基于抗差最小二乘法的大坝安全监测统计模型

传统监测资料分析是在假定监测数据服从正态分布的前提下,采用经典最小二乘法进行建模分析的,但最小二乘法善于吸收异常值,不具备抗差能力。提出应用抗差最小二乘法建模,并对其计算方法以及相关参数进行了系统的研究,提出了监控模型的优化方案。同时通过监测实例证明,抗差最小二乘法计算模型优于最小二乘法计算模型,能够降权使用可疑值,淘汰异常值,具有较强的抗差能力,预测精度高。     

基于稳健估计的大坝变形监测统计模型分析

回归分析是大坝安全监测统计模型的核心,测值粗差的存在使回归参数变异,造成统计模型一定程度上失真,所以在回归分析中必须对其进行处理。本文采用稳健估计的思想,根据残差大小对监测值进行赋权迭代计算,使常规统计模型具有抗差性。将稳健估计的思想应用于某工程实例分析,发现与常规统计模型相比,稳健统计模型能够有效抵抗粗差对模型参数求解结果的影响,并能较好反映水压、温度和时效等效应量的影响。     

大坝安全监测统计模型中的异方差问题

 针对大坝安全监测统计模型中异方差产生的原因和可能带来的后果，用图示法及格莱斯尔检验法检验异方差性的方法，并采用方差的稳定变换及加权最小二乘估计法对存在异方差问题的统计模型进行了改进，使之降低了异方差性对模型参数估计的影响，使回归模型更好地拟合观测数据，提高了利用模型进行预测和控制的可靠度。     

土石坝垂直位移预报模型的建立

一、引言 在土石坝垂直位移观测资料分析中,一般通过散点图来配置线性或者非线性拟合模型,参数估计时,对于非线性拟合模型往往先进行线性化变换。然后,再按最小二乘法求得模型未知参数,从而得到比较“合适”的垂直位移预报模型,藉此进行未来沉陷量的预报。这种最基本最常用的方法,业已广泛地应用于各种土石坝、高层建筑和滑坡观测资料的处理分析中。但是,人们在实践中常常感到,用这种方法建立的预报模型,对于历史资料虽有较高的拟合率,但预报精度却常常较低。文献〔1〕也指出应用这种模型进行预报(即外推)要十分慎     

水文序列的相关插补条件

引用统计学中有关统计检验的概念，分析作为插补资料的相关关系条件，并尽可能给出判别准则及判别式。在相关插补水文序列时，首先需要进行以下检验：（１）残差检验；（２）线性相关程度检验；（３）展延范围的确定。残差检验可论证随机误差是否服从正态分布及是否相互独立。线性相关程度检验可分析所假定的线性回归是否合理及相关程度是否满足插补要求。通过插补范围的估计可求出最大的外延幅度，使插补值的取值范围或误差控制在一     

大盈江泥沙时间序列预测模型

采用时间序列自回归滑动平均(ARIMA)预测模型,对伊洛瓦底江支流大盈江拉贺练水文站1980～2005年平均含沙量资料进行建模预测.综合AIC值、相对误差,确定模型的阶数,运用Marquaredt非线性最小二乘法估计模型参数,建立ARIMA预测模型.经检验,AIC=-114,相对误差全部低于20%,残差序列为白噪声序列,表明ARIMA(1,3,2)模型较为合理.应用模型对2006～2009年拉贺练水文站的年平均含沙量进行了预测,实现河流输沙状况的短期预报.     

MLR-Legendre多项式模型在混凝土坝裂缝开度预测中的应用

在混凝土坝裂缝开度预测中得到了广泛应用的统计回归模型仍存在不足。首先,对小容量样本的观测时间序列难以建立有效的统计回归模型;其次,预测模型未能考虑残差项,而残差项包含了裂缝发展演变的海量信息,为了准确预测裂缝开度还须在预测模型中纳入残差项。同时,统计回归模型的残差序列中存在混沌成分,残差项受到某种动力特性支配,故基于混沌理论对残差项进行推求,建立了统计与混沌混合预测模型。采用基于Legendre多项式的RLS(递推最小二乘法)自适应预测算法,提出了针对小容量样本观测数据时间序列的实时预测模型以及针对大容量样本观测数据时间序列的统计回归-Legendre多项式残差预测模型。最后,结合陈村重力拱坝在105 m高程的裂缝开度实测数据,对裂缝开度实时预测模型以及统计回归-Legendre多项式组合模型分别进行了检验,结果表明模型具有良好的预测精度,可为工程的安全运行管理工作提供一定的技术支持。     

水文频率计算中参数估计方法述评

介绍了目前常见的水文频率分布模型统计参数的估计方法(不包括目估适线法)，以三参数Γ分布模型为例，进行了分析和比较。各种方法的估计结果，对均值、离差系数和设计值的内插部位比较接近，但偏态系数和设计值的外延部位有一定差异。     

复杂水情下H-ADCP流量在线监测推流方法

为提高受水利工程影响测验断面推流精度及提升H-ADCP流量在线监测系统水平,综合考虑仪器入水深、落差等因素,建立多元线性回归模型推算断面平均流速,利用最小二乘法求解模型参数;同时针对小流量下推流精度低的问题,充分考虑相关性较强的单个流速网格单元,采用机器学习中的LASSO回归模型进行参数估计,充分挖掘每个网格流速与实测...     

双向差分灰色模型及其在水文预报中的应用

采用双向差分建立一种水文预报模型。该模型与传统GM(1，1)模型的不同之处在于如何推求GM(1，1)模型的参数.本文是在某一预报量的前后差预报误差之和趋于最小时求出模型的参数。该模型用于宣城站年最高水位预报的结果表明，通过残差、后验差检验，模型达到了灰色模型所规定的精度等级(一级)。     

Prediction of longitudinal dispersion coefficient in natural streams by prediction map

Longitudinal dispersion coefficient can be determined by experimental procedures in natural streams. Many theoretical and empirical equations that are based on hydraulic and geometric characteristics have been developed from the field experiments of longitudinal dispersion coefficient. Regression analysis, which carries some restrictive assumptions such as linearity, normality and homoscedasticity, was used to derive some of these equations. Generally speaking, results obtained from regression analyses are not that accurate as these assumptions are often not satisfied completely. In this study, a method called Prediction Map (PM) is developed based on geostatistics to predict longitudinal dispersion coefficient from measured discharge values, shear velocities, and other conventional parameters of the hydraulic variables and normalized velocity with the objective of overcoming the drawbacks indicated above. As part of this method, a new procedure called Iterative Error Training Procedure (IETP) was developed to minimize prediction error. The prediction error level was reduced after implementing the IETP. PM was compared with various regression models by taking analyzed errors (average relative error percentage and root mean square error), coefficient of efficiency, coefficient of determination and Scatter Index as performance evaluation criteria. The results of the study indicate that the PM approach can perform very well in predicting longitudinal dispersion coefficient by applying IETP. The presented approach yielded lower average relative error percentage, root mean square error and Scatter Indices, and higher coefficient of efficiency and coefficient of determination values compared to the regression models. One of the important advantages of the PM method is that valuable interpretations and a prediction map can be extracted from the resulting contour maps, and as a result, more accurate predictions can be obtained compared to regression analysis.     

The Development of Rating Curve Considering Variance Function Using Pseudo-likelihood Estimation Method

This paper employs a new estimation method for estimating stage–discharge rating curve parameters. In typical practical applications, the original non-linear rating curve is transformed into a simple linear regression model by log-transforming the measurement without examining the effect of heteroscedasticity of residuals. Therefore, the model with pseudo-likelihood estimation is developed in this study to deal with heteroscedasticity of residuals in the original non-linear model. The parameters of rating curves and variance functions of errors are simultaneously estimated by the pseudo-likelihood estimation (P-LE) method. Also simulated annealing, a sort of global optimization techniques, is adapted to minimize the log likelihood of the weighted residuals. At first, the developed P-LE model was applied to a hypothetical site where stage–discharge data were generated by incorporating various errors for statistical test. Also, the limit of stages for segmentation is estimated in the process of P-LE using the Heaviside function. For the validation of effects of the developed P-LE model, the results of the conventional log-transformed linear regression (LT-LR) model and the P-LE model are estimated and compared. After statistical simulation, the developed P-LE model is then applied to the real data sets from six gauge stations in the Geum River basin. It can be suggested that this new estimation method is applied to real river sites to successfully determine the weights taking into account error distributions from observed discharge data.     

基于偏最小二乘回归与神经网络耦合的岩溶泉预报模型

本文将偏最小二乘回归与神经网络耦合，建立了泉流量预报模型。利用偏最小二乘法对影响岩溶泉流量的诸多因素进行分析，提取对因变量影响强的成分，从而克服了变量之间的多重相关性问题，降低了神经网络的输入维数。同时，利用神经网络建模可以较好地解决非线性问题。实例表明，本耦合模型的拟合和预报精度均优于独立使用偏最小二乘回归或神经网络建模的精度。     

Comparing ANN and ARIMA model in predicting the discharge of River Opeki from 2010 to 2020

Many attempts have been made in the recent past to model and forecast streamflow using various techniques with the use of time series techniques proving to be the most common. Time series analysis plays an important role in hydrological research. Traditionally, the class of autoregressive moving average techniques models has been the statistical method most widely used for modelling water discharge, but it has been shown to be deficient in representing nonlinear dynamics inherent in the transformation of runoff data. In contrast, the relatively newly improved and efficient soft computing technique artificial neural networks has the capability to approximate virtually any continuous function up to an arbitrary degree of accuracy, which is not otherwise true of other conventional hydrological techniques. This technique corresponds to human neurological system, which consists of a series of basic computing elements called neurons, which are interconnected together to form networks. The aim of the study is to compare the artificial neural network and autoregressive integrated moving average to model River Opeki discharge (1982–2010) and to use the best predictor to forecast the discharge of the river from 2010 to 2020. The performance of the two models was subjected to statistical test based on correlation coefficient (r) and the root‐mean‐square error. The result showed that autoregressive integrated moving average performs better considering the level of root‐mean‐square error and higher correlation coefficient.     

Temporal Change Analysis Based on Data Characteristics and Nonparametric Test

Based on data characteristics and nonparametric test, a new statistical temporal change analysis approach is proposed. The new approach consists of data characteristics analysis, temporal change analysis (including both change point and trend analysis), and result interpretation. Data characteristics are firstly investigated, especially with respect to the assumptions of independence and normality. Then proper nonparametric methods are chosen based on the detected characteristics of the observed data to analyze change points and monotonous linear trend for each of the segments divided by the change points. To avoid shortcoming of the traditional approach of carrying out the trend analysis before change point analysis, it is proposed in this paper that change point detection be performed before trend analysis. At last, statistical analysis results are interpreted according to the physical mechanism of observations. As a study case, the proposed approach has been carried out on three annual discharge series of the Yangtze River at the Yichang hydrological station. The investigations of data characteristics show that the observed data do not meet the assumptions of being independent and identically Gaussian-distributed. So the nonparametric Pettitt’s test was adopted to detect abrupt changes in the mean levels, followed by trend analysis using the nonparametric Mann-Kendall (MK) test. Results indicate the proposed approach is both reliable and reasonable for the temporal change analysis.     

银川市降水量正态分布特征

分析降水量正态分布特征,可以为降水量分析与预测中选用合适的统计方法和统计模型提供依据,提高降水量分析与预测能力。利用银川市4个气象站(银川站、永宁站、贺兰站、灵武站)1960～2008年的降水量实测资料,运用2χ拟合优度检验和柯尔莫哥洛夫——斯米诺夫检验(K-S检验)对各站月、季和年降水量进行了正态分布检验。检验结果表明:银川市各气象站年降水量和季降水量均服从正态分布,夏季降水量正态性最好,冬季降水量正态性最差。6～9月份银川市各气象站月降水量均服从正态分布;11月至次年2月份月降水量均不服从正态性分布。各气象站中,通过正态性检验月份最多的是灵武站,通过正态性检验月份最少的是银川站,这与银川站多出现极端降水有一定关系。     

Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach

This paper presents a Takagi Sugeno (TS) fuzzy method for predicting future monthly water consumption values from three antecedent water consumption amounts, which are considered as independent variables. Mean square error (MSE) values for different model configurations are obtained, and the most effective model is selected. It is expected that this model will be more extensively used than Markov or ARIMA (AutoRegressive Integrated Moving Average) models commonly available for stochastic modeling and predictions. The TS fuzzy model does not have restrictive assumptions such as the stationarity and ergodicity which are primary requirements for the stochastic modeling. The TS fuzzy model is applied to monthly water consumption fluctuations of Istanbul city in Turkey. In the prediction procedure only lag one is considered. It is observed that the TS fuzzy model preserves the statistical properties. This model also helps to make predictions with less than 10% relative error.     

Three different models to evaluate water discharge: An application to a river section at Vinh Tuy location in the Lo river basin,Vietnam

This study presents three different models, namely power-law rating curve, one-dimensional lateral distribution method (1D–LDM), and gated recurrent network (GRU) model that can be applied to evaluate water discharge from water surface elevation time-series in a river cross-section for a long time period. A river section at Vinh Tuy location on the Lo river basin (Vietnam) is used to demonstrate the models. Appropriate values of modelling parameters are carefully determined using (i) the daily observed discharge values collected in the period from 2012 to 2018 and (ii) five error estimates for quantitatively assessing the agreement between estimated and observed water discharges. The results showed that all three models reproduced very well the observed discharge values, with root mean square error and mean absolute error, as well as mean error of discharge, are only about 5.5% of the maximum value of discharge monitoring in the studied cross-section, while Nash–Sutcliffe efficiency and Pearson’s correlation coefficient are greater than 0.89. The models are then applied to evaluate discharge values in the studied cross-section for the period from 1972 to 2011, revealing that statistical indicators, i.e. mean value, standard derivation, and covariance of estimated water discharge, are consistent with those obtained from the observations. Among three investigated models, the GRU model was finally proved to be the best one, providing even better results than the 1D-LDM and power-law rating curve.     

大坝变形监控数据处理的粗差识别方法及应用效果分析

正大坝安全监控的核心就在于通过各种监控理论与方法对监测资料进行分析,建立各类监控模型及监控指标,据此定量分析大坝及坝基的安全状态,监控大坝的安全运行,使大坝在安全运行的前提下充分发挥工程效益。大坝安全监测资料是大坝运行状态的直接反应,监测值的变化与大坝运行的环境荷载以及大坝本身结构形态有关,同时监测系统的稳定性也是重要的影响因素。大坝监测数据的异常一般由结构形态变化、环境量异常、系统改造、人为误测、误计或系统故障等     

Integrating Ground-based Observations and Radar Data Into Gridding Sub-daily Precipitation

A new and general approach is proposed for interpolating 6-h precipitation series over large spatial areas. The outputs are useful for distributed hydrological modelling and studies of flooding. We apply our approach to large-scale data, measured between 2014 and 2016 at 159 weather stations network of Meteo Romania, using weather radar information and local topography as ancillary data. Novelty of our approach is in systematic development of a statistical model underlying the interpolation. Seven methods have been tested for the interpolation of the 6-h precipitation measurements: four regression methods (linear regression via ordinary least squares (OLS), with and without logarithmic transformation, and two models of generalized additive model (GAM) class, with logarithmic and identity links), and three regression-kriging models (one uses semivariogram fitted separately every 6-h, based on the residuals of the GAM with identity links models, and other two with pooled semivariograms, based on the OLS and GAM with identity links models). The prediction accuracy of the spatial interpolation methods was evaluated on a part of the dataset not used in the model-fitting stage. Due to the good results in interpolating sub-daily precipitation, normal general additive model with identity link followed with kriging of residuals with kriging parameters estimated from pooled semivariograms was applied to construct the final 6-h precipitation maps (PRK-NGAM). The final results of this work are the 6-h precipitation gridded datasets available in high spatial resolution (1000 m?×?1000 m), together with their estimated accuracy.