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Service-Oriented Tsunami Modeling: VMVC-Based Functional Engines

  • Kensaku HayashiEmail author
  • Alexander Vazhenin
  • Andrey Marchuk
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 513)

Abstract

Lessons learned from the Great Japanese Earthquake and Tsunami provide direction to research and emergency management communities on how to develop tools, models, and methods for mitigating impact for such devastating event both locally and globally. The solution of this problem is that it is more effective to integrate the applications and services rather than rebuilding because redevelopment is a costly affaire. The presented paper demonstrates an approach for developing the service-oriented Tsunami Modeling Environment as a framework of the original Virtual Model-View-Controller (VMVC) design pattern. It is based on decoupling of the view from the mode. The Model-View link is redirected within an enhanced controller as a virtual layer for distributed and service-oriented applications. This allows the programmers to concentrate on building new functionalities and services without bothering on how the services will be exposed, consumed, and maintained. To simplify the structure of services, the Model is represented as a set of application-oriented components named Engines. We are describing the main Tsunami Modeling Functional engines allowing to model each stage of a tsunami process including tsunami wave propagation over the deep ocean water, inundation of these waves on the coast area, and impaction on the coast object. We are also describing in detail the Tsunami Visualizing Engine (TVE) showing the modeling results in a convenient multimedia form. For each engine, we are showing its functionality and corresponding services that are provided by it.

Keywords

Wave Height Tsunami Wave Modeling Engine Tsunami Warning Bathymetry Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kensaku Hayashi
    • 1
    Email author
  • Alexander Vazhenin
    • 1
  • Andrey Marchuk
    • 2
  1. 1.University of AizuAizuwakamatsuJapan
  2. 2.Institute of Computational Mathematics and Mathematical Geophysics SD RASNovosibirskRussia

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