共查询到16条相似文献，搜索用时 156 毫秒
1.
针对地震中城市桥梁震害状态具有较强的非线性、复杂性的特点,采用了具有RBF核函数的最小二乘支持向量机(LS-SVM)算法。在大量收集我国地震中城市桥梁震害资料的基础上,将此算法引入桥梁的震害预测中,选取了地震烈度、上部结构、地基失效程度、支座类型、墩台高度、桥梁跨数和场地类别等因素作为模型的特征输入向量,建立了最小二乘支持向量机的桥梁震害预测模型。通过反复地样本训练及模型参数设置,仿真结果表明,该方法具有一定的准确度和可行性。基于最小二乘支持向量机的桥梁震害预测方法是一种可以用于地震中桥梁震害预测的良好方法。 相似文献
2.
应用简便、可靠的震害预测方法对我国大量存在的砌体结构进行抗震性能评估，是防震减灾工作的重要举措。基于支持向量机(support vector machine, SVM)理论提出了砌体结构震害预测新方法。首先，详细阐述了基于SVM的砌体结构震害预测新方法的基本原理及步骤；其次，确定了砌体结构的震害影响因子及量化值，建立了震害样本数据库及预测模型；最后，将SVM预测结果分别与实际震害结果和BP神经网络预测结果进行对比分析。结果表明，基于SVM模型的砌体结构震害预测方法步骤简单。结果可靠，在样本数据有限的情况下相对BP神经网络算法有较大的优势，可以用于砌体结构的震害预测。 相似文献
3.
为准确预测地震死亡人数,提出了基于主成分分析法(PCA)和粒子群算法(PSO)优化的支持向量机(SVM)模型。首先利用主成分分析法对地震死亡人数7个影响因子中的6个进行数据降维,同时对第7个发震时刻因子单独进行区间分类,然后对提取出的主成分进行归一化处理,将归一化的主成分数据作为支持向量机的输入向量,通过粒子群算法寻优获得最优支持向量机模型参数,最终建立基于PCA-PSO-SVM的地震死亡人数预测模型,并对5组样本进行死亡人数预测,同时对比分析包含和不包含发震时刻因子的2种情况下的模型预测效果。结果表明:在不考虑发震时刻因子的情况下,使用PCA-PSO-SVM模型的最小误差、最大误差和平均误差分别为0.85%、20%、10%,其平均误差相比PSO-SVM、SVM模型分别降低2.08%、2.28%;输入向量加入发震时刻因子分类数据后,PCA-PSO-SVM模型的最小误差、最大误差和平均误差分别为0.25%、20%、7.18%,其平均误差相比PSO-SVM、SVM模型分别降低3.34%、3.50%。因此,加入发震时刻因子后3种模型的平均误差明显降低,同时由于PCA-PSO-SVM模型进行主成分降维处理,能够明显提高运行效率和预测精度,故降低了模型复杂度。 相似文献
4.
王军 《地震地磁观测与研究》2018,39(3):181-188
为研究天然地震事件和爆破事件识别算法,对上海测震台网记录的上海周边区域天然构造地震和爆破事件记录进行小波包分解,并提取特征向量,提出用支持向量机（SVM）识别天然构造地震和人工爆破的算法。结果表明,基于SVM算法的向量识别分类方法,在天然地震和爆破识别中是可用的,准确率预计达85%以上。 相似文献
5.
6.
中国大陆强震时间序列预测的支持向量机方法 总被引：12，自引：2，他引：12
统计学习理论（Statistical Learning Theory或SLT）是研究有限样本情况下机器学习规律的理论。支持向量机（Support Vector Machines或SVM）是基于统计学习理论框架下的一种新的通用机器学习方法。它不但较好地解决了以往困扰很多学习方法的小样本、过学习、高维数、局部最小等实际难题，而且具有很强的泛化（预测）能力。文中使用支持向量机对中国大陆最大地震时间序列进行预测，预测次年的我国大陆最大地震震级，结果表明该方法具有较好的预报效果。研究结果还表明我国大陆强震活动除了与强震时间序列本身有关外，还与全球的强震活动、太阳黑子活动等有密切的关系。尽管这种关系还不清楚，但是通过支持向量机可以很好地反应出这种非线性关系。 相似文献
7.
为有效解决地震伤亡人数预测所需影响因子多、 运算量大、 模型训练烦琐等问题, 构建了主成分分析法(PCA)和遗传算法(GA)优化的支持向量机(SVM)模型, 采用PCA对地震伤亡人数影响因子进行降维以去除贡献率较低的主成分, 将贡献率较大的主成分作为支持向量机的输入变量, 以地震伤亡人数作为输出变量, 利用GA对SVM模型性能参数进行优化, 建立基于PCA-GA-SVM的地震伤亡人数预测模型, 并对测试样本进行预测, 结果表明: 与SVM模型、 GA-SVM模型和PCA-GA-BP模型相比, PCA-GA-SVM模型的预测准确率和运行效率分别提高 4.73%、 1.14%、 9.99% 和47.05%、 36.76%、 44.55%。结果显示, PCA-GA-SVM模型预测精度高, 泛化能力强, 能够科学合理地对地震伤亡人数作出预测。 相似文献
8.
9.
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
10.
地震前兆综合预测支持向量机模型研究 总被引：4，自引：0，他引：4
该文介绍了支持向量机算法的原理与回归方法。 采用支持向量机中的非线性回归算法与理论公式产生的多维样本, 对其进行了数值仿真实验。 利用该方法和地震前兆异常建立了最佳地震综合预测模型, 对获得的最佳模型进行了内符检验, 得出最佳模型的预测结果与实际震例的地震震级基本一致。 综合分析认为, 支持向量机无论在学习或者预测精度方面不但具有很大的优越性和具有较强的外推泛化能力, 而且基于支持向量机回归算法建立的地震前兆综合预测模型是可行的, 其获得的知识可较为准确地实现对主震震级的综合预测。 相似文献
11.
12.
针对影响地震伤亡人数的评价指标数量较多且各指标之间存在着复杂的非线性关系,运用机器学习理论,提出了基于支持向量机(Support Vector Machine)的地震伤亡人数预测模型;首先利用主成分分析法(Principle Component Analysis)对7个地震死亡人数影响指标进行数据降维,然后对提取出的主成分进行归一化处理,将归一化的主成分数据作为预测模型的输入向量,将地震伤亡人数作为预测模型的输出向量;以27个地震伤亡实例作为学习样本进行训练,运用网格搜索法(Grid Search Method)寻优获得最优支持向量机参数,最终建立基于PCA-GSM-SVM的地震死亡人数预测模型,并对5组样本进行死亡人数预测。结果表明:PCA-GSM-SVM模型的最小误差、最大误差和平均误差分别为5.12%、15.7%和9.16%,其平均误差相比于GSM-SVM模型和SVM模型分别降低6.51%和7.11%,因此PCA-GSM-SVM模型预测精度较高,可在工程实际中推广。 相似文献
13.
Due to the complex mechanisms of rockburst, there is no current effective method to reliably predict these events. A statistical learning method, support vector machine (SVM), is employed in this paper for kimberlite burst prediction. Four indicators \(\sigma_{\theta } ,\sigma_{c} ,\sigma_{t} ,W_{\text{ET}}\) are chosen as input indices for the SVM, which is trained using 108 groups of rockburst cases from around the world. Data uniformization is used to avoid negative impact of differing dimensions across the original data. Parameter optimization is embedded in the training process of the SVM to achieve optimized predictive ability. After training and optimization, the SVM reaches an accuracy of 95% in rock burst prediction for validation samples. The constructed SVM is then employed in kimberlite burst liability evaluation. The model indicated a moderate burst risk, which matches observed instances of rockburst at a diamond mine in north Canada. The SVM method ignores the focus on rockburst mechanisms, instead relying on representative indicators to develop a predictive model through self-learning. The prediction results show an excellent accuracy, which means this method has a potential application in rockburst prediction. 相似文献
14.
Evaporation estimation is an important issue in water resources management. In this article, a four‐season model with optimal input combination is proposed to estimate the daily evaporation. First, the model based on support vector machine (SVM) coupled with an input determination process is used to determine the optimal combination of input variables. Second, a comparison of the SVM‐based model with the model based on back‐propagation network (BPN) is made to demonstrate the superiority of the SVM‐based model. In addition, season data are used to construct the SVM‐based four‐season model to further improve the daily evaporation estimation. An application is conducted to demonstrate the performance of the proposed model. Results show that the SVM‐based model can select the optimal input combination with physical mechanism. The SVM‐based model is more appropriate than the BPN‐based model because of its higher accuracy, robustness and efficiency. Moreover, the improvement due to the use of the four‐season model increases from 3.22% to 15.30% for RMSE and from 4.84% to 91.16% for CE, respectively. In conclusion, the SVM‐based model coupled with the proposed input determination process should be used to select input variables. The proposed four‐season SVM‐based model with optimal input combination is recommended as an alternative to the existing models. The proposed modelling technique is expected to be useful to improve the daily evaporation estimation. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
15.
Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir 总被引：3，自引：1，他引：2
Yun Wang Shenglian Guo Hua Chen Yanlai Zhou 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(3):555-570
Due to the complexity of influencing factors and the limitation of existing scientific knowledge, current monthly inflow prediction accuracy is unable to meet the requirements of various water users yet. A flow time series is usually considered as a combination of quasi-periodic signals contaminated by noise, so prediction accuracy can be improved by data preprocess. Singular spectrum analysis (SSA), as an efficient preprocessing method, is used to decompose the original inflow series into filtered series and noises. Current application of SSA only selects filtered series as model input without considering noises. This paper attempts to prove that noise may contain hydrological information and it cannot be ignored, a new method that considerers both filtered and noises series is proposed. Support vector machine (SVM), genetic programming (GP), and seasonal autoregressive (SAR) are chosen as the prediction models. Four criteria are selected to evaluate the prediction model performance: Nash–Sutcliffe efficiency, Water Balance efficiency, relative error of annual average maximum (REmax) monthly flow and relative error of annual average minimum (REmin) monthly flow. The monthly inflow data of Three Gorges Reservoir is analyzed as a case study. Main results are as following: (1) coupling with the SSA, the performance of the SVM and GP models experience a significant increase in predicting the inflow series. However, there is no significant positive change in the performance of SAR (1) models. (2) After considering noises, both modified SSA-SVM and modified SSA-GP models perform better than SSA-SVM and SSA-GP models. Results of this study indicated that the data preprocess method SSA can significantly improve prediction precision of SVM and GP models, and also proved that noises series still contains some information and has an important influence on model performance. 相似文献
16.
Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs 总被引：1，自引：0，他引：1
This article employs Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of Evaporation Losses (E) in reservoirs. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε‐insensitive loss function has been adopted. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The input of SVM and RVM models are mean air temperature (T) ( °C), average wind speed (WS) (m/sec), sunshine hours (SH)(hrs/day), and mean relative humidity (RH) (%). Equations have been also developed for prediction of E. The developed RVM model gives variance of the predicted E. A comparative study has also been presented between SVM, RVM and ANN models. The results indicate that the developed SVM and RVM can be used as a practical tool for prediction of E. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献