Real-time decision support in the face of emerging natural hazard events

Annett Anders

    Research output: Book/ReportPh.D. thesis

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    Abstract

    Engineering structures are designed to resist a certain range of intensities of
    natural hazards. However, they are not designed to resist the entire range
    of possible intensities due to technical and economic constraints. Instead,
    in cases where they are most likely to fail as a result of emerging hazard
    events, several actions are undertaken to minimize possible consequences in
    real-time. For example, a dike is built to protect inhabitants and properties
    against flood events up to a certain return period. In extreme rain storm
    events where dike failure is likely, persons and movable property can be
    evacuated or temporary physical protections can be built. Such measures,
    when deemed prudent or necessary, are recommended or ordered by public
    authorities but also voluntarily undertaken by individuals. Other examples
    where private sector agents are in charge include engineering facilities such
    as wind turbines, agricultural facilities and offshore platforms. Operators of
    these facilities are often required to make decisions regarding the continued
    operations of their facilities in extreme storm events. These decisions, which
    in the present thesis are called real-time decisions, are often made by a small
    number of people in extremely stressful situations, ad-hoc relying on personal
    experiences of decision makers.
    On the other hand, recent advancements of information technology potentially
    make it possible for decision makers to access various types of information
    in real-time. Remarkable examples that facilitate real-time decision
    making in emerging natural hazard events are weather observation systems
    at the global scale, observation data processing systems, provision of best
    estimates of current atmospheric states and weather forecasts. However, the
    information provided is in most cases limited to the estimate of the current
    intensity of the emerging hazard event and the forecast thereof, and includes,
    in very limited cases, the prediction of risks. Yet, none of the cases seem to
    systematically utilize such information for the decision optimization of the
    choice and commencement of risk reduction measures in real-time. Consequently, unnecessary costs and losses may occur. However, systematic use
    of such information on a decision support system would not only alleviate the stress of decision makers but also facilitate the identification of optimal
    decisions, thereby avoiding unnecessary costs and losses. Motivated by these
    factors, the present thesis aims at developing a framework for the decision
    support system for real-time decision making in emerging natural hazard
    events. The thesis also demonstrates the implementation of the developed
    framework to illustrate its use and advantages.
    The developed framework is based on the work by Nishijima et al. (2009).
    They formulate the general framework concept; however, it lacks an algorithm
    that solves the optimization problem with sufficient speed so that it can be utilized in practice. The difficulty lies in the sequential nature of the
    optimization problem, which requires backward induction. Respecting the
    analogy between the considered decision problem and the American option
    pricing, the present work proposes a very efficient algorithm on the basis
    of the Least Squares Monte Carlo method (LSM), which has been developed
    as an algorithm for pricing American options. The main contribution
    of the present work is the development of the efficient algorithm based on
    LSM, which is called enhanced LSM (eLSM). As shown in the examples the
    efficiency of the proposed algorithm is up to the order of 100. Due to its efficiency it becomes possible to utilize decision support systems for variety of
    real-time decision problems. Moreover, whereas the algorithm is developed
    primarily aiming at applications to the real-time decision making in emerging
    natural hazard events, the algorithm can be straightforwardly applied for
    other types of decision problems that share the same decision problem characteristics. These include decision problems in quality control and structural
    health monitoring.
    Original languageEnglish
    Place of PublicationKgs. Lyngby
    PublisherTechnical University of Denmark, Department of Civil Engineering
    Number of pages182
    ISBN (Print)9788778773876
    Publication statusPublished - 2013
    SeriesDTU Civil Engineering Report
    ISSN1601-2917

    Bibliographical note

    Ph.D. Thesis R-301

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