Simulation-based collaborative decision support for surge floods prevention in St. Petersburg

Ivanov, Sergey V.; Kosukhin, Sergey S.; Kaluzhnaya, Anna V.; Boukhanovsky, Alexander V.

doi:10.1016/j.jocs.2012.08.005

Cited by 28 publications

(10 citation statements)

References 7 publications

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“…The average of the b-value across all the five stages of model testing was estimated to be 0.637 and the average error percentage was estimated to be 4.80%. Further, the proposed method was compared with other existing approaches [9,30,59,67] for group decision support for the supplier selection problem. The first approach was based on the fuzzy AHP for group decision making and selected suppliers through a ranking method based on geometric mean method only.…”

Section: Resultsmentioning

confidence: 99%

“…Although a wide variety of decision support theories has been used in the domain of supplier selection, there has been lesser exploration of decision support theories on group decision making has been explored in the supplier selection domain. For complex decision making problems, collective intelligence of a group of decision makers in consensus often is more effective than the same decision makers working in isolation [47,53,67]. In fact, group decision making under consensus has greater reliability, consistency and regularity than decision makers working in isolation for even the same prioritization problem [9,25,45].…”

Section: Introductionmentioning

confidence: 99%

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A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network

Kar

2015

Journal of Computational Science

148

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network

Kar

2015

Journal of Computational Science

148

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“…), interaction with decision makers etc. [41]. Thus, the improvement of forecast quality is a significant task a) to predict floods as early as possible; b) to perform simulation-based assessment and optimization of developed plans and final decision-making support.…”

Section: Problem Statementmentioning

confidence: 99%

Classification issues within ensemble-based simulation: application to surge floods forecasting

Kovalchuk

Krikunov

Knyazkov

et al. 2016

Stoch Environ Res Risk Assess

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Contemporary tasks of complex system simulation are often related to the issue of uncertainty management. It comes from the lack of information or knowledge about the simulated system as well as from restrictions of the model set being used. One of the powerful tools for the uncertainty management is ensemble-based simulation, which uses variation in input or output data, model parameters, or available versions of models to improve the simulation performance. Furthermore, the system of models for complex system simulation (especially in case of hiring ensemble-based approach) can be considered as a complex system. As a result, the identification of the complex model's structure and parameters provide additional sources of uncertainty to be managed. Within the presented work, we are developing a conceptual and technological approach to manage the ensemblebased simulation taking into account changing states of both simulated system and system of models within the ensemble-based approach. The states of these systems are considered as a subject of classification with consequent inference of better strategies for ensemble evolution over the simulation time and ensemble aggregation. Here the ensemble evolution enables implementation of dynamic reactive solutions that can automatically conform to the changing states of both systems. The ensemble aggregation can be considered within a scope of averaging (regression way) or selection (classification way, which complement the classification mentioned earlier) approach. The technological basis for such approach includes ensemble-based simulation techniques using domain-specific software combined within a composite application; data science approaches for analysis of available datasets (simulation data, observations, situation assessment, etc.); and machine learning algorithms for classes identification, ensemble management and knowledge acquisition. Within the work, a set of case studies is addressed to examine the opportunities provided by the developed approach: metocean events' forecasting simulation, urban traffic environment, multi-agent crowd simulation, etc.

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“…Among the other tasks, which require simulation, decision support system can be mentioned as a complex example which involves different type of users interacting with the system and with each other. An application for collaborative decision support for the task of surge floods prevention in St. Petersburg [14] was developed using touch table hardware for collaboration ( fig. 4a).…”

Section: Touch Table Application: An Examplementioning

confidence: 99%

Knowledge-Based Expressive Technologies Within Cloud Computing Environments

Kovalchuk

Smirnov

Knyazkov

et al. 2014

Advances in Intelligent Systems and Computing

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Presented paper describes the development of comprehensive approach for knowledge processing within e-Sceince tasks. Considering the task solving within a simulation-driven approach a set of knowledge-based procedures for task definition and composite application processing can be identified. This procedures could be supported by the use of domain-specific knowledge being formalized and used for automation purpose. Within this work the developed conceptual and technological knowledge-based toolbox for complex multidisciplinary task solving support is proposed. Using CLAVIRE cloud computing environment as a core platform a set of interconnected expressive technologies were developed.

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Simulation-based collaborative decision support for surge floods prevention in St. Petersburg

Cited by 28 publications

References 7 publications

A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network

A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network

Classification issues within ensemble-based simulation: application to surge floods forecasting

Knowledge-Based Expressive Technologies Within Cloud Computing Environments

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