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1.
在大坝变形监测统计模型研究的基础上,针对传统大坝变形监测回归模型存在的不足,将逐步回归模型与智能优化算法相结合,提出．了一种基于人工蜂群算法一逐步回归分析的大坝变形监控模型。该模型以逐步回归方法为基础,利用相关性分析、多重共线性分析等方法对观测数据进行处理,进而对大坝回归模型的荷载集变量进行筛选和评价,并将改进的人工蜂群算法引入回归模型分析,对荷载集系数进行优化和重新评估。人工蜂群算法是一种新型的群体智能优化方法,具有全局智能性搜索、鲁棒性强等优点,将其引入大坝安全监控建模领域,同时为改进人工蜂群算法的局部搜索性能,引入了单纯形操作算子。实例分析表明,与同类模型相比,所提出模型在一定程度上改善了拟合效果,达到了简化模型、提高拟合精度和增强模型预测能力的目的。 相似文献
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有效地压制地震信号中的噪声是地震信号解释和后续处理的重要环节之一.本文建立两种双树复小波域双变量模型对地震信号中的随机噪声进行压制.地震信号经双树复小波变换后,同一方向实部与虚部系数、实部(或虚部)系数与对应的模之间存在较强的相关性.鉴于此,对同一方向实部与虚部小波系数建立双变量模型,从含噪地震信号小波系数中估计原始信号的小波系数,再基于双树复小波逆变换重构得到降噪后的地震信号.进一步对同一方向实部(或虚部)系数与对应的模建立双变量模型,得到地震信号随机噪声压制的第二种双树复小波域双变量方法.最后对合成地震记录和实际地震资料中的随机噪声进行压制的实验结果证实本文两种方法都能够有效地压制地震信号中的随机噪声. 相似文献
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实验数据表明土体参数具有很大的空间变异性,而随机场理论为模拟土体参数空间变异性提供了有效途径。因为传统的谱表示法(SRM)无法正确模拟多维多元随机场参数间的互相关性,提出支持向量机法(SVM)与SRM耦合的方法。SVM是基于统计学习理论和结构风险最小化原理基础上的通用机器学习方法,它在解决小样本、非线性和高维模式识别问题中表现出诸多优势。以土体抗剪强度参数:黏聚力c和内摩擦角φ为例,通过实验证明二者之间存在天然负相关性,即为二维二元随机场。结果表明,在样本数量较少的条件下,基于耦合算法模拟随机场不仅能有效地描述变量的自相关性,而且能够准确地描述变量间的互相关性,为解决小样本条件下模拟多维多元随机场提供了一种有效的方法。 相似文献
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《地震地质》2015,(3)
面对越来越多的观测数据、越来越复杂的地电模型,大地电磁法的高维正反演需要发展高效、稳定的正、反演计算新技术。多重网格法是求解椭圆型偏微分方程最优化的方法之一,近些年来被广泛地用作大规模、高精度方程求解的加速器。目前,多重网格法多基于矩形网格来构造粗细不同的层次网格组,但是矩形网格不能适应几何形状复杂的区域并且不支持局部加密细化从而限制了多重网格法的应用。文中提出一种采用Delaunay三角网的非结构化多重网格生成算法,该算法能够自动对复杂区域生成粗细不同的网格,并且每层网格单元具有良好的形状比和可控的尺寸大小。文中采用该算法实现了对复杂地电模型的非结构多重网格的自动生成,解决了大地电磁多重网格正反演计算中复杂模型离散化这一关键的技术问题。 相似文献
5.
频率域全波形反演是重要的地震成像方法,而频率域波动方程数值模拟是频率域全波形反演的基础.对于大规模的问题,由于受存储和计算量的限制,基于LU分解的直接方法一般不再适用,而是采用迭代方法.基于多重网格预条件的双共轭梯度稳定化方法是一种重要的迭代方法.本文重点讨论了多重网格预条件求解过程中的松弛因子选择方法,研究结果表明,(1)对于一般选取的松弛因子,随模型复杂性的增加,所能计算的重数逐渐下降,方法的实用性也随之下降;(2)对于复杂模型,采用局部模式分析方法选取松弛因子,提高了所能计算的重数,保证了多重网格方法的收敛性和实用性.这些研究成果对基于多重网格预条件的迭代算法的实际应用具有重要意义. 相似文献
6.
混杂的全局最优化算法是一种模拟退火法和下坡的单纯形法相结合的方法，该法用来反演远震体波求解震源参数，在时间域对震源时间函数加约束，反演双力偶参数而不是反演矩张互通主考虑多重震源导致非线性和多重模型问题，在这些问题中，目标函数包括许多局部极小值。 相似文献
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高斯混合模型(Gaussian Mixture Model, GMM)可以用来描述储层性质的多峰分布特性,多峰特性主要是由于它们在不同离散变量内的变化而引起的.在高斯混合模型中,高斯分量的权值代表离散变量的概率.然而,基于高斯混合模型的贝叶斯线性反演可能会对某些点的离散变量错误地分类,进而影响连续变量的反演结果,尤其存在强噪声的时候.在本文中,我们考虑了离散变量的空间变化性,并将高斯混合模型与序贯指示模拟(Sequential Indicator Simulation, SIS)相结合来确定离散变量的后验条件权值,形成了结合序贯指示模拟的贝叶斯高斯混合线性反演方法.该方法能够准确地对离散变量进行归类,且具有良好的抗噪性.通过模型试算,我们证明了这种方法的可行性,并在实际资料中取得了较好的结果. 相似文献
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针对现有的河道水流洪水演算模型只能模拟单一变量(流量或水位)的问题,以水流连续方程和河段蓄水量的两种不同表达形式(蓄水量等于平均过水断面面积与河段长乘积,蓄水量等于河段平均流量与传播时间的乘积)为基础,对马斯京根模型进行了通用性改进,提出了双变量耦合通用演算模型.选取了四大水系(包括内陆河流和入海河流)的16个河段汛期洪水资料进行模型检验,模型验证考虑了地理范围、不同的河段特征和水力特征、洪水量级等因素,全面地检验了模型结构的合理性和模拟实际洪水的有效性.将双变量耦合通用演算模型与传统的马斯京根法进行了效果比较,结果表明双变量耦合通用演算模型的模拟精度高于马斯京根法,模拟效果比马斯京根法稳定一些,而且具有较好的通用性. 相似文献
11.
Francis O. Odemerho 《Journal of Hydrology》1984,70(1-4):85-100
Stream-channel morphologic responses are found to be related to different parameters measuring traditional agricultural land-use patterns and practices in 50 small headwater basins in southwest Nigeria. The problem of intercorrelations among these parameters made it initially difficult to establish their precise channel enlargement effects and to calibrate an impact prediction model. Through factor analysis of the 22 land-use and morphometric parameters, six factors identified as measures of traditional land-use practice, farm size, planting activities, shortened fallow, relief and overland flow, were found to account for 86% of the variance in the data. The factor-defining variables are length of cropping period, areas in short fallow, farm-plot size, length of farm preparation, relief ratio and overland flow. In a multiple regression analysis, only the first three variables were found to be statistically significant in explaining stream-channel morphologic responses. Thus, areas in short fallows, average farm size and length of cropping period adequately described those aspects of the traditional farming practices that affect basin hydrologic and channel responses. Since these variables were orthogonally derived, they formed the basis for the evaluation of the channel impact status of traditional land-use activities. The duplication of information and effects in the original 22-variable full-rank model were removed while utilizing the three-factor reduced model. 相似文献
12.
Using the SWAT model to assess impacts of land use changes on runoff generation in headwaters 
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下载免费PDF全文 The upstream regions of the Three Gorges Reservoir (TGR) have undergone significant changes in land use during recent years, and these changes have strongly influenced runoff generation downstream. In this study, the relationships between land use changes and corresponding hydrological responses in the Dong and Puli River basins in the upstream region of the TGR were quantified using the runoff coefficient. Empirical regression equations between the runoff coefficient and the percentage of land use types were developed for the study area using partial least squares regression (PLSR). The Soil and Water Assessment Tool was used to simulate the runoff generation processes in the two basins, and land use maps developed using Landsat Thematic Mapper images from 2000, 2005, and 2010 were compared to extract information on changes in land use. The results showed that the total area of forest and pasture decreased over the 10‐year study period, while paddy fields and upland increased in both basins. These land use changes dramatically affected hydrological processes. Evapotranspiration decreased by 2.13% and 2.41% between 2000 and 2010 in the Dong and Puli River basins, respectively, whereas quickflow, infiltration, and baseflow increased to varying degrees. The PLSR modeling results showed that upland had a negative effect on the runoff coefficient and was the most influential land use type in the study area. In contrast, a positive effect of forest on runoff generation was found in most of the regression models. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
13.
《水文科学杂志》2013,58(3):606-622
Abstract The impact of changes in land-use/cover on streamflow at short time scales is evaluated by examining the changes in the flow duration curve (FDC) before and after land-use/cover change. The FDC characterizes the relationship between the magnitude and frequency and hence provides the complete range of streamflow over time. Two issues need to be considered in predicting the FDC due to land-use changes: (a) the appropriate parametric form of the FDC that enables application of the same expression of FDCs before and after the changes; and (b) the ability of parameters to capture and characterize the effect of land-use changes. In this paper, we propose a model which is a four-parameter double power form as a function of the FDC, where the two hydrological parameters represent the mean annual flow ([Qbar]) and the cease-to-flow point (τ expressed as a percentage), while the other two parameters (α and β) determine the shape of the FDC. The properties of this function are investigated in order to assign parameters to cope with the land-use changes. The model is used for several typical catchments in Australia for demonstration. 相似文献
14.
Qian Lu Shijie Wang Xiaoyong Bai Fang Liu Shiqi Tian Mingming Wang Jinfeng Wang 《Acta Geochimica》2020,(1):116-126
In near-infrared spectroscopy,the traditional feature band extraction method has certain limitations.Therefore,a band extraction method named the three-step extraction method was proposed.This method combines characteristic absorption bands and correlation coefficients to select characteristic bands corresponding to various spectral forms and then uses stepwise regression to eliminate meaningless variables.Partial least squares regression(PLSR)and extreme learning machine(ELM)models were used to verify the effect of the band extraction method.Results show that the differential transformation of the spectrum can effectively improve the correlation between the spectrum and nickel(Ni)content.Most correlation coefficients were above 0.7 and approximately 20%higher than those of other transformation methods.The model effect established by the feature variable selection method based on comprehensive spectral transformation is only slightly affected by the spectral transformation form.Infive types of spectral transformation,the RPD values of the proposed method were all within the same level.The RPD values of the PLSR model were concentrated between 1.6 and 1.8,and those of the ELM model were between 2.5 and2.9,indicating that this method is beneficial for extracting more complete spectral features.The combination of the three-step extraction method and ELM algorithm can effectively retain important bands associated with the Ni content of the soil.The model based on the spectral band selected by the three-step extraction method has better prediction ability than the other models.The ELM model of the first-order differential transformation has the best prediction accuracy(RP^2=0.923,RPD=3.634).The research results provide some technical support for monitoring heavy metal content spectrum in local soils. 相似文献
15.
Jagdish Krishnaswamy Michael Lavine Daniel D. Richter Karl Korfmacher 《Advances in water resources》2000,23(8):2078
Modeling of sediment transport in relation to changing land-surface conditions against a background of considerable natural variability is a challenging area in hydrology. Bayesian dynamic linear models (DLMs) however, offer opportunities to account for non-stationarity in relationships between hydrologic input and basin response variables. Hydrologic data are from a 40 years long record (1951–1990) from the 5905 km2 Yadkin River basin in North Carolina, USA. DLM regressions were estimated between log-transformed volume-weighted sediment concentration as a response and log-transformed rainfall erosivity and river flow, respectively, as input variables. A similar regression between log-transformed river flow and log-transformed basin averaged rainfall was also analyzed. The dynamic regression coefficient which reflects the erodibility of the basin decreased significantly between 1951 and 1970, followed by a slowly rising trend. These trends are consistent with observed land-use shifts in the basin. Bayesian DLMs represent a substantial improvement over traditional monotonic trend analysis. Extensions to incorporate multiple regression and seasonality are recommended for future applications in hydrology. 相似文献
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Y. Zee Ma 《Journal of Applied Geophysics》2011,75(2):390-400
Statistical methods are commonly used for prediction of geoscience and engineering properties. This commonly involves selection of a small number of variables among a large number of available geological, geophysical, petrophysical and engineering variables. The conventional view is to select the variables that have highest correlations with the variable of concern. In this article, we show that this may not always be a wise approach because it ignores a critical aspect of the variable interaction — suppression. We review the suppression phenomenon, and discuss three types of suppression in multiple linear regression of geoscience and reservoir properties. We present examples using wireline logs, seismic attributes, and other engineering parameters. We show that understanding the suppression phenomenon is important for selecting appropriate variables for optimal prediction of geoscience and reservoir properties. 相似文献
17.
AbstractSpatial error regression is employed to regionalize the parameters of a rainfall–runoff model. The approach combines regression on physiographic watershed characteristics with a spatial proximity technique that describes the spatial dependence of model parameters. The methodology is tested for the monthly abcd model at a network of gauges in southeast United States and compared against simpler regression and spatial proximity approaches. Unlike other comparative regionalization studies that only evaluate the skill of regionalized streamflow predictions in ungauged catchments, this study also examines the fit between regionalized parameters and their optimal (i.e. calibrated) values. Interestingly, the spatial error model produces parameter estimates that better resemble the optimal parameters than either of the simpler methods, but the spatial proximity method still yields better hydrologic simulations. The analysis suggests that the superior streamflow predictions of spatial proximity result from its ability to better preserve correlations between compensatory hydrological parameters.
Editor D. Koutsoyiannis; Associate editor Y. Gyasi-Agyei 相似文献
18.
Wen-Juan Ding Ren-Qing Wang Da-Qian Wu Jian Liu 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(4):899-907
Land-use changes are generally recognized as multi-scale complex systems with processes and driving factors operating at different scales. Traditional linear approaches could not adequately acquire the nonlinear features in complex land-use changes. A multi-state artificial neural network based cellular automata (MANNCA) model and a multi-state autologistic regression based cellular automata (MALRCA) model were developed to simulate complex land-use changes in the Yellow River Delta during the period of 1992–2005. Relatively good conformity between simulated and actual land-use patterns indicated that the two models were able to simulate land-use dynamics effectively and generate realistic land-use patterns. The MANNCA model obtained higher fuzzy kappa values over MALRCA model at all the three simulation periods, which indicated that artificial neural networks could more effectively capture the complex relationships between land-use changes and a large set of spatial variables. Although the MALRCA model does have some advantages, the proposed MANNCA model represents a more effective approach to simulate the complex and nonlinear land-use evolutionary process. 相似文献
19.
Mehdi Zare 《Journal of Seismology》2017,21(4):591-613
This study aims to develop a new earthquake strong motion-intensity catalog as well as intensity prediction equations for Iran based on the available data. For this purpose, all the sites which had both recorded strong motion and intensity values throughout the region were first searched. Then, the data belonging to the 306 identified sites were processed, and the results were compiled as a new strong motion-intensity catalog. Based on this new catalog, two empirical equations between the values of intensity and the ground motion parameters (GMPs) for the Iranian earthquakes were calculated. At the first step, earthquake “intensity” was considered as a function of five independent GMPs including “Log (PHA),” “moment magnitude (MW),” “distance to epicenter,” “site type,” and “duration,” and a multiple stepwise regression was calculated. Regarding the correlations between the parameters and the effectiveness coefficients of the predictors, the Log (PHA) was recognized as the most effective parameter on the earthquake “intensity,” while the parameter “site type” was removed from the equations since it was determines as the least significant variable. Then, at the second step, a simple ordinary least squares (OLS) regression was fitted only between the parameters intensity and the Log (PHA) which resulted in more over/underestimated intensity values comparing to the results of the multiple intensity-GMPs regression. However, for rapid response purposes, the simple OLS regression may be more useful comparing to the multiple regression due to its data availability and simplicity. In addition, according to 50 selected earthquakes, an empirical relation between the macroseismic intensity (I0) and MW was developed. 相似文献
20.
Estimation of flood quantiles in ungauged catchments is a common problem in hydrology. For this, the log-linear regression model is widely adopted. However, in many cases, a simple log transformation may not be able to capture the complexity and nonlinearity in flood generation processes. This paper develops generalized additive model (GAM) to deal with nonlinearity between the dependent and predictor variables in regional flood frequency analysis (RFFA) problems. The data from 85 gauged catchments from New South Wales State in Australia is used to compare the performances of a number of alternative RFFA methods with respect to variable selection, variable transformation and delineation of regions. Four RFFA methods are compared in this study: GAM with fixed region, log-linear model, canonical correlation analysis (to form neighbourhood in the space catchment attributes) and region-of-influence approach. Based on the outcome from a leave-one-out validation approach, it has been found that the GAM method generally outperforms the other methods even without linking GAM with a neighbourhood/region-of-influence approach. The main strength of GAM is that it captures the non-linearity between the dependent and predictor variables without any restrictive assumption. The findings of this study will encourage other researchers worldwide to apply GAM in RFFA studies, allowing development of more flexible and realistic RFFA models and their wider adoption in practice. 相似文献