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Estimating resilience for water resources systems
Author(s) -
Li Yi,
Lence Barbara J.
Publication year - 2007
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2006wr005636
Subject(s) - bivariate analysis , resilience (materials science) , probabilistic logic , computer science , reliability (semiconductor) , range (aeronautics) , autoregressive model , mathematical optimization , importance sampling , domain (mathematical analysis) , lag , time domain , reliability engineering , econometrics , mathematics , statistics , engineering , artificial intelligence , machine learning , monte carlo method , mathematical analysis , computer network , power (physics) , physics , quantum mechanics , computer vision , thermodynamics , aerospace engineering
Resilience characterizes the recovery capacity of repairable systems from the failure state to the safe state. Resilience has been recognized as a meaningful probabilistic indicator for evaluating risk‐cost trade‐offs in water resources systems. Traditionally, the resilience in the discrete time domain is estimated by sampling methods, which have a high computational expense. No single approximation approach has been well developed for estimating resilience, even under stationary conditions. This paper proposes two practical approximation methods for estimating the lag‐1 resilience in the discrete time domain. Both methods are theoretical developments, one based on a bivariate normal distribution, and the other based on a stochastic linear prediction of the performance function using the mean point of the failure domain. The foundations of both methods are the first‐order reliability method and the periodic vector autoregressive moving‐average time series model. The methods are robust for a wide range of problem characteristics and are applicable for systems facing stationary or nonstationary input conditions.