共查询到18条相似文献，搜索用时 125 毫秒
1.
本文应用回归—马尔可夫链联合预测地震的方法,结合川、滇强震的特点,对川、滇强震进行了计算,并作了预测验证。结果表明,该方法对川、滇强震的预测效果较好。 相似文献
2.
3.
CART——分类与回归树方法介绍 总被引：7，自引：0，他引：7
分类与回归树是一种非常有趣并且十分有效的非参数分类和回归方法，它通过构建二叉树达到预测目的，该方法是四位美国统计学家耗时十多年辛勤劳动的成果，笔者认为该方法在古生物化石分类和鉴定以及矿产资源预测等方面颇有用武之地。本文只对该方法作简单介绍，希望能对从事预测工作的读者有所启发。美国加利福尼亚统计软件公司已开发了ＣＡＲＴ方法软件。 相似文献
4.
对于大型水库,在作出诱震危险性评估以后,需要预测可能的发震震级.利用水库综合影响参数E与大量震例的实发震级进行回归,可以获得预测方程.本文依据文献的资料,研究回归模型的精度及误差问题,并给出经过优化后的预测方程. 相似文献
5.
6.
水库地震震级预测的讨论 总被引：2，自引：2，他引：0
作者于１９８７年提出用水库“综合影响参数Ｅ”预测水库地震最大震级Ｍ的方法以来,为许多工程和科研人员引用或讨论．后来,除Ｅ外我们又增加了最大库深Ｈｍａｘ,并把它们都视为具有随机统计偏差的变量进行回归．为使关心的读者更多了解本法,本文讨论了回归变量的物理意义及其随机性、回归方法、几种回归结果的比较,以及实际应用时的修正问题． 相似文献
7.
用｛Ｙ＿Ｋ｝Ｋ＝１，２，…，ｎ表示量Ｙ的巳有记载，当Ｙ＿Ｋ与Ｋ相关较强时，有些文章中就由Ｙ关于序号Ｋ的回归方程，取作为Ｙ在下一步的预测或预测的过渡值。本文对这种按顺序回归的回归系数进行剖析，得知：ｎ稍大时，前期与中期的数据Ｙ＿Ｋ在序回归中起主要作用，代表新近信息的后期Ｙ＿Ｋ作用甚小，因而回归值在尾端与实际值有较大偏离。加之，系数ｂ只是增量Ｙ＿Ｋ－Ｙ＿（Ｋ－１）的一种加权平均，以ｂ为步长外延未能刻划Ｙ＿（ｎ＋１）－Ｙ＿ｎ的起伏，故地震预测中应用序回归难有良好实效。 相似文献
8.
光耀华同志近作“水库地震震级预测的回归模型”(《华南地震》1993年第2期,以下简称“光文”),对本人拙文“水库地震最大震级的预测”(“人民珠江”,1992年第5期,以下简称“原文”)作了很高评价并进行了讨论,本人表示感谢。现就“光文”对“原文”提出的两个问题答复如下。 相似文献
9.
城市桥梁震害预测方法的探讨 总被引：10，自引：2，他引：8
简要介绍了城市桥梁的六种震害预测方法。在确定采用哪种方法时,要根据桥梁的结构型式、桥墩材料、墩台高度、基础类型等因素来确定。在详细介绍了回归统计法的基础上,通过实际桥梁的震害与回归统计法的预测进行比较,检验了回归统计法的可靠性。 相似文献
10.
水库诱发地震震级(M)的预测是在地震工程中的一项重要任务。本文采用支持向量机(SVM)和高斯过程回归(GPR)模型根据水库的参数预测了水库诱发地震震级(M)。综合参数(E)和最大的水库深度(H)作为支持向量机和高斯过程回归模型的输入参数。我们给出一个方程确定水库诱发地震震级(M)。将本文开发的支持向量机和建立的高斯过程回归方法与人工神经网络(ANN)方法相比。结果表明,本文研发的支持向量机和高斯过程回归方法是预测水库诱发地震震级(M)的有效工具。 相似文献
11.
Investigating the extrapolation performance of neural network models in suspended sediment data 总被引：1，自引：1，他引：0
A. O. Pektas 《水文科学杂志》2017,62(10):1694-1703
Suspended sediment modelling is a quite significant issue in hydrology. The prediction of suspended sediment has taken the attention of several scientists in water resources. With extrapolation, the forecasting ability of the employed forecasting model beyond the calibration range is investigated. In the present study, different smoothing parameters are used to differentiate the kurtosis of the local critical points (local minima and maxima). The two models used are an artificial neural network (ANN) model and a multiple linear regression (MLR) model for prediction in order to examine the model extrapolation ability. The ANN model provides closer estimations to the observed peaks, being higher than the corresponding MLR ones. For the local minima, the ANN predictions are higher than the MLR predictions. As there are limited local points, all the remaining ANN predictions are lower than the MLR ones except for one point. 相似文献
12.
我国是一个地质灾害多发的国家,特别是滑坡发生的次数比较多、危害性比较大。因此对滑坡的位移进行监控预测有着十分重要的意义。对于滑坡位移变化的非线性问题,可以利用支持向量机在回归分析中的方法——ε-支持向量回归机（ε-SVR）进行预测,该方法基于统计学理论,在处理小样本、非线性、高维数等问题上有一定的优势。以福建八尺门滑坡的监测数据为例,将前面的17个位移数据作为学习样本,后面的6个位移数据作为预测样本,采用不同的核函数分别进行位移预测来与原始监测值进行对比,比较其预测精度。结果显示,该方法产生的预测值与原始监测值之间的误差比较小,其位移变化趋势与原始数据的变化趋势也基本一致,这说明该方法预测精度高,实用性强。 相似文献
13.
In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
14.
根据洪湖2014—2019年水质及藻类监测数据,运用综合营养状态指数法评价了丰、平、枯3个时期的营养状态.在此基础上运用逐步回归分析法确定影响藻类生长的显著因子,并根据不同水量不同营养状态细分9种情形对藻类生长做回归预测分析,同时运用BP神经网络模型对回归预测的结果进行比较验证.结果表明:洪湖丰、平水期以蓝藻门为主,枯水期以硅藻门为主;湖泊的营养状态处于中度富营养与轻度富营养之间.分析各时期藻种生物量与影响因子的相关性,发现丰水期控制因子有水温、CODMn和透明度;平水期和枯水期控制因子有水温、总氮、总磷.以2014—2018年数据逐步回归分析得出枯水期+中营养和枯水期+轻度富营养决定系数较低,其余7种时期决定系数均在0.5以上,说明逐步回归并不适用于所有时期.使用2014—2018年的数据进行神经网络训练和验证,2019年的数据进行预测,比较BP神经网络与逐步回归的均方根误差发现全年预测时BP神经网络效果更好;枯水期+中营养和枯水期+轻度富营养逐步回归效果较好,逐步回归的均方根误差仅为1600～4000;丰水期和平水期2种方法预测效果相当.合理地选择预测模型能为湖泊水华做出预警,控制显著变量可以达到防治水华污染的效果. 相似文献
15.
Regression‐based regional flood frequency analysis (RFFA) methods are widely adopted in hydrology. This paper compares two regression‐based RFFA methods using a Bayesian generalized least squares (GLS) modelling framework; the two are quantile regression technique (QRT) and parameter regression technique (PRT). In this study, the QRT focuses on the development of prediction equations for a flood quantile in the range of 2 to 100 years average recurrence intervals (ARI), while the PRT develops prediction equations for the first three moments of the log Pearson Type 3 (LP3) distribution, which are the mean, standard deviation and skew of the logarithms of the annual maximum flows; these regional parameters are then used to fit the LP3 distribution to estimate the desired flood quantiles at a given site. It has been shown that using a method similar to stepwise regression and by employing a number of statistics such as the model error variance, average variance of prediction, Bayesian information criterion and Akaike information criterion, the best set of explanatory variables in the GLS regression can be identified. In this study, a range of statistics and diagnostic plots have been adopted to evaluate the regression models. The method has been applied to 53 catchments in Tasmania, Australia. It has been found that catchment area and design rainfall intensity are the most important explanatory variables in predicting flood quantiles using the QRT. For the PRT, a total of four explanatory variables were adopted for predicting the mean, standard deviation and skew. The developed regression models satisfy the underlying model assumptions quite well; of importance, no outlier sites are detected in the plots of the regression diagnostics of the adopted regression equations. Based on ‘one‐at‐a‐time cross validation’ and a number of evaluation statistics, it has been found that for Tasmania the QRT provides more accurate flood quantile estimates for the higher ARIs while the PRT provides relatively better estimates for the smaller ARIs. The RFFA techniques presented here can easily be adapted to other Australian states and countries to derive more accurate regional flood predictions. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
16.
ABSTRACT Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection. 相似文献
17.
18.
随着川滇地区强震记录的不断增加,为了建立更符合该区域地震动特征的预测模型,文中基于该区域现有的地震动数据,通过随机效应回归模型建立适用于川滇地区的地震动预测模型;2021年5月21日,云南省大理州漾濞县发生6.4级地震,为了分析文中预测模型对漾濞地震的适用性,首先根据预测模型的适用范围选取合适的漾濞地震数据,计算真实记... 相似文献