No / River morphological processes are among the most complex and least understood phenomenon in nature. Recent research indicates that the braiding of marine waterways of the estuary zone occurs at an aspect ratio similar to the alluvial braided river. The instability of complex sporadic fluvial processes at river-sea interface is responsible for bar formation in alluvial as well as in marine waterbodies Due to the lack of knowledge of flow characteristics around bar, the flow structure around the sand bar is analyzed. The bursting events play the crucial role in understanding the fluvial characteristics in the vicinity of submerged structure. The study of bursting events around the mid-channel bar is only done by the present author. The effect of submergence ratio on the turbulence behavior in the proximity of bar is analyzed in this study. The flow turbulence generated by the mid-channel bar is also analyzed in detail. The extreme turbulent burst is segregated from low intensity turbulent events by using the hole size concept. The effect of hole size on the parameter Dominance Function is analysed which is not yet studied by any researcher for mid-channel bar. The Momentum Dominance Function (MDF) parameter increases with increase in the Hole Size. This indicates that the magnitude of upward flux increases with increase in the hole size. The effect of bar height on the turbulent burst which is not yet studied by any researchers is analyzed in the present research. The joint probability distribution of bursting events is modeled using the Gram-Charlier bivariate joint probability function. The joint probability distribution gives the details of probabilistic structure of flow in the vicinity of bar. The effect of bar is predominant only in the lower flow layer. The joint probability distribution graph becomes more eccentric toward the dominant quadrants with increase in the submergence ratio. This indicates that the probability of dominant events further increases with increase in the submergence ratio.

Since past more than hundred years, fluvial geomorphologists all across the globe have been trying to understand the basic phenomena and processes that control the behavioral patterns of streams. A large number of stream classification systems has been proposed till date, but none of them have been accepted universally. Lately, a large amount of efforts have been made to develop bankfull relations for estimating channel geometry that can be employed for stream restoration practices. Focusing on these two objectives, in this study a new stream classification system based on elevation above mean sea level has been developed and later using elevation as one of the independent and nondimensionalising parameters, universal and regional regime equations in dimensionless forms have been developed for predicting channel geometry at bankfull conditions. To accomplish the first objective, 873 field measurement values describing the hydraulic geometry and morphology of streams mainly from Canada, UK and USA were compiled and statistically analyzed. Based on similar mode values of three dimensionless channel variables (aspect ratio, sinuosity and channel slope), several fine elevations ranges were merged to produce the final five elevation ranges. These final five zones formed the basis of the new elevation based classification system and were identified with their unique modal values of dimensionless variables. Performing joint probability distributions on each of these zones, trends in the behavior of channel variables while moving from lowland to upland were observed. For the completion of second objective, 405 data points out of initial 873 points were selected and employed for the development of bankfull relations by using bankfull discharge and watershed variables as the input variables. Regression equations developed for width and depth established bankfull discharge as the only required input variable whereas all other watershed variables were proved out to be relatively insignificant. Channel slope equation did not show any dependence on bankfull discharge and was observed to be influenced only by drainage area and valley slope factors. Later when bankfull discharge was replaced by annual average rainfall as the new input variable, watershed parameters (drainage area, forest cover, urban cover etc.) became significant in bankfull width and depth regression equations. This suggested that bankfull discharge in itself encompasses the effects of all the watershed variables and associated processes and thus is sufficient for estimating channel dimensions. Indeed, bankfull discharge based regression equation demonstrated its strong dependence on watershed and rainfall variables. / Master of Science

In the field of cognitive science, as well as the area of Artificial Intelligence (AI), the role of context has been investigated in many forms, and for many purposes. It is clear in both areas that consideration of contextual information is important. However, the significance of context has not been emphasized in the Bayesian networks literature. We suggest that consideration of context is necessary for acquiring knowledge about a situation and for refining current representational models that are potentially erroneous due to hidden independencies in the data.<p>In this thesis, we make several contributions towards the automation of contextual consideration by discovering useful contexts from probability distributions. We show how context-specific independencies in Bayesian networks and discovery algorithms, traditionally used for efficient probabilistic inference can contribute to the identification of contexts, and in turn can provide insight on otherwise puzzling situations. Also, consideration of context can help clarify otherwise counter intuitive puzzles, such as those that result in instances of Simpson's paradox. In the social sciences, the branch of attribution theory is context-sensitive. We suggest a method to distinguish between <i>dispositional causes</i> and <i>situational factors</i> by means of contextual models. Finally, we address the work of Cheng and Novick dealing with causal attribution by human adults. Their <i>probabilistic contrast model</i> makes use of contextual information, called focal sets, that must be determined by a human expert. We suggest a method for discovering complete <i>focal sets</i> from probabilistic distributions, without the human expert.

In the field of cognitive science, as well as the area of Artificial Intelligence (AI), the role of context has been investigated in many forms, and for many purposes. It is clear in both areas that consideration of contextual information is important. However, the significance of context has not been emphasized in the Bayesian networks literature. We suggest that consideration of context is necessary for acquiring knowledge about a situation and for refining current representational models that are potentially erroneous due to hidden independencies in the data.<p>In this thesis, we make several contributions towards the automation of contextual consideration by discovering useful contexts from probability distributions. We show how context-specific independencies in Bayesian networks and discovery algorithms, traditionally used for efficient probabilistic inference can contribute to the identification of contexts, and in turn can provide insight on otherwise puzzling situations. Also, consideration of context can help clarify otherwise counter intuitive puzzles, such as those that result in instances of Simpson's paradox. In the social sciences, the branch of attribution theory is context-sensitive. We suggest a method to distinguish between <i>dispositional causes</i> and <i>situational factors</i> by means of contextual models. Finally, we address the work of Cheng and Novick dealing with causal attribution by human adults. Their <i>probabilistic contrast model</i> makes use of contextual information, called focal sets, that must be determined by a human expert. We suggest a method for discovering complete <i>focal sets</i> from probabilistic distributions, without the human expert.

Investments in space systems are substantial, indivisible, and irreversible, characteristics that make them high-risk, especially when coupled with an uncertain demand environment. Traditional approaches to system design and acquisition, derived from a performance- or cost-centric mindset, incorporate little information about the spacecraft in relation to its environment and its value to its stakeholders. These traditional approaches, while appropriate in stable environments, are ill-suited for the current, distinctly uncertain and rapidly changing technical, and economic conditions; as such, they have to be revisited and adapted to the present context. This thesis proposes that in uncertain environments, decision-making with respect to space system design and acquisition should be value-based, or at a minimum value-informed. This research advances the value-centric paradigm by providing the theoretical basis, foundational frameworks, and supporting analytical tools for value assessment of priced and unpriced space systems. For priced systems, stochastic models of the market environment and financial models of stakeholder preferences are developed and integrated with a spacecraft-sizing tool to assess the system's net present value. The analytical framework is applied to a case study of a communications satellite, with market, financial, and technical data obtained from the satellite operator, Intelsat. The case study investigates the implications of the value-centric versus the cost-centric design and acquisition choices. Results identify the ways in which value-optimal spacecraft design choices are contingent on both technical and market conditions, and that larger spacecraft for example, which reap economies of scale benefits, as reflected by their decreasing cost-per-transponder, are not always the best (most valuable) choices. Market conditions and technical constraints for which convergence occurs between design choices under a cost-centric and a value-centric approach are identified and discussed. In addition, an innovative approach for characterizing value uncertainty through partial moments, a technique used in finance, is adapted to an engineering context and applied to priced space systems. Partial moments disaggregate uncertainty into upside potential and downside risk, and as such, they provide the decision-maker with additional insights for value-uncertainty management in design and acquisition. For unpriced space systems, this research first posits that their value derives from, and can be assessed through, the value of information they provide. To this effect, a Bayesian framework is created to assess system value in which the system is viewed as an information provider and the stakeholder an information recipient. Information has value to stakeholders as it changes their rational beliefs enabling them to yield higher expected pay-offs. Based on this marginal increase in expected pay-offs, a new metric, Value-of-Design (VoD), is introduced to quantify the unpriced system's value. The Bayesian framework is applied to the case of an Earth Science satellite that provides hurricane information to oil rig operators using nested Monte Carlo modeling and simulation. Probability models of stakeholders' beliefs, and economic models of pay-offs are developed and integrated with a spacecraft payload generation tool. The case study investigates the information value generated by each payload, with results pointing to clusters of payload instruments that yielded higher information value, and minimum information thresholds below which it is difficult to justify the acquisition of the system. In addition, an analytical decision tool, probabilistic Pareto fronts, is developed in the Cost-VoD trade space to provide the decision-maker with additional insights into the coupling of a system's probable value generation and its associated cost risk.