共查询到18条相似文献，搜索用时 107 毫秒
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人工神经网络在地震中短期预报中的应用 总被引：4，自引：0，他引：4
本文将ＢＰ神经网络用于地震中短期预报。作者把一些常用的地震学指标作为神经网络的输入,而将ＢＰ神经网络的输出作为表征地震活动平静的特征参数Ｗｑ,井将其用于华北地区进行空间扫描,结果表明中强地震前１年左右或稍长时间,未来震中周围一般都开始出现Ｗｑ值的中短期异常区,证明本方法具有限好的中短期预报效果。 相似文献
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介绍了神经网络的一些基本概念，ＢＰ神经网络及其算法，使用地震强度因子Ｍｆ值，地震空间集中度Ｃ值，地震危险度Ｄ值对华北地区１９７２￣１９９２年期间进行空间扫描的中期和短期异常资料，通过ＢＰ神经网络进行学习并进行地震短期预测。研究结果表明：利用这３类资料的多项因子进行短期预测的效果较为理想。文章还对使用ＢＰ神经网络的一些具体问题进行了讨论。 相似文献
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本文把神经网络方法引进地震预报研究当中。使用地震频次，最大震级，平均震级，等价地震次数等多项地震活动性指标作为神经网络的输入，未来时段内的最大地震震级作为其输出，可以对某一固定地区的最大地震震级作出中近期预报。选用的神经网络模型为含两个中间层的前向模型，并采用ＢＰ算法。所得结果表明，用神经网络方法可以在一定精度范围内使震级预报的内检符合率达到１００％，在本文的例子中，外推预报准确率达到６０％以上。 相似文献
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地震强度因子Mf值在华南和东北地区中强以上地震前的空间异常变化 总被引：3，自引：1，他引：2
将地震强度因子Ｍｆ值用于华南和东北地区地震的中期预报,以检验Ｍｆ值在上述地区对Ｍｓ≥５．０级以上地震的预报效果。结果表明,震前２￣３ａ开始震中周围区域一般都出现明显的Ｍｆ值中期异常区,表明Ｍｆ值对华南和东北地区中强以上地震具有较好的预报效能。 相似文献
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阐述了小震活动增强图像的物理基础,资料分析处理方法和地震活动水平等级划分标准、预报判据及回顾性检验结果。还介绍了１９９６年新疆阿图什６．７级和喀喇昆仑７．１级２次强震的中期预报过程。震例分析表明;多数目标地震发生前１－２年内出现区域小震活动增强异常图像,若取异常结束后１２个月作为预报时段,则异常对频率为０．３５,有震报准率为０．７０,通过Ｒ值检验,中期预报效果较好。 相似文献
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神经网络模型在地震预报中的某些应用 总被引：2，自引：2，他引：2
本文介绍了人工神经网络模型以地震活动性指标为基础应用于地震预报的一些最新研究结果，选用多层前向神经网络模型及ＢＰ算法，其输入取不同的地震活动性指标的集合，输出为某一指定地区在未来时段内可能发生的最大地震的震级，以华北及首都圈地区为例，用多组不同类型的地震活动性指标进行学习与检验，结果表明，利用人工神经网络模型对未来时段震级预报的符合率较高，内检预报符合率可达１００％，外推预报符合率达到６０％以上。 相似文献
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IntroductionThe theory of artificial neural netWorks has been used in some fields for recent years such asearthquake damage prediction (Shi, Liu, 1991), earthquake intensity (Wang, 1993), earthquakecomprehensive prediction (Wang, Dai, 1997), and so on. The initial Studies indicate that someresults are prevail over classical statistical pattern recognition and fuZZy recognition methods.Neural network system is a high adaptive nonlinear dynamical system. It can extract causalitythrough a ple… 相似文献
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简单介绍了径向基函数神经网络方法的原理和应用,发展了用径向基函数（RBF）对平滑月平均黑子数进行预报的方法. 用不同的数据序列对网络进行训练,对未来8个月的平滑月平均黑子数进行预报. 用该方法对第23周开始后的平滑月平均黑子数进行逐月预报,并与实测值进行比较,结果表明随着预报实效的延长预报误差被逐渐放大,该方法可以较准确地做出未来４个月的预报,绝对误差可以控制在20以内,标准差为4.8,相对误差控制在38％以内,大部分相对误差不超过15%（占总预报数的89％）,具有较好的应用价值. 用于网络训练的样本数量对预报结果会产生一定的影响. 相似文献
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I. N. Myagkova S. A. Dolenko A. O. Efitorov V. R. Shirokii N. S. Sentemova 《Geomagnetism and Aeronomy》2017,57(1):8-15
The paper investigates the possibilities of the prediction of the time series of the flux of relativistic electrons in the Earth’s outer radiation belt by parameters of the solar wind and the interplanetary magnetic field measured at the libration point and by the values of the geomagnetic indices. Different adaptive methods are used (namely, artificial neural networks, group method of data handling, and projection to latent structures). The comparison of quality indicators of predictions with a horizon of 1–12 h between each other and with the trivial model prediction has shown that the best result is obtained for the average value of the responses of three neural networks that have been trained with different sets of initial weights. The prediction result of the group method of data handling is close to the result of neural networks, and the projection to latent structures is much worse. It is shown that an increase in the prediction horizon from 1 to 12 h reduces its quality but not dramatically, which makes it possible to use these methods for medium-term prediction. 相似文献
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Artificial neural networks as routine for error correction with an application in Singapore regional model 总被引：1，自引：1，他引：0
This research presents an error correction scheme based on artificial neural networks, and demonstrates its application on
water level forecast for the Singapore water. The error correction scheme combines the numerical model outputs with the in
situ measurements on a two-step basis: (1) predicting the model errors at the measurement stations and (2) distributing the
predicted errors to the nonmeasurement stations. Artificial neural networks are used in both error prediction and error distribution
as the mapping function approximators. The efficiency of this scheme is tested on six water level stations in the Singapore
regional model domain with four prediction horizons. The results show that this error correction scheme produces high-precision
forecasts, and improves the forecast accuracy at both measurement and nonmeasurement stations. 相似文献
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AbstractArtificial neural networks (ANNs) have recently been used to predict the hydraulic head in well locations. In the present work, the particle swarm optimization (PSO) algorithm was used to train a feed-forward multi-layer ANN for the simulation of hydraulic head change at an observation well in the region of Agia, Chania, Greece. Three variants of the PSO algorithm were considered, the classic one with inertia weight improvement, PSO with time varying acceleration coefficients (PSO-TVAC) and global best PSO (GLBest-PSO). The best performance was achieved by GLBest-PSO when implemented using field data from the region of interest, providing improved training results compared to the back-propagation training algorithm. The trained ANN was subsequently used for mid-term prediction of the hydraulic head, as well as for the study of three climate change scenarios. Data time series were created using a stochastic weather generator, and the scenarios were examined for the period 2010–2020.
Editor Z.W. Kundzewicz; Associate editor L. SeeCitation Tapoglou, E., Trichakis, I.C., Dokou, Z., Nikolos, I.K., and Karatzas, G.P., 2014. Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimization. Hydrological Sciences Journal, 59(6), 1225–1239. http://dx.doi.org/10.1080/02626667.2013.838005 相似文献
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地震强度因子Mf值在华北中强以上地震前的短期异常变化 总被引：6，自引：0，他引：6
使用华北地区１９７２￣１９９２年的中小地地震震级资料进行地震强度因子Ｍｆ值空间扫描，研究华北地区中经以上地震前的短期异常变化。结果表明，在震前０．５ａ左右的短期阶段，Ｍｆ值中期异常区存在二类不同形态的变化。其中大部分震例的Ｍｆ值中期异常区出现明显的缩小乃至消失，可将这一特征作为短期异常的标志；而另一部分震例的中期异常区无明显变化，文中讨论了Ｍｆ值用于地震预报的方法和预报效果，并对Ｍｆ值的短期变化过 相似文献
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An approach to monitoring of electromagnetic earthquake precursors including variations of the apparent resistivity, electrotelluric fields, electromagnetic emission, and ionospheric disturbances perspective for short-term and mid-term earthquake prediction is considered. Parameters of hardware-software systems for audio- and radiomagnetotelluric soundings used in the monitoring of these precursors in a wide range of frequencies from 0.1 Hz to 1 MHz are described. The technique for the stress-strain sensitive area selection for the creation of monitoring networks based on the analysis of geological and geophysical characteristics of seismo active regions, study of geoelectric structures of preselected sites, and monitoring test sessions with the assessment of the response on tidal media deformations is presented. Recommendations on the recording technique of earthquake electromagnetic precursors are provided. 相似文献
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Wang Wei Song Xianyue and Liu Zheng and Wang ZhengzhengSeismological Bureau of Shanghai Municipality Shanghai China Seismological Bureau of Jiangsu Province Nanjing China 《中国地震研究》1999,(4)
The seismicity factor A-value is defined by synthesizing the seismicity precursors in time, space and magnitude in this paper. The seismicity data of moderate or small earthquakes during 1972 ~ 1996 in North China are used to perform spacial scanning of seismicity factor A-value. The result shows that there are obviously anomaly zones of A-value with better prediction effect in the mid-term of 2~3 years before most moderately strong earthquakes. Some problems regarding the mid-term prediction using A-value have been discussed. 相似文献