共查询到19条相似文献，搜索用时 218 毫秒
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本文提供了一个描述西藏地区年、季降水量空间分布的估算模型.利用卫星遥测数字化地形高程资料和西藏地区仅有的27个常规气象站的多年平均降水整编资料,根据地形坡向站点分为三类.再采用多元逐步回归方法,建立西藏地区的年、季降水量和经度、纬度、海拔高度、坡度、坡向、遮蔽度等6个地理、地形因子之间的关系模型,估算西藏地区降水量的空间分布.结果表明,此方法建立的关于西藏地区降水量与诸因子之间方程的相关性显著,平均绝对误差、相对误差分别为093mm和116%,对估算模型进行F检验,均通过置信度为095的相关检验,回归效果较显著.分析表明估算降水能够定量、定性地再现西藏地区的实际降水分布. 相似文献
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针对出行生成预测中变量众多以及多重相关性问题，以常州调查数据为样本，分别采用逐步回归分析方法和偏最小二乘回归（PLSR）方法，建立基于土地利用形态的交通发生量模型。并对两者建模方法进行分析比较．结果表明PLSR能有效地解决变量多重相关性问题，模型意义清晰，效果显著。 相似文献
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目前对岩溶地面塌陷的研究仍主要在宏观和定性的水平上.偏最小二乘通径模型采用一系列一元或多元线性回归的迭代求解,无需对观测变量做特定的概论分布假设,对样本点容量的要求也十分宽松,不存在模型不可识别的问题,是一种实用和有效的线性统计建模方法.本文在分析武汉市岩溶塌陷影响因素的基础上,基于GIS技术,采用偏最小二乘通径模型对武汉市岩溶塌陷危险性进行了预测.结果表明利用该模型可以取得较好的预测结果,在工程实际应用中有一定的借鉴意义. 相似文献
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基于模态参数化线性求解结构损伤的识别方法在工程中具有较为广泛的应用。然而，在噪声的干扰下，当结构可测量的模态阶数较少时，利用该方法求解的结果会出现大量的虚假损伤，严重扰乱真实的损伤信息。针对此问题，引入了一种基于偏最小二乘多线性回归建模的方法来对损伤识别结果进行降噪处理。通过对损伤结构的频率和振型信息添加一定水平的噪声干扰，分析确定结构单元的损伤参数，并利用偏最小二乘法重构线性方程组。同时，选择识别结果较为稀疏的解作为标准样本点，利用奇异值分解法回代求解结构的损伤参数。以桁架模型为例的数值模拟结果表明，在噪声干扰下，该方法与传统最小二乘法和奇异值分解法相比，不仅损伤识别结果准确，而且能够最大程度抑制虚假损伤的产生。 相似文献
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蓝藻水华暴发前,浮游植物群类结构的变化可通过其指示型色素的浓度变化来反映.为了同时反演叶绿素a、叶绿素b(绿藻门指示型色素)、叶绿素c(硅藻门指示型色素)和藻蓝素(蓝藻门的指示型色素)的浓度,利用偏最小二乘回归构建线性模型,通过2011年太湖实测吸收数据,较为准确地反演了叶绿素a和藻蓝素的浓度;针对无明显优势藻的春季数据集较为准确地反演了叶绿素b和叶绿素c的浓度.相对于经典最小二乘算法,偏最小二乘法在多色素混合的吸收光谱分析上更为有效.通过反演指示性色素浓度来反映藻类的分布,为富营养化湖泊主要藻类时空分布变化的遥感监测提供了一定的理论与技术支持. 相似文献
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经典最小二乘回归模型假设自变量没有误差而所有误差都集中于响应变量,但是许多应用领域中实际问题的自变量含有噪声数据,往往不符合这个假设,经典最小二乘回归模型不再适用。为克服这一缺陷,介绍了正交最小二乘回归模型和参数估计算法。对经典最小二乘和正交最小二乘回归系数进行了理论分析和计算机数值仿真,结果表明当自变量和响应变量都含有误差时,正交最小二乘法优于经典最小二乘法。最后将经典最小二乘法和正交最小二乘法用于蒙城地震台2001-2006年地震数据,确定了台站震级偏差校正公式,并对它们的结果进行了详细比较。 相似文献
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用维尼迪科夫(Vinedikov)调和分析方法,计算2008年1月1日至2010年12月31日兰州地震台PET重力仪观测资料,获得几个主要潮波的潮汐参数.对日均值采用最小二乘拟合法,分析非潮汐重力变化信息.基于一元线性回归,分析气压对重力观测的影响,结果表明:气压对重力观测具有一定影响. 相似文献
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最小二乘偏移是一种基于反射地震数据与地下反射率间线性关系而建立起来的地震数据线性反演方法，相比常规偏移成像具有更好的保幅性能.本文提出了一种基于照明补偿的单程波最小二乘偏移方法，首先利用单程波方程的稳定Born近似广义屏波场传播算子构建反射地震数据与地下反射率间的线性算子，然后再应用线性最优化方法求解最小二乘偏移所对应的线性反问题.在迭代求解最优化问题的过程中，以地震波场的地下照明强度作为迭代反演的预条件算子加快迭代的收敛速度.单程波传播过程中考虑了速度分界面产生的透射效应，并用单极震源代替常规偏移中的偶极震源.把本文提出的方法应用于层状理论模型和Marmosi模型地震数据的数值试验中均取得了理想的结果. 相似文献
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Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method. 相似文献
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The purpose of this study is to establish a relation between observed total precipitation values and estimations from a one-dimensional diagnostic cloud model. Total precipitation values estimated from maximum liquid water content, maximum vertical velocity, cloud top height, and temperature excess are also used to provide an equation for the total precipitation prediction. Data for this study were collected in Istanbul during the autumns of 1987 and 1988. The statistical models are developed with multiple regression technique and then comparatively verified with independent data for 1990. The multiple regression coefficients are in the range of 75% to 80% in the statistical models. Results of the test showed that total precipitation values estimated from the above techniques are in good agreement, with correlation coefficient between 40% and 46% based on test data for 1990. 相似文献
13.
Mogi模型在长白山天池火山区的应用 总被引：6，自引：0，他引：6
文中首先介绍了Mogi模型的阻尼最小二乘反演,得出应用形变资料联合反演压力源参数的公式,再指出用等效源半径反演时不同初始值对反演结果的影响,说明选择合适的初始值可以方便地得到可靠的反演结果。然后用此方法反演长白山天池火山区压力源的大小和位置参数。采用垂直和水平位移的联合反演,数据为2002—2005年共4期的水准和GPS观测资料,最后结合其它观测资料,分析近年来岩浆可能的活动特征。结果显示岩浆的位置在发生变化,体积增量逐年减小,表明火山岩浆在2002—2005年的活动逐年减弱 相似文献
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The Use of Radar and Gauge Measurements to Estimate Areal Precipitation for Several Czech River Basins 总被引：2，自引：0，他引：2
Two methods estimating areal precipitation for selected river basins in the Czech Republic are compared. The methods use radar precipitation (the radar-derived precipitation estimate based on column maximum reflectivity) and data from 81 on-line rain gauges routinely provided by the Czech Hydrometeorological Institute. Data from a dense network of climatological rain gauges (the average inter-station distance is approximately 8 km), the measurements of which are not available in real time, are utilized for the verification. The mean areal precipitation, which is used as the ground truth, is obtained by the weighted interpolation of the dense rain gauge network. The accuracy of the methods is evaluated by the root-mean-square-error.The first, pixel-related method merges radar precipitation with rain gauge data to obtain adjusted pixel values. The adjusting procedure combines radar and gauge values in one variable that is interpolated into all radar pixels. The adjusted pixel precipitation is calculated from radar precipitation and from the value of the combined variable. The areal estimates are determined by adding the corresponding pixel values. The second method applies a linear regression model to describe the relationship between the areal precipitation (dependent variable) and its estimates, which are determined from (i) non-adjusted radar precipitation and (ii) on-line rain gauge measurements interpolated into pixels. Classical linear regression, ridge regression and robust regression models are tested.Both the methods decrease the average areal error in comparison with the reference method, which uses the on-line rain gauge data only. The decrease is about 10% and 15% for the pixel-related and regression methods, respectively. When the estimates of the pixel-related method are included as predictors into the regression method then the improvement of accuracy is almost 25%. 相似文献
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This paper presents the development of a probabilistic multi‐model ensemble of statistically downscaled future projections of precipitation of a watershed in New Zealand. Climate change research based on the point estimates of a single model is considered less reliable for decision making, and multiple realizations of a single model or outputs from multiple models are often preferred for such purposes. Similarly, a probabilistic approach is preferable over deterministic point estimates. In the area of statistical downscaling, no single technique is considered a universal solution. This is due to the fact that each of these techniques has some weaknesses, owing to its basic working principles. Moreover, watershed scale precipitation downscaling is quite challenging and is more prone to uncertainty issues than downscaling of other climatological variables. So, multi‐model statistical downscaling studies based on a probabilistic approach are required. In the current paper, results from the three well‐reputed statistical downscaling methods are used to develop a Bayesian weighted multi‐model ensemble. The three members of the downscaling ensemble of this study belong to the following three broad categories of statistical downscaling methods: (1) multiple linear regression, (2) multiple non‐linear regression, and (3) stochastic weather generator. The results obtained in this study show that the new strategy adopted here is promising because of many advantages it offers, e.g. it combines the outputs of multiple statistical downscaling methods, provides probabilistic downscaled climate change projections and enables the quantification of uncertainty in these projections. This will encourage any future attempts for combining the results of multiple statistical downscaling methods. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
16.
Drought indices have been commonly used to characterize different properties of drought and the need to combine multiple drought indices for accurate drought monitoring has been well recognized. Based on linear combinations of multiple drought indices, a variety of multivariate drought indices have recently been developed for comprehensive drought monitoring to integrate drought information from various sources. For operational drought management, it is generally required to determine thresholds of drought severity for drought classification to trigger a mitigation response during a drought event to aid stakeholders and policy makers in decision making. Though the classification of drought categories based on the univariate drought indices has been well studied, drought classification method for the multivariate drought index has been less explored mainly due to the lack of information about its distribution property. In this study, a theoretical drought classification method is proposed for the multivariate drought index, based on a linear combination of multiple indices. Based on the distribution property of the standardized drought index, a theoretical distribution of the linear combined index (LDI) is derived, which can be used for classifying drought with the percentile approach. Application of the proposed method for drought classification of LDI, based on standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized runoff index (SRI) is illustrated with climate division data from California, United States. Results from comparison with the empirical methods show a satisfactory performance of the proposed method for drought classification. 相似文献
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Z. Sokol 《Studia Geophysica et Geodaetica》2006,50(1):105-120
A statistical post-processing methodology for application to numerical weather prediction (NWP) model outputs for precipitation
forecast is proposed. The post-processing is based on the model output statistics approach. The statistical relationships
are described by the multiple linear regression model, which is complemented by an iteration procedure to further correct
the regression outputs. Prognostic fields of the ALADIN/LACE (Aire Limitée Adaptation Dynamique Développement InterNational/Limited
Area Modelling in Central Europe) NWP model are used for the forecast of 6-hourly areal precipitation amounts at 15 river
basins. The NWP model integration starts at 00UTC and forecasts are calculated for lead times of +12, +18, +24 and +30 hours.
The post-processing models are developed separately for each lead time and for separate warm (April to September) and cool
(October to March) seasons. The forecasts are focused on large precipitation amounts. Using all the combinations, data from
four years (1999–2002) are divided into calibration data (3 years), where the models are developed, and verification data.
The models are evaluated by examining the root-mean-square error (RMSE), bias, and correlation coefficient (CC) on the verification
data samples.
The results show that the additional iteration procedure increases the forecast accuracy for a given range of precipitation
amounts and simultaneously does not deteriorate the bias, a situation which can arise when negative regression outputs are
set to zero. The post-processing method improves the forecast of the NWP model in terms of RMSE and CC. For large precipitation
amounts during the summer season, the decrease of RMSE reaches 10% to 20% depending upon the applied method of verification.
For the cool season, the decrease is somewhat smaller (7% to 15%). 相似文献
18.
An understanding of the factors that affect the spread of endemic bovine tuberculosis (bTB) is critical for the development of measures to stop and reverse this spread. Analyses of spatial data need to account for the inherent spatial heterogeneity within the data, or else spatial autocorrelation can lead to an overestimate of the significance of variables. This study used three methods of analysis—least-squares linear regression with a spatial autocorrelation term, geographically weighted regression (GWR) and boosted regression tree (BRT) analysis—to identify the factors that influence the spread of endemic bTB at a local level in England and Wales. The linear regression and GWR methods demonstrated the importance of accounting for spatial differences in risk factors for bTB, and showed some consistency in the identification of certain factors related to flooding, disease history and the presence of multiple genotypes of bTB. This is the first attempt to explore the factors associated with the spread of endemic bTB in England and Wales using GWR. This technique improves on least-squares linear regression approaches by identifying regional differences in the factors associated with bTB spread. However, interpretation of these complex regional differences is difficult and the approach does not lend itself to predictive models which are likely to be of more value to policy makers. Methods such as BRT may be more suited to such a task. Here we have demonstrated that GWR and BRT can produce comparable outputs. 相似文献
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A fast inversion technique for the interpretation of data from resistivity tomography surveys has been developed for operation on a microcomputer. This technique is based on the smoothness-constrained least-squares method and it produces a two-dimensional subsurface model from the apparent resistivity pseudosection. In the first iteration, a homogeneous earth model is used as the starting model for which the apparent resistivity partial derivative values can be calculated analytically. For subsequent iterations, a quasi-Newton method is used to estimate the partial derivatives which reduces the computer time and memory space required by about eight and twelve times, respectively, compared to the conventional least-squares method. Tests with a variety of computer models and data from field surveys show that this technique is insensitive to random noise and converges rapidly. This technique takes about one minute to invert a single data set on an 80486DX microcomputer. 相似文献