Damage identification method using harmonic excitation force considering both modelling and measurement errors

A method of structural damage identification using harmonic excitation force is presented. It considers the effects of both measurement and modelling errors in the baseline finite element model. Damage that accompanies changes in structural parameters can be estimated for a damaged structure from the change between measured vibration responses and ones calculated from the analytical model of the intact structure. In practice, modelling errors exist in the analytical model due to material and geometric uncertainties and a reduction in the degrees of freedom as well as measurement errors, making identification difficult. To surmount these problems, bootstrap hypothesis testing, which enables statistical judgment without information about these errors, was introduced. The method was validated by numerical simulation using a three‐dimensional frame structure and real vibration data for a three‐storey steel frame structure. Copyright © 2005 John Wiley & Sons, Ltd.     

A stepwise method for determining the number of component distributions in a mixture

This paper considers the problem of estimatingm, the number of components in a finite mixture of distributions from a parametric family. A step-up procedure using the bootstrap method is proposed. Some properties of the procedure are illustrated with simulation studies. An example of the method, applied to orientation of beach clasts, is given.     

Calibration of paired watersheds: Utility of moving sums in presence of externalities

Historically, paired watershed studies have been used to quantify the hydrological effects of land use and management practices by concurrently monitoring 2 similar watersheds during calibration (pretreatment) and post‐treatment periods. This study characterizes seasonal water table and flow response to rainfall during the calibration period and tests a change detection technique of moving sums of recursive residuals (MOSUM) to select calibration periods for each control–treatment watershed pair when the regression coefficients for daily water table elevation were most stable to minimize regression model uncertainty. The control and treatment watersheds were 1 watershed of 3–4‐year‐old intensely managed loblolly pine (Pinus taeda L.) with natural understory, 1 watershed of 3–4‐year‐old loblolly pine intercropped with switchgrass (Panicum virgatum), 1 watershed of 14–15‐year‐old thinned loblolly pine with natural understory (control), and 1 watershed of switchgrass only. The study period spanned from 2009 to 2012. Silvicultural operational practices during this period acted as external factors, potentially shifting hydrologic calibration relationships between control and treatment watersheds. MOSUM results indicated significant changes in regression parameters due to silvicultural operations and were used to identify stable relationships for water table elevation. None of the calibration relationships developed using this method were significantly different from the classical calibration relationship based on published historical data. We attribute that to the similarity of historical and 2010–2012 leaf area index on control and treatment watersheds as moderated by the emergent vegetation. Although the MOSUM approach does not eliminate the need for true calibration data or replace the classic paired watershed approach, our results show that it may be an effective alternative approach when true data are unavailable, as it minimizes the impacts of external disturbances other than the treatment of interest.     

A physically constrained wavelet-aided statistical model for multi-decadal groundwater dynamics predictions

A physically constrained wavelet-aided statistical model (PCWASM) is presented to analyse and predict monthly groundwater dynamics on multi-decadal or longer time scales. The approach retains the simplicity of regression modelling but is constrained by temporal scales of processes responsible for groundwater level variation, including aquifer recharge and pumping. The methodology integrates statistical correlations enhanced with wavelet analysis into established principles of groundwater hydraulics including convolution, superposition and the Cooper–Jacob solution. The systematic approach includes (1) identification of hydrologic trends and correlations using cross-correlation and multi-time scale wavelet analyses; (2) integrating temperature-based evapotranspiration and groundwater pumping stresses and (3) assessing model prediction performances using fixed-block k-fold cross-validation and split calibration-validation methods. The approach is applied at three hydrogeologicaly distinct sites in North Florida in the United States using over 40 years of monthly groundwater levels. The systematic approach identifies two patterns of cross-correlations between groundwater levels and historical rainfall, indicating low-frequency variabilities are critical for long-term predictions. The models performed well for predicting monthly groundwater levels from 7 to 22 years with less than 2.1 ft (0.7 m) errors. Further evaluation by the moving-block bootstrap regression indicates the PCWASM can be a reliable tool for long-term groundwater level predictions. This study provides a parsimonious approach to predict multi-decadal groundwater dynamics with the ability to discern impacts of pumping and climate change on aquifer levels. The PCWASM is computationally efficient and can be implemented using publicly available datasets. Thus, it should provide a versatile tool for managers and researchers for predicting multi-decadal monthly groundwater levels under changing climatic and pumping impacts over a long time period.     

Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response

Following many applications artificial neural networks (ANNs) have found in hydrology, a question has been rising for quantification of the output uncertainty. A pre‐optimized ANN simulated the hydraulic head change at two observation wells, having as input hydrological and meteorological parameters. In order to calculate confidence intervals (CI) for the ANN output two bootstrap methods were examined namely bootstrap percentile and BCa (Bias‐Corrected and accelerated). The actual coverage of the CI was compared to the theoretical coverage for different certainty levels as a means of examining the method's reliability. The results of this work support the idea that the bootstrap methods provide a simple tool for confidence interval computation of ANNs. Comparing the two methods, the percentile requires fewer calculations and yields narrower intervals with similar actual coverage to that of BCa. Overall, the actual coverage was proved lower than desired when not modeled points were present in the data subset. Copyright © 2011 John Wiley & Sons, Ltd.     

近25 a气候变化对江苏省粮食产量的影响

利用1986-2010年江苏省63个气象站的常规气象数据和粮食单产统计资料,分析了苏北、苏中、苏南地区和江苏全省三种时间尺度的气候变化特征;基于自助抽样(bootstrap)和一元线性回归的方法,研究了各区和全省粮食产量对作物年(11月一次年10月)、夏粮—秋粮生长季(11月-次年5月和6-10月)和月尺度气候要素的响应;并定量评价了过去25 a气候变化对各区和全省粮食产量的影响以及各气候要素的贡献.结果表明:1)在作物年、夏粮—秋粮生长季以及月尺度上,三区和全省各气候要素均发生了不同程度的变化,且存在一定的时空差异.在不断发展的农业管理措施和技术以及气候的共同作用下,三区和全省粮食单产显著(p＞0.01)增加,其中,全省增加趋势为66.89 kg·hm-2·a-1.2)除苏南地区对作物年尺度上的气候变化响应不显著外,粮食产量对降水的不随时间变化的负响应关系(即随降水的增加而减小,减小而增加)均在不同时间尺度和地区得到了体现,说明降水对这些地区粮食生产的影响十分重要;其中,苏北、苏中和全省粮食产量随作物年降水的增加(减少)而减小(增加),平均速率分别为0.19％·(10 mm)-1、0.09％·(10 mm)-1和0.11％·(10 mm)-1.3)三类模型结果均显示气候变化使得苏北、苏南和江苏粮食产量减小,但结果略有差异,其中,利用月气候要素建立的模型C的结果显示气候变化对粮食单产(总产)的影响最大,其均值分别为-6.51％·(10 a)-1(-11.28×108kg· (10 a)-1)、-3.27％·(10 a)-1(-2.36×108 kg·(10 a)-1)和-1.34％·(10 a)-1(-4.45×108kg·(10 a)-1).另外,为了系统而全面地评估气候变化对粮食产量的影响,考虑月尺度的气候变化的影响是十分必要的.     

Prediction of Resource Volumes at Untested Locations Using Simple Local Prediction Models

This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses.

Dependence of model-based extreme flood estimation on the calibration period: case study of the Kamp River (Austria)

Abstract

The Kamp River is a particularly interesting case study for testing flood frequency estimation methods, since it experienced a major flood in August 2002. Here, the Kamp catchment is studied in order to quantify the influence of such a remarkable flood event on the calibration of a rainfall–runoff model, in particular when it is used in a stochastic simulation method for flood estimation, by performing numerous rainfall–runoff model calibrations (based on split-sample and bootstrap tests). The results confirmed the usefulness of the multi-period and bootstrap testing schemes for identifying the dependence of model performance and flood estimates on the information contained in the calibration period. The August 2002 event appears to play a dominating role for the Kamp River, since the presence or absence of the event within the calibration sub-periods strongly influences the rainfall–runoff model calibration and the extreme flood estimations that are based on the calibrated model.