Most of the time series analysis methods applied today rely heavily on the key assumptions of linearity, Gaussianity and stationarity. Natural time series, including hydrologic, climatic and environmental time series, which satisfy these assumptions seem to be the exception rather than the rule. Nevertheless, most time series analysis is performed using standard methods after relaxing the required conditions one way or another, in the hope that the departure from these assumptions is not large enough to affect the result of the analysis. A large amount of data is available today after almost a century of intensive data collection of various natural time series. In addition to a few older data series such as sunspot numbers, sea surface temperatures, etc., data obtained through dating techniques (tree-ring data, ice core data, geological and marine deposits, etc.), are available. With the advent of powerful computers, the use of simplified methods can no longer be justified, especially with the success of these methods in explaining the inherent variability in natural time series. This book presents a number of new techniques that have been discussed in the literature during the last two decades concerning the investigation of stationarity, linearity and Gaussianity of hydrologic and environmental times series. These techniques cover different approaches for assessing nonstationarity, ranging from time domain analysis, to frequency domain analysis, to the combined time-frequency and time-scale analyses, to segmentation analysis, in addition to formal statistical tests of linearity and Gaussianity. It is hoped that this endeavor would facilitate further research into this important area.

The Hilbert-Huang Transform ((HHT) is a recently developed technique which is used to analyze nonstationary data. Hydrologic and environmental series are, in the main, analyzed by using techniques which were developed for stationary data. This has led to problems of interpretation of the results. Environmental and hydrologic series are quite often nonstationary. The basic objective of the material discussed in this book is to analyze these data by using methods based on the Hilbert-Huang transform. These results are compared to the results from the traditional methods such as those based on Fourier transform and other classical statistical tests.

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.

R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy.

Written by a sociologist, a graph theorist, and a statistician, this title provides social network analysts and students with a solid statistical foundation from which to analyze network data. Clearly demonstrates how graph-theoretic and statistical techniques can be employed to study some important parameters of global social networks. The authors uses real life village-level social networks to illustrate the practicalities, potentials, and constraints of social network analysis ( SNA ). They also offer relevant sampling and inferential aspects of the techniques while dealing with potentially large networks.

Intended Audience This supplemental text is ideal for a variety of graduate and doctoral level courses in social network analysis in the social, behavioral, and health sciences "

Most of the time series analysis methods applied today rely heavily on the key assumptions of linearity, Gaussianity and stationarity. Natural time series, including hydrologic, climatic and environmental time series, which satisfy these assumptions seem to be the exception rather than the rule. Nevertheless, most time series analysis is performed using standard methods after relaxing the required conditions one way or another, in the hope that the departure from these assumptions is not large enough to affect the result of the analysis.
A large amount of data is available today after almost a century of intensive data collection of various natural time series. In addition to a few older data series such as sunspot numbers, sea surface temperatures, etc., data obtained through dating techniques (tree-ring data, ice core data, geological and marine deposits, etc.), are available. With the advent of powerful computers, the use of simplified methods can no longer be justified, especially with the success of these methods in explaining the inherent variability in natural time series.
This book presents a number of new techniques that have been discussed in the literature during the last two decades concerning the investigation of stationarity, linearity and Gaussianity of hydrologic and environmental times series. These techniques cover different approaches for assessing nonstationarity, ranging from time domain analysis, to frequency domain analysis, to the combined time-frequency and time-scale analyses, to segmentation analysis, in addition to formal statistical tests of linearity and Gaussianity. It is hoped that this endeavor would facilitate further research into this important area.

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 Hilbert-Huang Transform (HHT) is a recently developed technique used to analyze nonstationary data. This book uses methods based on the Hilbert-Huang Transform to analyze hydrological and environmental time series. These results are compared to the results from the traditional methods such as those based on Fourier transform and other classical statistical tests.

R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy.