Civil & Environmental Engineering

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    Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines
    (2018-04-16) Xue, Yuan; Forman, Barton; Reichle, Rolf; Forman, Barton
    To estimate snow mass across North America, multi-frequency brightness temperature (Tb) observations collected by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) from 2002 to 2011 were assimilated into the Catchment land surface model using a support vector machine (SVM) as the observation operator as part of a one-dimensional ensemble Kalman filter. The performance of the assimilation system is evaluated through comparisons against ground-based measurements and publicly-available reference SWE and snow depth products. Assimilation estimates agree better with ground-based snow depth measurements than model-only (“open loop”, or OL) estimates in approximately 82% (56 out of 62) of pixels that are colocated with at least two ground-based stations. In addition, assimilation estimates tend to agree better with all snow products over tundra snow, alpine snow, maritime snow, as well as sparsely-vegetated snow-covered pixels. Improvements in snow mass via assimilation translate into improvements in cumulative runoff estimates when compared against discharge measurements in 11 out of 13 major snow-dominated basins in Alaska. These results prove that a SVM can serve as an efficient and effective observation operator for snow mass estimation within a radiance assimilation system.
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    Agent-Based Models of Highway Investment Processes: Forecasting Future Networks under Public and Private Ownership Regimes
    (2012) Yusufzyanova, Dilya; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The present highway funding system, especially fuel taxes, may become a less reliable revenue source in the future, while the transportation public agencies do not have sufficient financial resources needed to meet the increasing traffic demand. In the last two decades there has been increasing interest in utilizing private sector to develop, finance and operate new and existing roadways in the United States. While transportation privatization projects have shown signs of success, it is not always clear how to measure the true benefits associated with these projects for all stakeholders, including the public sector, the private sector and the public. "Win-win" privatization agreements are tricky to make due to conflicting nature of the various stakeholders involved. Therefore, there is a huge need to study the welfare impacts of various road privatization arrangements for the society as a whole, and the financial implications for private investors and public road authorities. In order to address these needs, first, an empirical analysis is performed to study the investment decision processes of public transportation agencies. Second, the agent-based decision-making model is developed to consider transportation investment processes at different levels of government which forecasts future transportation networks and their performance under both existing and alternative transportation planning processes. Third, various highway privatization schemes currently practiced in the U.S. are identified and an agent-based model for analyzing regulatory policies on private-sector transportation investments is developed. Fourth, the above mentioned models are demonstrated on the networks with grid and beltway topologies to study the impacts of topology configuration on the privatization arrangements. Based on the simulation results of developed models, a number of insights are provided about impacts of ownership structures on the socio-economic performance in transportation systems and transportation network changes over time. The proposed models and the approach can be used in long-run prediction of economic performance intended for describing a general methodology for transportation planning on large networks. Therefore, this research is expected to contribute significantly to the understanding and selecting proper road privatization programs on public networks.