Urgent Computing for Operational Storm Surge Forecasting in Saint-Petersburg

Boukhanovsky, Alexander V.; Ivanov, Sergey V.

doi:10.1016/j.procs.2012.04.188

Cited by 20 publications

(7 citation statements)

References 3 publications

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“…They solve real-world problems by developing software that utilizes high-performance computing resources and e-Science infrastructures. That involves data assimilation and processing (including Big Data), Decision Support Systems, and Urgent Computing applications [30] with dynamic control over computational resources in a distributed environment [20]. Students take part in international research projects [5], [19], [25], [31], and collaborate with ICT (Information Communication Technology) companies [32].…”

Section: Research Topics Of Master's Projectsmentioning

confidence: 99%

Russian-Dutch double-degree Master’s programme in computational science in the age of global education

Krzhizhanovskaya

Dukhanov

Bilyatdinova

et al. 2015

Journal of Computational Science

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Section: Research Topics Of Master's Projectsmentioning

confidence: 99%

Russian-Dutch double-degree Master’s programme in computational science in the age of global education

Krzhizhanovskaya

Dukhanov

Bilyatdinova

et al. 2015

Journal of Computational Science

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“…This part of the workflow has been implemented in a Decision support system for flood barrier gate operation (highlighted as shown in Fig. 3, left), and described in detail in [17] and [28]. In addition, another model has been developed that simulates the under-gate flows in order to avoid potentially dangerous vibration modes, seabed scour (erosion) around the gates, and some other aspects of gate operation (ecological, economical, technical maintenance, etc.…”

Section: Urban Flood Decision Support System and Modeling Workflowmentioning

confidence: 99%

Distributed Simulation of City Inundation by Coupled Surface and Subsurface Porous Flow for Urban Flood Decision Support System

Krzhizhanovskaya¹,

Melnikova²,

Chirkin³

et al. 2013

Procedia Computer Science

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We present a decision support system for flood early warning and disaster management. It includes the models for datadriven meteorological predictions, for simulation of atmospheric pressure, wind, long sea waves and seiches; a module for optimization of flood barrier gates operation; models for stability assessment of levees and embankments, for simulation of city inundation dynamics and citizens evacuation scenarios. The novelty of this paper is a coupled distributed simulation of surface and subsurface flows that can predict inundation of low-lying inland zones far from the submerged waterfront areas, as observed in St. Petersburg city during the floods. All the models are wrapped as software services in the CLAVIRE platform for urgent computing, which provides workflow management and resource orchestration.

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“…The Flood Warning Systems (FWS) was developed to support the management of the barrier system in cases of flood danger. Its main objective is to predict water level rising in the Gulf of Finland and calculate possible scenarios for flood prevention to help the Decision Making Group to reach a decision about the barrier gates maneuvering plan [9].…”

Section: Introductionmentioning

confidence: 99%

Computational Uncertainty Management for Coastal Flood Prevention System

Kalyuzhnaya

Boukhanovsky

2015

Procedia Computer Science

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Multivariate and progressive uncertainty is the main factor of accuracy in simulation systems. It can be a critical issue for systems that forecast and prevent extreme events and related risks. To deal with this problem, computational uncertainty management strategies should be used. This paper aims to demonstrate an adaptation of the computational uncertainty management strategy in the framework of a system for prediction and prevention of such natural disasters as coastal floods. The main goal of the chosen strategy is to highlight the most significant ways of uncertainty propagation and to collocate blocks of action with procedures for reduction or evaluation of uncertainty in a way that catches the major part of model error. Blocks of action involve several procedures: calibration of models, data assimilation, ensemble forecasts, and various techniques for residual uncertainty evaluation (including risk evaluation). The strategy described in this paper was tested and proved based on a case study of the coastal flood prevention system in St. Petersburg.

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Urgent Computing for Operational Storm Surge Forecasting in Saint-Petersburg

Cited by 20 publications

References 3 publications

Russian-Dutch double-degree Master’s programme in computational science in the age of global education

Russian-Dutch double-degree Master’s programme in computational science in the age of global education

Distributed Simulation of City Inundation by Coupled Surface and Subsurface Porous Flow for Urban Flood Decision Support System

Computational Uncertainty Management for Coastal Flood Prevention System

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