The only book on this topic
Summarizes and illustrates the research in this topic
Brings out the benefits of regionalization
Delineates the problems which need to be addressed in this research topic
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.
|
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 watersheds which have similar flood characteristics. This book is a comprehensive reference on how to use these techniques for RFFA and is the first of its kind. It provides a detailed account of several recently developed clustering techniques, including those based on fuzzy set theory and artificial neural networks. It also documents research findings on application of clustering techniques to RFFA that remain scattered in various hydrology and water resources journals.
The optimal number of groups defined in an area is based on cluster validation measures and L-moment based homogeneity tests. These form the bases to check the regions for homogeneity.
The subjectivity involved and the effort needed to identify homogeneous groups of watersheds with conventional approaches are greatly reduced by using efficient clustering techniques discussed in this book. Furthermore, better flood estimates with smaller confidence intervals are obtained by analysis of data from homogeneous watersheds. Consequently, the problem of over- or under-designing by using these flood estimates is reduced. This leads to optimal economic design of structures. The advantages of better regionalization of watersheds and their utility are entering into hydrologic practice.
Audience:
This book will be of interest to researchers in stochastic hydrology, practitioners in hydrology and graduate students.
Preface. 1. Introduction. 2. Regionalization by hybrid cluster analysis. 3. Regionalization by fuzzy cluster analysis. 4. Regionalization by artificial neural networks. 5. Effect of regionalization on flood frequency analysis. 6. Concluding remarks. References. Index of notation. Abbreviations. Index.
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 watersheds which have similar flood characteristics. This book is a comprehensive reference on how to use these techniques for RFFA and is the first of its kind. It provides a detailed account of several recently developed clustering techniques, including those based on fuzzy set theory and artificial neural networks. It also documents research findings on application of clustering techniques to RFFA that remain scattered in various hydrology and water resources journals.
The optimal number of groups defined in an area is based on cluster validation measures and L-moment based homogeneity tests. These form the bases to check the regions for homogeneity.
The subjectivity involved and the effort needed to identify homogeneous groups of watersheds with conventional approaches are greatly reduced by using efficient clustering techniques discussed in this book. Furthermore, better flood estimates with smaller confidence intervals are obtained by analysis of data from homogeneous watersheds. Consequently, the problem of over- or under-designing by using these flood estimates is reduced. This leads to optimal economic design of structures. The advantages of better regionalization of watersheds and their utility are entering into hydrologic practice.
Audience
This book will be of interest to researchers in stochastic hydrology, practitioners in hydrology and graduate students.
Regionalization by Hybrid Cluster Analysis.- Regionalization by Fuzzy Cluster Analysis.- Regionalization by Artificial Neural Networks.- Effect of Regionalization on Flood Frequency Analysis.- Concluding Remarks.
Inhaltsverzeichnis
Preface. 1. Introduction. 2. Regionalization by hybrid cluster analysis. 3. Regionalization by fuzzy cluster analysis. 4. Regionalization by artificial neural networks. 5. Effect of regionalization on flood frequency analysis. 6. Concluding remarks. References. Index of notation. Abbreviations. Index.
Klappentext
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 watersheds which have similar flood characteristics. This book is a comprehensive reference on how to use these techniques for RFFA and is the first of its kind. It provides a detailed account of several recently developed clustering techniques, including those based on fuzzy set theory and artificial neural networks. It also documents research findings on application of clustering techniques to RFFA that remain scattered in various hydrology and water resources journals.
The optimal number of groups defined in an area is based on cluster validation measures and L-moment based homogeneity tests. These form the bases to check the regions for homogeneity.
The subjectivity involved and the effort needed to identify homogeneous groups of watersheds with conventional approaches are greatly reduced by using efficient clustering techniques discussed in this book. Furthermore, better flood estimates with smaller confidence intervals are obtained by analysis of data from homogeneous watersheds. Consequently, the problem of over- or under-designing by using these flood estimates is reduced. This leads to optimal economic design of structures. The advantages of better regionalization of watersheds and their utility are entering into hydrologic practice.
Audience:
This book will be of interest to researchers in stochastic hydrology, practitioners in hydrology and graduate students.
The only book on this topic
Summarizes and illustrates the research in this topic
Brings out the benefits of regionalization
Delineates the problems which need to be addressed in this research topic
