5.2 Split Sample Test for the Second Method -- 5.5.3 Split Sample Test for the Third Method -- 5.5.4 Comparison of the Three Methods -- 5.6 Simple Scaling in Regionalized Watersheds -- 5.7 Probability Distributions for Flood Frequency Analysis in Regionalized Watersheds -- 5.7.1 Parameter Estimation -- 5.7.2 Quantile Estimation -- 5.7.3 Probability Distributions -- 5.7.4 Data Analysis -- 5.7.5 Dimensionless and Standardized Quantile Measures -- 5.8 Concluding Comments -- 6 Concluding Remarks -- 6.1 General Remarks on Clustering Approach to Region Summary Design of water control structures, reservoir management, economic evaluation of flood protection projects, land use planning and management, flood insurance assessment, and other projects rely on knowledge of magnitude and frequency of floods. Often, estimation of floods is not easy because of lack of flood records at the target sites. Regional flood frequency analysis (RFFA) alleviates this problem by utilizing flood records pooled from other watersheds, which are similar to the watershed of the target site in flood characteristics. Clustering techniques are used to identify group(s) of wate Analysis civiele techniek civil engineering ruimtelijke ordening physical planning regionale planning regional planning engineering fysica physics aardwetenschappen earth sciences hydrogeologie hydrogeology patroonherkenning pattern recognition toegepaste statistiek applied statistics Geology (General) Geologie (algemeen) Bibliography Includes bibliographical references (pages 223-232) and index Notes Print version record Subject Flood forecasting -- Statistical methods Floods -- Mathematical models Watershed management -- Mathematical models Cluster analysis. SCIENCE -- Earth Sciences -- Limnology. SCIENCE -- Earth Sciences -- Hydrology. Environnement. Sciences de la terre. Cluster analysis Flood forecasting -- Statistical methods Floods -- Mathematical models Watershed management -- Mathematical models Form Electronic book Author Srinivas, V. V., 1972- ISBN 9781402068522 1402068522 9781402068515 1402068514 1281397458 9781281397454

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