Technical note: comparison of methods for threshold selection for extreme sea levels

Caballero-Megido, C.; Hillier, John K.; Wyncoll, David; Bosher, Lee S.; Gouldby, Ben

doi:10.1111/jfr3.12296

Cited by 13 publications

(10 citation statements)

References 32 publications

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“…Note that there is an inverse relationship between and . To ensure (13), is substituted with 1 − and normalized for direct illustration…”

Section: Entropy Methodmentioning

confidence: 99%

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Threshold Selection for POT Framework in the Extreme Vehicle Loads Analysis Based on Multiple Criteria

Qiu

2018

Shock and Vibration

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Extreme value of vehicle load plays an important role in bridge design and risk assessment. Peaks over threshold (POT) is a method commonly used in extreme load estimation. The selection of thresholds for the POT method is extremely crucial, but the selected optimal threshold varies under different test criteria. Therefore, a method to select the suitable threshold is developed based on multiple criteria decision analysis (MCDA) in the paper. In MCDA, Chi-Square (χ2) test, Kolmogorov–Smirnov (K–S) test, and Root Mean Square Error (RMSE) in probability distribution functions (PDF) are employed as the test criteria and the weight of these criteria is calculated using the entropy method. Finally, vehicle loads obtained from simulation and field measurement are adopted to validate the effectiveness and feasibility of the proposed method. The results indicate that the proposed MCDA is a useful and complementary tool for threshold selection in the extreme value analysis.

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“…Note that there is an inverse relationship between and . To ensure (13), is substituted with 1 − and normalized for direct illustration…”

Section: Entropy Methodmentioning

confidence: 99%

“…On the other hand, the threshold level cannot be too high so that sufficient data can be included [12]. The literature suggests that the most used threshold selection methods are based on judgement [13]. One way is to use graphical diagnostic plots, but interpreting these plots is subjective and rather challenging.…”

Section: Introductionmentioning

confidence: 99%

Threshold Selection for POT Framework in the Extreme Vehicle Loads Analysis Based on Multiple Criteria

Qiu

2018

Shock and Vibration

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“…Different methods have been proposed for selecting thresholds automatically, e.g. Northrop et al (2017) and Caballero-Megido et al (2018). Different sampling methods may result in different ESL estimates, especially for long return periods.…”

Section: Samplingmentioning

confidence: 99%

Extreme sea levels along coastal China: uncertainties and implications

Fang

Wahl

Zhang

et al. 2021

Stoch Environ Res Risk Assess

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Extreme sea levels (ESLs) due to typhoon-induced storm surge threaten the societal security of densely populated coastal China. Uncertainty in extreme value analysis (EVA) for ESL estimation has large implications for coastal communities’ adaptation to natural hazards. Here we evaluate uncertainties in ESL estimation and relevant driving factors based on hourly observations from 13 tide gauge stations and a complementary dataset derived from a hydrodynamic model. Results indicate significant uncertainties in ESL estimations stemming from using different EVA methods, which then propagate to the inundation assessment. Amplification factors due to sea-level rise (SLR) are highly sensitive to local relative SLR and the shape of the exceedance probability curve, which in turn depends on the selected EVA method. The hydrodynamic model hindcast indicates that high ESLs mainly occurred in eastern coastal China due to typhoon-induced storm surge. Larger uncertainties in the modelled ESLs are found for the coasts of the Yangtze River Delta, and particularly in the river mouth region. Future research and adaptation planning should prioritize these regions given expected future rising sea level, compound flood events, and human-induced factors (e.g. subsidence). This study provides theoretical and practical references for adaptation to ESL-related hazards along coastal China, with implications for coastal regions worldwide.

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“…Moreover, the level k 0 leads to a biased estimate of the EVI. Other methods for selecting the threshold can be found in References 13‐15.…”

Section: Introductionmentioning

confidence: 99%

A class of weighted Hill estimators

Caeiro

Mateus

Soltane

2021

Comp and Math Methods

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In Statistics of Extremes, the estimation of the extreme value index is an essential requirement for further tail inference. In this work, we deal with the estimation of a strictly positive extreme value index from a model with a Pareto-type right tail. Under this framework, we propose a new class of weighted Hill estimators, parameterized with a tuning parameter a. We derive their non-degenerate asymptotic behavior and analyze the influence of the tuning parameter in such result. Their finite sample performance is analyzed through a Monte Carlo simulation study. A comparison with other important extreme value index estimators from the literature is also provided.

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Technical note: comparison of methods for threshold selection for extreme sea levels

Cited by 13 publications

References 32 publications

Threshold Selection for POT Framework in the Extreme Vehicle Loads Analysis Based on Multiple Criteria

Threshold Selection for POT Framework in the Extreme Vehicle Loads Analysis Based on Multiple Criteria

Extreme sea levels along coastal China: uncertainties and implications

A class of weighted Hill estimators

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