Evaluation of dam overtopping probability induced by flood and wind

Hsu, Yung Chia; Tung, Yeou‐Koung; Kuo, Jan Tai

doi:10.1007/s00477-010-0435-7

Cited by 36 publications

(15 citation statements)

References 20 publications

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“…This will automatically be at the expense of the accuracy of the estimate. Fortunately, the efficiency of Monte Carlo simulation can be enhanced through application of advanced sampling techniques like Latin hypercube sampling (Georgiou 2009;HSU et al 2011;Olsson et al 2003;Owen 1994;Ye 1998), directional sampling (Bjerager 1988;Ditlevsen et al 1990;Grooteman, 2011;Melchers 2002), stratified sampling (May et al 2010;Keskintürk and Er 2007;Christofides 2003) or importance sampling (Engelund and Rackwitz 1993;Koopman et al 2009;Sezer 2009;Yuan and Druzdzel 2006). A further efficiency gain may be achieved if these methods are combined with adaptive response surface techniques (Liu et al 2010;Steenackers et al 2009 Allaix andCarbone, 2011) which help explore the failure space, Z(x) \ 0, at a low computational cost.…”

Section: Probabilistic Computation Methodsmentioning

confidence: 99%

Importance sampling for efficient modelling of hydraulic loads in the Rhine–Meuse delta

Diermanse

Bruijn

Beckers

et al. 2014

Stoch Environ Res Risk Assess

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During flood events, breaching of flood defences along a river system can have a significant reducing effect on downstream water levels and flood risks. This paper presents a Monte Carlo based flood risk framework for policy decision making, which takes this retention effect into account. The framework is developed to estimate societal flood risk in terms of potential numbers of fatalities and associated probabilities. It is tested on the Rhine-Meuse delta system in the Netherlands, where floods can be caused by high flows in the Rhine and Meuse rivers and/or high sea water levels in the North Sea. Importance sampling is applied in the Monte Carlo procedure to increase computational efficiency of the flood risk computations. This paper focuses on the development and testing of efficient importance sampling strategies for the framework. The development of an efficient importance sampling strategy for river deltas is more challenging than for non-tidal rivers where only discharges are relevant, because the relative influence of river discharge and sea water level on flood levels differs from location to location. As a consequence, sampling methods that are efficient and accurate for one location may be inefficient for other locations or, worse, may introduce errors in computed design water levels. Nevertheless, in the case study described in this paper the required simulation time was reduced by a factor 100 after the introduction of an efficient importance sampling method in the Monte Carlo framework, while at the same time the accuracy of the Monte Carlo estimates were improved.

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Section: Probabilistic Computation Methodsmentioning

confidence: 99%

Importance sampling for efficient modelling of hydraulic loads in the Rhine–Meuse delta

Diermanse

Bruijn

Beckers

et al. 2014

Stoch Environ Res Risk Assess

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“…There are many uncertainties associated with real‐time operations of flood control systems [ Simonovic , ; Melching , ; Tung et al ., ; Hsu et al ., ]. This paper considers the following four sources of uncertainty: (1) the flood forecasting errors, arising from model structural uncertainty in flood forecasting models as well as errors in parameter estimation of those models [ Wu et al ., ]; (2) reservoir capacity curve errors (the curve defines the relationship between reservoir storage and water level) due to uncertainty in environmental change, such as reservoir‐bank sediment deposition and degradation, collapse of reservoir banks during an earthquake, and landsides into reservoir banks caused by debris flow; (3) reservoir discharge curve errors (the curve defines the relationship between reservoir discharge and water level), arising from the difference between actual spillway capacity and the designed spillway capacity caused by measurement errors of spillway facilities and errors of flow coefficients [ Jiang , ]; and (4) river flood routing errors, due to the structural uncertainty in river flood routing models and the errors in parameter estimation of the models.…”

Section: Introductionmentioning

confidence: 99%

A decomposition‐integration risk analysis method for real‐time operation of a complex flood control system

Chen

Zhong

Zhang

et al. 2017

Water Resources Research

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Risk analysis plays an important role in decision making for real‐time flood control operation of complex flood control systems. A typical flood control system consists of reservoirs, river channels, and downstream control points. The system generally is characterized by nonlinearity and large scale. Additionally, the input variables are mostly stochastic. Because of the dimensionality problem, generally, it would not be possible to carry out risk analysis without decomposition. In this paper, we propose a decomposition‐integration approach whereby the original complex flood control system is decomposed into a number of independent subsystems. We conduct risk analysis for each subsystem and then integrate the results by means of combination theory of stochastic processes. We evaluate the propagation of uncertainties through the complex flood control system and calculate the risk of reservoir overtopping, as well as the risk of flooding at selected downstream control points. We apply the proposed methodology to a flood control system in the middle reaches of the Huaihe River basin in China. The results show that the proposed method is practical and provides a way to estimate the risks in real‐time flood control operation of a complex flood control system.

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“…Environmental issues sometimes require binary classification: for example, the question whether to raise a dam (Hsu et al 2010) or to issue a dengue fever warning (Yu et al 2010) is a yes-or-no decision. Using supervised learning, such questions can be approached using historical precedents collected in a training set of exemplars with categorical labels.…”

Section: Introductionmentioning

confidence: 99%

Gaussian process classification: singly versus doubly stochastic models, and new computational schemes

Röder

Tolosana‐Delgado

Hamprecht

2011

Stoch Environ Res Risk Assess

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The aim of this paper is to compare four different methods for binary classification with an underlying Gaussian process with respect to theoretical consistency and practical performance. Two of the inference schemes, namely classical indicator kriging and simplicial indicator kriging, are analytically tractable and fast. However, these methods rely on simplifying assumptions which are inappropriate for categorical class labels. A consistent and previously described model extension involves a doubly stochastic process. There, the unknown posterior class probability f(Á) is considered a realization of a spatially correlated Gaussian process that has been squashed to the unit interval, and a label at position x is considered an independent Bernoulli realization with success parameter f(x). Unfortunately, inference for this model is not known to be analytically tractable. In this paper, we propose two new computational schemes for the inference in this doubly stochastic model, namely the ''Aitchison Maximum Posterior'' and the ''Doubly Stochastic Gaussian Quadrature''. Both methods are analytical up to a final step where optimization or integration must be carried out numerically. For the comparison of practical performance, the methods are applied to storm forecasts for the Spanish coast based on wave heights in the Mediterranean Sea. While the error rate of the doubly stochastic models is slightly lower, their computational cost is much higher.Keywords Model-based geostatistics Á Logistic transformation Á Classical indicator kriging Á Simplicial indicator kriging Á Doubly stochastic process Aitchison maximum posterior Á Doubly stochastic Gaussian quadrature 1 Introduction

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Evaluation of dam overtopping probability induced by flood and wind

Cited by 36 publications

References 20 publications

Importance sampling for efficient modelling of hydraulic loads in the Rhine–Meuse delta

Importance sampling for efficient modelling of hydraulic loads in the Rhine–Meuse delta

A decomposition‐integration risk analysis method for real‐time operation of a complex flood control system

Gaussian process classification: singly versus doubly stochastic models, and new computational schemes

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