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Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

Bilateral contracts are important risk-hedging instruments constituting a major component in the portfolios held by many electric power market participants. However, bilateral contract negotiation is a complicated process as it involves risk management, strategic bargaining, and multi-market participation. This study analyzes a financial bilateral contract negotiation process between a generation company and a load-serving entity in a wholesale electric power market with congestion managed by locational marginal pricing. Nash bargaining theory is used to model a Pareto-efficient settlement point. The model predicts negotiation outcomes under various conditions and identifies circumstances in which the two parties might fail to reach an agreement. Both analysis and simulation are used to gain insight regarding how these negotiation outcomes systematically vary in response to changes in the participants' risk preferences and price biases. [ABSTRACT FROM PUBLISHER]

We provide a systematic study of the problem of finding the source of a rumor in a network. We model rumor spreading in a network with the popular susceptible-infected (SI) model and then construct an estimator for the rumor source. This estimator is based upon a novel topological quantity which we term rumor centrality. We establish that this is a maximum likelihood (ML) estimator for a class of graphs. We find the following surprising threshold phenomenon: on trees which grow faster than a line, the estimator always has nontrivial detection probability, whereas on trees that grow like a line, the detection probability will go to 0 as the network grows. Simulations performed on synthetic networks such as the popular small-world and scale-free networks, and on real networks such as an internet AS network and the U.S. electric power grid network, show that the estimator either finds the source exactly or within a few hops of the true source across different network topologies. We compare rumor centrality to another common network centrality notion known as distance centrality. We prove that on trees, the rumor center and distance center are equivalent, but on general networks, they may differ. Indeed, simulations show that rumor centrality outperforms distance centrality in finding rumor sources in networks which are not tree-like. [ABSTRACT FROM PUBLISHER]

This paper proposes methodologies to estimate the electric energy and power consumption by light-duty plug-in electric vehicles (PEVs). Using the travel patterns of light-duty vehicles in the U.S. obtained from the 2009 National Household Travel Survey, the PEVs' energy and power consumption are estimated for two uncontrolled charging scenarios. [ABSTRACT FROM PUBLISHER]