Stochastic Modeling Concepts in Groundwater and Risk Assessment: Potential Application to Marine Problems |
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| Institution: | 1. The Fourth People''s Hospital of Chengdu, Chengdu, Sichuan, China;2. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China;3. School of Physics, University of Electronic Science and Technology of China, Chengdu, Sichuan, China;4. Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA;1. University of Michigan, College of Engineering, Department of Biomedical Engineering, Cellular Engineering & Nano-Therapeutics Laboratory, Ann Arbor, MI 48109, USA;2. Istanbul Medipol University, School of Engineering and Natural Sciences, Department of Biomedical Engineering, 34810 Istanbul, Turkey;3. University of Michigan, School of Medicine, Department of Internal Medicine, Ann Arbor, MI 48109, USA;4. University of Michigan, Macromolecular Science and Engineering Program, 2300 Hayward Avenue, Ann Arbor, MI 48109, USA |
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| Abstract: | Parameter uncertainty is ubiquitous in marine environmental processes. Failure to account for this uncertainty may lead to erroneous results, and may have significant environmental and economic ramifications. Stochastic modeling of oil spill transport and fate is, therefore, central in the development of an oil spill contingency plan for new oil and gas projects. Over the past twenty years, several stochastic modeling tools have been developed for modeling parameter uncertainty, including the spectral, perturbation, and simulation methods. In this work we explore the application of a new stochastic methodology, the first-order reliability method (FORM), in oil spill modeling. FORM was originally developed in the structural reliability field and has been recently applied to various environmental problems. The method has many appealing features that makes it a powerful tool for modeling complex environmental systems. The theory of FORM is presented, identifying the features that distinguish the method from other stochastic tools. Different formulations to the reliability-based stochastic oil spill modeling are presented in a decision-analytic context. |
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