Strategic River Water Quality Planning Using Calibrated Stochastic Simulation

Chiang, Pei-chih; Gates, Timothy K.

doi:10.1061/(asce)0733-9496(2004)130:3(215)

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…First, a periodic vector (or multivariate) autoregressive moving‐average (VARMA) model, which is a kind of time series model, mimics the stochastic process. For details regarding periodic VARMA models and their applications in stochastic hydrology, see Salas et al [1980], Kottegota [1980], Salas [1996], Brockwell and Davis [1996], and Chiang and Gates [2004]. Second, FORM estimates all probabilistic contents required for estimating lag‐1 resilience.…”

Section: Proposed Methodologiesmentioning

confidence: 99%

Estimating resilience for water resources systems

Lence

2007

Water Resources Research

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[1] 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.

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Section: Proposed Methodologiesmentioning

confidence: 99%