Constructing confidence intervals of extreme rainfall quantiles using Bayesian,bootstrap, and profile likelihood approaches

Hydrological risk is highly dependent on the occurrence of extreme rainfalls.This fact has led to a wide range of studies on the estimation and uncertainty analysis of the extremes.In most cases,confidence intervals(CIs)are constructed to represent the uncertainty of the estimates.Since the accuracy of CIs depends on the asymptotic normality of the data and is questionable with limited observations in practice,a Bayesian highest posterior density(HPD)interval,bootstrap percentile interval,and profile likelihood(PL)interval have been introduced to analyze the uncertainty that does not depend on the normality assumption.However,comparison studies to investigate their performances in terms of the accuracy and uncertainty of the estimates are scarce.In addition,the strengths,weakness,and conditions necessary for performing each method also must be investigated.Accordingly,in this study,test experiments with simulations from varying parent distributions and different sample sizes were conducted.Then,applications to the annual maximum rainfall(AMR)time series data in South Korea were performed.Five districts with 38-year(1973–2010)AMR observations were fitted by the three aforementioned methods in the application.From both the experimental and application results,the Bayesian method is found to provide the lowest uncertainty of the design level while the PL estimates generally have the highest accuracy but also the largest uncertainty.The bootstrap estimates are usually inferior to the other two methods,but can perform adequately when the distribution model is not heavy-tailed and the sample size is large.The distribution tail behavior and the sample size are clearly found to affect the estimation accuracy and uncertainty.This study presents a comparative result,which can help researchers make decisions in the context of assessing extreme rainfall uncertainties.     

A new calibration algorithms of spinning projectile aerodynamic parameters

This paper demonstrates that the application of calibration algorithms of aerodynamic parameters for the trajectory of spinning projectile is successful. First, from the point of view of the trajectory simulation, a general summary of well-known trajectory models is given. A five degrees of freedom （5 DOF） model is developed that can match the projectile motion essentially in the vertex region, and the results obtained by 5 DOF model are in close agreement with those of a more sophisticated 6 DOF model for elevation angles above 45 degrees. Secondly, the calibration algorithms have been developed and are summarized. The methods of calibrating the flight trajectory models are compared, and these methods are shown to be effective in the representative cases. In addition, the method of Math number calibration （MNC） is presented; some possible areas in MNC for further investigation are indicated together with benefits to be gained. The utilization of MNC schemes not only allow a worthwhile reduction of calibration rounds firing in range and accuracy （R＆A） trial and production of firing tables （PFT） test, but also make PFT and fire control data （FCD） more cost effective.     

Sensitivity Analysis, Determination and Optimization of Granite RHT Parameters

The RHT model has 34 parameters, among which 19 parameters can be obtained by experiments or theoretical calculations and the remaining 15 parameters are difficult to acquire. In this study, firstly, 10 Hopkinson impact tests were conducted to acquire the typical stress-strain curves of granite under dynamic loads. Through the sensitivity analysis, it is found that 13 of the 15 difficult-acquired parameters are effective to affect the shape of the stress-strain curve, and the other two parameters have no effect. Following the initial determination of model parameters with reference to the concrete RHT model, a new approach is proposed to optimize the 13 influential parameters through the LS-DYNA numerical simulation and orthogonal experiments. Finally, the determined granite RHT model parameters are verified by the results of Hopkinson impact tests conducted in this study and the bullet penetration test by Wang et al. Both results of the numerical simulations are in a good agreement with the tested results, which validates the suitability of the proposed method to acquire RHT model parameters for granite and the other rocks.     

Parameter Identification Based on Force-Displacement Curves for a Bolted Joint Finite Element Model

In order to identify the uncertain parameters of a bolted joint finite element model, a simple and applicable way of parameter identification is introduced. By utilizing numerical simulation with the Abaqus software and experimental investigation with the MTS material testing system, the tangential force-displacement curves that reflect the characteristics of the bolted joint were acquired. On the basis of this, by employing the response surface methodology (RSM) and genetic algorithms (GAs), parameters in the FEM model were identified. The force-displacement curves by both virtual and experimental approaches are well correlated at the end. This phenomenon-based parameter identification method may help facilitate precise prediction of complex jointed connection structures.     

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life（RUL） prediction based on condition monitoring is an essential part in condition based maintenance（CBM）. In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item＇s individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.     

Recent developments on applications of sequential loop closing and diagonal dominance control schemes to industrial multivariable system

With a focus on an industrial multivariable system, two subsystems including the flow and the level outputs are analysed and controlled, which have applicability in both real and academic environments. In such a case, at first, each subsystem is distinctively represented by its model, since the outcomes point out that the chosen models have the same behavior as corresponding ones. Then, the industrial multivariable system and its presentation are achieved in line with the integration of these subsystems, since the interaction between them can not actually be ignored. To analyze the interaction presented, the Gershgorin bands need to be acquired, where the results are used to modify the system parameters to appropriate values. Subsequently, in the view of modeling results, the control concept in two different techniques including sequential loop closing control （SLCC） scheme and diagonal dominance control （DDC） schemes is proposed to implement on the system through the Profibus network, as long as the OPC （OLE for process control） server is utilized to communicate between the control schemes presented and the multivariable system. The real test scenarios are carried out and the corresponding outcomes in their present forms are acquired. In the same way, the proposed control schemes results are compared with each other, where the real consequences verify the validity of them in the field of the presented industrial multivariable system control.     

Head Curvature of Pure Titanium Sheet Influenced by Process Parameters and Controlling in Hot Rolling Process

We investigated the influences of process parameters on the head curvature of pure titanium sheet in hot rolling process and proposed the controlling means. First, the thermal simulation experiments for pure titanium TA1 were carried out to investigate the hot deformation behaviors of pure titanium in the temperature range of 700-800 ℃ with strain rate range of 1-20 S-1, and the processing map was established to determine optimized deformation parameters. Then, the finite element model has been constructed and used to analyze the effect of process parameters on the direction and severity of head curvature of pure titanium sheet. The process parameters considered in the present study include workpiece temperature, work roll diameter, pass reduction, oxide scale thickness of workpiece surface, and interface friction coefficient. The simulation results show that the workpiece temperature and the interface friction coefficient are the two main factors. The proposed controlling means was carried out on a hot rolling production line and solved the head curvature problem effectively. The rolling practices indicate that the rolling yield is improved greatly.     

Calibration of Camera with Large Field-of-View Based on Flexible Planar Target

For high precision calibration of camera with large field-of-view, massive calibration points will be needed if traditional methods are selected, which makes the calibration complex and time-consuming. In order to solve this problem, a calibration method based on flexible planar target is proposed. In this method, distortion factor is firstly acquired by the invariance of cross ratio, and existing feature points are adjusted with the distortion factor. Then, a large number of points that will be used for the calibration are constructed with the adjusted feature points. Simultaneously, Tsai method is modified so as to reduce the complexity of calibration, which makes the process linear. The simulation and real experiments show that the method proposed in this paper is simple, linear, accurate and robust, and the precision of this method is close to that of Tsai method using abundant points. The method can satisfy the requirement of high precision calibration for camera with large field-of-view.     

An iterative calibration method for nonlinear coefficients of marine triaxial accelerometers

Single-axis rotation technique is often used in the marine laser inertial navigation system so as to modulate the constant biases of non-axial gyroscopes and accelerometers to attain better navigation performance. However, two significant accelerometer nonlinear errors need to be attacked to improve the modulation effect. Firstly, the asymmetry scale factor inaccuracy enlarges the errors of frequent zero-cross oscillating specific force measured by non-axial accelerometers. Secondly, the traditional linear model of accelerometers can hardly measure the continued or intermittent acceleration accurately. These two nonlinear errors degrade the high-precision specific force measurement and the calibration of nonlinear coefficients because triaxial accelerometers is urgent for the marine navigation. Based on the digital signal sampling property, the square coefficients and cross-coupling coefficients of accelerometers are considered. Meanwhile, the asymmetry scale factors are considered in the I-F conversion unit. Thus, a nonlinear model of specific force measurement is established compared to the linear model. Based on the three-axis turntable, the triaxial gyroscopes are utilized to measure the specific force observation for triaxial accelerometers. Considering the nonlinear combination, the standard calibration parameters and asymmetry factors are separately estimated by a two-step iterative identification procedure. Besides, an efficient specific force calculation model is approximately derived to reduce the real-time computation cost. Simulation results illustrate the sufficient estimation accuracy of nonlinear coefficients. The experiments demonstrate that the nonlinear model shows much higher accuracy than the linear model in both the gravimetry and sway navigation validations.     

基于正交极差分析的青龙河流域HSPF模型参数寻优模式

为评价桃林口水库水文水质状况,建立其上游青龙河流域的BASINs/HSPF水文水质模拟模型.以Nash-Sutcliffe效率系数作为评价标准,联合正交设计和极差分析法进行参数寻优研究和可靠性分析.结果表明:BASINs/HSPF模型参数寻优的正交极差联合分析模式可大幅减小参数的寻优次数和寻优区间,是获得模型灵敏度参数和参数满意解的高效模式,可为模型参数寻优算法开发提供借鉴;应用正交极差分析的参数率定结果,以青龙河流域下游桃林口水库监测点2011年实测径流数据对模型进行验证,验证期(2011年)的Nash-Sutcliffe效率系数为0.79,稍低于率定期(2012年)的Nash-Sutcliffe效率系数(0.81)以及PEST自动校准的Nash-Sutcliffe效率系数(0.84),水质模拟结果也基本吻合;针对青龙河流域BASINs/HSPF模型,应用正交极差分析方法识别出6个高灵敏的BASINs/HSPF模型参数(IRC、INFILT、AGWRC、DEEPFR、AGWETP和LZSN);应用正交极差分析方法获得的BASINs/HSPF模型参数满意解取值区间依次为:LZSN[2.0,6.3],INFILT[0.084,0.167],AGWRC[0.90,0.95],DEEPFR[0.209,0.333],BASETP[0.067,0.199],AGWETP[0.067,0.199],CEPSC[0.14,0.27],UZSN[1.35,2.00],IRC[0.392,0.483].     

SCS模型在流域尺度水文模拟中的应用

流域的产汇流是很复杂的水文过程,也是水文水资源领域的主要研究对象,采用SCS模型对我国湖北省漳河灌区新埠河-桥河流域的水文过程进行模拟研究,并根据该流域实际水文情况和下垫面条件,对模型进行改进.通过对SCS产汇流模型参数和结构进行调整,可以大大提高模型效率.将改进后的模型应用于该流域水文模拟,模型应用结果表明:率定期产流合格率可以达到75%,汇流合格率达到87.5%;检验期产流合格率达到80%,汇流合格率能够达到75%,大大改善了模型模拟效果.     

水文模型两种不确定性研究方法的比较

水文模型结构本身的缺陷、模型输入输出误差、水文模型参数冗余及其复杂的非线性联系等,导致了流域水文模型的不确定性.基于贝叶斯理论的MCMC方法及GLUE方法近年来被广泛应用于流域水文模型的不确定性研究工作中.为比较上述2种模型不确定性分析方法的分析效果及其优劣,以位于汉江流域的牧马河流域作为研究对象,采用集总式概念性水文模型SMAR模型作为实验模型,推求其模型参数的不确定性及参数的后验分布.采用基于实测流量资料估计的置信区间可靠性作为评判标准,实验结果表明:就SMAR模型而言,MCMC方法能够更好地推求模型参数的后验分布.     

基于栅格式SCS模型的分布式水文模型研究

利用基于栅格分布的土地利用类型和土壤类型,通过将SCS模型改进为栅格式的SCS模型,并结合栅格式Clark单位线汇流方法,构建了一个具有松散耦合型分布式水文模型.该模型结构简单,模型参数较少,所需流域特性资料容易获取,对流域具有较强的适应性,并可以获得流域栅格分布式的产流结果.本研究将该模型应用到欧阳海流域上,构建洪水预报模型进行场次洪水过程模拟,并与新安江模型模拟结果进行对比.模拟结果表明,所构建的松散结构分布式水文模型具有较高的模拟精度,能够应用到实际的洪水预报中.     

Modeling the crop transpiration using an optimality-based approach

Evapotranspiration constitutes more than 80% of the long-term water balance in Northern China. In this area, crop transpiration due to large areas of agriculture and irrigation is responsible for the majority of evapotranspiration. A model for crop transpiration is therefore essential for estimating the agricultural water consumption and understanding its feedback to the environment. However, most existing hydrological models usually calculate transpiration by relying on parameter calibration against local observations, and do not take into account crop feedback to the ambient environment. This study presents an optimality-based ecohydrology model that couples an ecological hypothesis, the photosynthetic process, stomatal movement, water balance, root water uptake and crop senescence, with the aim of predicting crop characteristics, CO₂ assimilation and water balance based only on given meteorological data. Field experiments were conducted in the Weishan Irrigation District of Northern China to evaluate performance of the model. Agreement between simulation and measurement was achieved for CO₂ assimilation, evapotranspiration and soil moisture content. The vegetation optimality was proven valid for crops and the model was applicable for both C ₃ and C ₄ plants. Due to the simple scheme of the optimality-based approach as well as its capability for modeling dynamic interactions between crops and the water cycle without prior vegetation information, this methodology is potentially useful to couple with the distributed hydrological model for application at the watershed scale.     

概念性水文模型参数多目标率定及参数组合预报

针对概念性水文模型参数优化率定问题,考虑流域水文系统不同产汇流特性,提出一种多目标文化自适应仿电磁学算法（multi-objective cultural self-adaptive electromagnetism-like mechanism, MOCSEM）,并基于MOCSEM的优化结果,提出了一种简便的自识别参数组合预报方法。该算法采用仿电磁学算法实现可行解集的进化,并置于文化进化的框架中,根据水文模型参数优化率定问题的特点构建了信息空间,并引入了基于个体聚集密度的信息空间更新方法,保证了非劣方案的多样性;同时,加入一种自适应的扰动因子对算法的局部随机搜索操作进行修正,改善了算法的全局寻优能力。在此基础上,将MOCSEM算法应用于概念性水文模型——新安江模型的参数多目标优化率定,与MOSCDE算法进行对比分析,应用本文提出的自识别参数组合预报方法,尝试找到一种能权衡流域水文系统不同水文特性的自识别参数组合方式,可以为水文预报人员提供更为精确的流域径流预报方法。     

基于贝叶斯方法的实时洪水校正模型

简述了实时洪水校正方法的研究进展,提出并建立了基于贝叶斯方法的实时洪水校正模型. 该模型用先验分布考虑水文资料的不确定性,用似然函数描述模型结构和参数的不确定性,将两者综合得到预报流量的后验分布. 选用江西省白云山水库的雨洪资料对模型进行探讨与检验,采用可变遗忘因子最小二乘递推算法估计先验分布和似然函数的参数,以后验分布密度函数的均值作为结果发布. 结果表明该法不仅可显著的提高洪水预报精度,还给出预报区间估计值,实现了预报与决策的有机耦合.     

基于变结构方法的机器手臂自适应控制

针对二自由度机器手臂的轨迹跟踪控制中出现的运动学模型参数可能发生改变以及外部状况变化和负荷变化等不确定因素,提出基于滑模变结构补偿方法的径向基神经网络自调节控制律,其中径向基神经网络抵偿了系统参数不确定性和外部扰动量,滑模变结构方法抵消了径向基神经网络的逼近误差,较好地消除了系统未知不确定性的影响。仿真结果表明,该控制律能保证轨迹跟踪误差的快速收敛性及对参数不确定性和外部扰动的鲁棒性。     

基于SWAT模型的三峡库区流域污染物来源分析及重点控制区域识别

随着非点源污染逐步成为三峡库区流域污染的重要控制对象,将SWAT模型应用于三峡库区流域非点源模拟研究,以识别库区非点源污染来源关键区域.模型建立后采用2002—2008年水文、水质实测数据进行率定与验证,径流、泥沙、营养盐的评价指标ENS均达到令人满意的效果,表明SWAT模型应用于三峡库区流域是可行的.通过对三峡水库污染物的来源分析可知:三峡库区上游汇入为库区污染物的主要来源,贡献率平均为71.74%,库区流域非点源污染贡献率最高为33.03%,而库区点源污染贡献率较小,最高仅为4.17%.对库区单位面积污染负荷的模拟研究表明:三峡库区流域非点源污染物产生的关键区域主要集中在库区腹部,即小江、大宁河、汤溪河等支流流域.     

A distributed hydrological model with its application to the Jinghe watershed in the Yellow River Basin

TheYellowRiverBasincoversalargeportionofChina,consistingofcomplicatedlandformswithnoticeabledifferencesfromoneareatoanother.Undertheimpactsofatmosphericcirculationsandmonsoonsystems,itsdifferentpartsareinfluencedgreatlybydissimilarclimate,leadingtoappreciablespatialvariabilityofthelandsurfaceandmeteorologicalaspects,whichhasresultedinthecomplicationofhydrologicalcycleinthebasin[1].Thedistributedhydrologicalmodel,withthespatialdistributioncharactersofmodelstructuresandparameters,isnowconsidere…