Modelling climate-weather change process including extreme weather hazards for critical infrastructure operating area

• Autorzy: Kołowrocki K. , Soszyńska-Budny J.
• Języki publikacji: EN

Abstrakty

EN

The climate-weather change process for the critical infrastructure operating area is considered and its states are introduced. The semi-Markov process is used to construct a general probabilistic model of the climate-weather change process for the critical infrastructure operating area. To build this model the vector of probabilities of the climate-weather change process staying at the initials climate-weather states, the matrix of probabilities of the climate-weather change process transitions between the climate-weather states, the matrix of conditional distribution functions and the matrix of conditional density functions of the climate-weather change process conditional sojourn times at the climate-weather states are defined. To describe the climate-weather change process conditional sojourn times at the particular climate-weather states the uniform distribution, the triangular distribution, the double trapezium distribution, the quasi-trapezium distribution, the exponential distribution, the Weibull distribution, the chimney distribution and the Gamma distribution are suggested and introduced.

Słowa kluczowe

EN

climate-weather states; climate-weather change process; modelling; extreme weather hazards

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2016
• Tom: Vol. 7, No. 3
• Strony: 149--154
• Opis fizyczny: Bibliogr. 19 poz., wykr.

Twórcy

autor

Kołowrocki K.

• Maritime University, Gdynia, Poland

autor

Soszyńska-Budny J.

• Gdynia Maritime University, Gdynia, Poland

Bibliografia

• [1] Barbu, V. & Limnios, N. (2006). Empirical estimation for discrete-time semi-Markov processes with applications in reliability. Journal of Nonparametric Statistics 18, 7-8, 483-498.
• [2] Collet, J. (1996). Some remarks on rare-event approximation. IEEE Transactions on Reliability 45, 106-108.
• [3] EU-CIRCLE Report D2.1-GMU3. (2016). Modelling Climate-Weather Change Process Including Extreme Weather Hazards.
• [4] EU-CIRCLE Report D2.3-GMU2. (2016). Identification Methods and Procedures of Climate-Weather Change Process Including Extreme Weather Hazards.
• [5] EU-CIRCLE Report D3.3-GMU1. (2016). Modelling inside dependences influence on safety of multistate ageing systems – Modelling safety of multistate ageing systems.
• [6] EU-CIRCLE Report D3.3-GMU12. (2017). Integration of the Integrated Model of Critical Infrastructure Safety (IMCIS) and the Critical Infrastructure Operation Process General Model (CIOPGM) into the General Integrated Model of Critical Infrastructure Safety (GIMCIS) related to operating environment threads (OET) and climate-weather extreme hazards (EWH).
• [7] Ferreira, F. & Pacheco, A. (2007). Comparison of level-crossing times for Markov and semi-Markov processes. Statistics and Probability Letters 7, 2, 151-157.
• [8] Glynn, P. W. & Haas, P. J. (2006). Laws of large numbers and functional central limit theorems for generalized semi-Markov processes. Stochastic Models 22, 2, 201-231.
• [9] Grabski, F. (2002). Semi-Markov Models of Systems Reliability and Operations Analysis. System Research Institute, Polish Academy of Science (in Polish).
• [10] Habibullah, M. S., Lumanpauw, E., Kolowrocki, K. et al. (2009). A computational tool for general model of operation processes in industrial systems operation processes. Electronic Journal Reliability & Risk Analysis: Theory & Applications 2, 4, 181-191.
• [11] Helvacioglu, S. & Insel, M. (2008). Expert system applications in marine technologies. Ocean Engineering 35, 11-12, 1067-1074.
• [12] Jakusik, E., Kołowrocki, K., Kuligowska, E. et al. (2016). Modelling climate-weather change process including extreme weather hazards for oil piping transportation system. Journal of Polish Safety and Reliability Association, Summer Safety & Reliability Seminars, 7, 3, 31-40.
• [13] Kołowrocki, K. (2004). Reliability of Large Systems. Elsevier, ISBN: 0080444296.
• [14] Kołowrocki, K. (2014). Reliability of large and complex systems. Elsevier, ISBN: 978080999494.
• [15] Kolowrocki, K. & Soszynska-Budny, J. (2011). Reliability and Safety of Complex Technical Systems and Processes: Modeling-Identification Prediction-Optimization. Springer, ISBN: 9780857296931.
• [16] Limnios, N. & Oprisan, G. (2005). Semi-Markov Processes and Reliability. Birkhauser, Boston.
• [17] Limnios, N., Ouhbi, B. & Sadek, A. (2005). Empirical estimator of stationary distribution for semi-Markov processes. Communications in Statistics-Theory and Methods 34, 4, 987-995.
• [18] Macci, C. (2008). Large deviations for empirical estimators of the stationary distribution of a semi-Markov process with finite state space. Communications in Statistics-Theory and Methods 37, 19, 3077-3089.
• [19] Mercier, S. (2008). Numerical bounds for semi-Markovian quantities and application to reliability. Methodology and Computing in Applied Probability 10, 2, 179-198.