Control Systems and Computers, N6, 2017, Article 9
DOI: https://doi.org/10.15407/usim.2017.06.071
Upr. sist. maš., 2017, Issue 6 (272), pp. 71-83.
UDK 574:004.2
Yurij M. Bardachov – Doctor of Technical Sciences, Professor, the Department of High Mathematics and Mathematical Modeling, rector@kntu.net.ua
Maryna V. Zharikova – PhD in Techn. Sciences, Associate Professor, the Department of Information Technologies, marina.jarikova@gmail.com
Volodymyr G. Sherstju – Doctor of Technical Sciences, Professor, the Department of Information Technologies, vgsherstyuk@gmail.com
Kherson National Technical University, Beryslavske highway, 24, Kherson, 73008, Ukraine
Event-Network Model of Distructive Processes for the Real-Time Risk-Oriented DSS
Introduction. Giving the fact that the natural emergency is a result of simultaneous influence of еру considerable number of
factors, and the evolving processes are non-linear and transient, making decisions a difficult task in the natural emergency conditions. Uncertainty, incompleteness and inconsistency of the input information, territorial distribution of the events, as well as time shortage and high responsibility embarrass decision making, which stimulate the developing of the decision making systems for natural emergency counteracting. Using the traditional approaches to developing the models of destructive processes doesn’t provide the required system performance, which stipulates the topicality of the further search of non-conventional models and methods of decision making for natural emergency counteracting.
Purpose. The purpose of the work is the development of formal plausible model of destructive process propagation, suitable for the tasks solution of the natural emergency counteracting in the real time decision making systems.
Method. The authors used the event-based approach for the developing the plausible model of the destructive process. The methods of fuzzy, probabilistic and rough sets were used to assess the likelihood of cell transitions between states.
Results. The formal plausible model of the destructive process in geoecotechnosystems is described, having the form of territorial system, which is discretized using the grid of equal cells. The model of destructive process is represented as the formalism of plausible tree network of the events modeling the transitions of cells from one state to another and allowing to assess the likelihood of such transition and the time during which such transition is anticipated. The proposed formalism allows combining the different likelihood assessments, such as fuzzy, probabilistic and rough, in the frame of one structure.
Conclusion. The proposed model can be used in decision support systems for the natural emergencies counteracting, which are based on geoinformation technologies. Using the proposed model allows increasing the efficiency of decision making in the natural emergency conditions by means of informational support.
Keywords: geoecotechnosystems, destructive process, natural emergency, decision support system.
- MILLER C, AGER A.A., 2013. “A review of recent advances in risk analysis for wildfire management”, Int. J. of Wildland Fire, 22, 1, pp. 1–14.
https://doi.org/10.1071/WF11114 - LOBODA, T., Kriszar, A., 2007. “Assessing the risk of ignition in the Russian Far East within a modeling framework of fire threat”, Ecological Applications, 17, 3, pp. 791–805.
https://doi.org/10.1890/05-1476 - NEWMAN, J.P. ET AL., 2017. “Review of literature on decision support systems for natural hazard risk reduction: current status and future directions”, Environmental modeling & software, 96, pp. 378–409.
https://doi.org/10.1016/j.envsoft.2017.06.042 - Van WESTERN, C.J., GREIVING, S., 2017. “Multi-hazard risk assessment and decision making”, Environmental hazards. Methodologies for risk assessment and management, IWA Publishing, pp. 31–94.
- CALKIN, D.E., THOMPSON, M.P., FINNEY, M.A., HYDE, K.D., 2011. “A real-time assessment tool supporting wildland fire decision making”, J. Forestry, 109, pp. 274–280.
- FINNEY, M., 1993. “Modeling the spread and behaviour of prescribed natural fires”, Proc. of the 12th Conference on Fire and Forest Meteorology, Jekill Island. Georgia: Society of American Foresters, pp. 138–143.
- FINNEY, M., 2005. “The challenge of quantitative risk analysis for wildland fire”, Forest Ecology and Management, 211, pp. 97–108.
https://doi.org/10.1016/j.foreco.2005.02.010 - LACASSE, S. et al., 2008. “Event tree analysis of Aknes rock slide hazard”, Proc. of 4th Canadian Conf. on Geohazards, Quebec, Canada, pp. 551–557.
- PEILA, D., GUARDINI, D., 2008. “Use of the event tree to assess the risk reduction obtained from rockfall protection devices”, Natural Hazards Earth System Sciences, 8, pp. 1441–1450.
https://doi.org/10.5194/nhess-8-1441-2008 - PAWLAK, Z., 1982. “Rough Sets”, Int. J. of Comp. and Inf. Sciences, 11 (5), pp. 341–356.
https://doi.org/10.1007/BF01001956 - SHERSTJUK, V., ZHARIKOVA, M., 2017. “Approximate model of spatially distributed Markov process for GIS-based decision support system”, Proc. of IEEE 12th Int. Sc. and Tech. Conf. on Comp. Sciences and Inf. Technologies (CSIT), Lviv, Ukraine, pp. 300–304.
- MARTIN, F., 2004. Case-Based Sequence Analysis in Dynamic, Imprecise, and Adversarial Domains. Ph.D. Thesis, Barcelona: Universitat Politecnica De Catalunya, 285 p.
- BISTARELLI, S., MONTANARI, U., ROSSI, F., 1997. “Semiring-Based Constraint Satisfaction and Optimization”. J. of the ACM, 44 (2), pp. 201–236.
https://doi.org/10.1145/256303.256306 - PEARL, J., 1986. “Fusion, propagation and structuring in belief networks”, Artificial Intelligence, 29 (3), pp. 241–288.
https://doi.org/10.1016/0004-3702(86)90072-X - DARWICHE, A., 2009. Modeling and Reasoning with Bayesian Networks, Cambridge: Cambridge University Press, 526 p.
https://doi.org/10.1017/CBO9780511811357 - KOCKA, T., ZHANG, N.L., 2002. “Effective Dimensions of Partially Observed Polytrees”, Proc. of The European Conf. on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp. 311–322.
- PEARL, J., 2009. Causality: Models, Reasoning, and Inference: 2-nd ed., MA.: Cambridge University Press, 464 p. https://doi.org/10.1017/CBO9780511803161
- TERZIYAN, V., VITKO, A.V., 2002. “Probabilistic metasets for solving problems of data mining”, Artificial Intelligence, 3, pp. 188-197. (In Russian).
Received 14.11.2017