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

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.