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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. .

Flood forecasting. --- Floods --- Watershed management --- Cluster analysis. --- Mathematical models. --- Correlation (Statistics) --- Multivariate analysis --- Spatial analysis (Statistics) --- Watershed development --- Watersheds --- Ecosystem management --- Flooding --- Inundations --- Natural disasters --- Water --- Hydrological forecasting --- Management --- Forecasting --- Hydraulic engineering. --- Statistics. --- Civil engineering. --- Optical pattern recognition. --- Regional planning. --- Hydrogeology. --- Hydrology/Water Resources. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Civil Engineering. --- Pattern Recognition. --- Landscape/Regional and Urban Planning. --- Regional development --- Regional planning --- State planning --- Human settlements --- Land use --- Planning --- City planning --- Landscape protection --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Engineering --- Public works --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Engineering, Hydraulic --- Fluid mechanics --- Hydraulics --- Shore protection --- Government policy --- Hydrology. --- Statistics . --- Pattern recognition. --- Urban planning. --- Aquatic sciences --- Earth sciences --- Hydrography --- Geohydrology --- Geology --- Hydrology --- Groundwater --- Cities and towns --- Civic planning --- Land use, Urban --- Model cities --- Redevelopment, Urban --- Slum clearance --- Town planning --- Urban design --- Urban development --- Urban planning --- Art, Municipal --- Civic improvement --- Urban policy --- Urban renewal --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception