共查询到19条相似文献，搜索用时 671 毫秒
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以陕西地区的地震为例，探讨了人工神经网络方法在地震预报中的应用。预报因子采用Ｋｅｉｌｉｓ－Ｂｏｒｏｋ提出的地震流函数。结果表明，人工神经网络方法能够较好地学习复杂的预报因子和预报对象的关系，模拟地震预报问题，预报效果也较好，有广阔的应用前景。 相似文献
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回顾了１９９年大同－－阳高地震现场进行分析预报工作的情况,叙述了预报强余震制定地震类型、进行后期安全预报的作法,指出要搞好现场分析预报工作,需要思想上立足有震、应迅速编制现场地震目录、现场分析预报应以测震学方法为主、要保持不同时段分析预报人员的合理结构、努力做好现场工作记录等。 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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地震现场预报数据处理系统是供分析预报人员在地震现场使用的一套实用性计算机软件。该系统根据国家地震局颁布的《地震现场工作大纲和震情分析指南》及目前在地震现场预报中常用的各类方法编制了相应的程序,并结合实际工作需要研制了该数据处理系统。它既是一个独立的完整的系统,又可以作为一个子系统与专家系统、数据库系统紧密地结合成一个功能更强的地震现场预报专家咨询系统。该系统主要有三大方面的功能模块：（1）计算机自动处理;（2）用户选择运行;（3）震前活动参数的计算。 相似文献
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人工神经网络是用来模拟人脑智能特点和结构的一种模型,具有很强的非线性映射功能．把它引用到地震前兆观测数据的分析处理中,可为前兆观测更好地服务于地震分析预报开辟出一条新路,也是对人工神经网络方法应用的推广．本文分析了时间序列的可预测性,给出了用人工神经网络预测地震前兆混沌时间序列的方法,并以江宁台和徐州台SQ 型地倾斜仪观测及溧阳台体应变观测的时间序列为例,对其作了预测和处理．结果表明:用该方法处理达到的精度能满足实际工作的需要,因而该方法在今后的实际地震分析预报工作中具有重要应用价值． 相似文献
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基于知识的地震现场预报系统（KPSES） 总被引：3，自引：2，他引：3
基于知识的地震现场预报系统（ＫＰＳＥＳ）王炜，庄昆元，黄冰树，夏仕华，黎捷，刘文龙一、系统总体设计的基本要求根据我国多年来地震现场预报的经验和特点，ＫＰＳＥＳ的设计应能满足下列基本要求：ｌ．系统的知识表示与推理过程应能体现地震现场预报的基本思路。（ｌ... 相似文献
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回顾了我国 30多年来的地震现场震情监视预报工作。该工作从目标角度考虑大致可分为二个不同的阶段, 1980年前主要是地震预报方法的探索, 而 1980年以后,更主要的是承担地震预报的社会服务。对我国赴国外地震现场的工作情况作了概述, 并提出了地震现场震情监视预报工作的可能前景。 相似文献
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利用人工神经网络预测电离层F2层骚扰 总被引：3，自引：1，他引：3
利用人工神经网络技术，提出预报离散随机的电离层骚扰事件的新方案.本文重点讨论了预报电离层骚扰的人工神经网络的构造，采用模糊理论和模式识别的思想构造了网络的输入层和输出层.将与电离层骚扰相关的日面现象如太阳耀斑、黑子等的日面位置、强度等参量作为网络的输入，该方案预报结果检验中，使传统方法难以预报的小型和中型电离层(骚扰达到80%以上)的预报准确率有所提高.最后还提出了利用人工神经网络识别单一型别骚扰事件的方案，预报准确率在95%以上。 相似文献
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利用人工神经网络预测电离层F2层骚扰 总被引：2，自引：1，他引：2
利用人工神经网络技术，提出预报离散随机的电离层骚扰事件的新方案.本文重点讨论了预报电离层骚扰的人工神经网络的构造，采用模糊理论和模式识别的思想构造了网络的输入层和输出层.将与电离层骚扰相关的日面现象如太阳耀斑、黑子等的日面位置、强度等参量作为网络的输入，该方案预报结果检验中，使传统方法难以预报的小型和中型电离层(骚扰达到80%以上)的预报准确率有所提高.最后还提出了利用人工神经网络识别单一型别骚扰事件的方案，预报准确率在95%以上。 相似文献
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Artificial Neural Network (ANN) models were used to forecast precipitation. Three-layer back propagation ANNs were trained with actual monthly precipitation data from six Czech and four Hungarian meteorological stations for the period 1961-1998. The predicted amounts are the next month's precipitation. Both training and testing ANN results provided a good fit with the actual data and displayed high feasibility in predicting extreme precipitation. 相似文献
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将专家知识和神经元网络方法结合起来，利用专家知识和规则构造规则样本的方法来提高神经元网络学习样本的质量，通过神经元网络对规则样本的学习，形成基于专家知识的神经元网络模型。在潜在震源区划分专家系统中有关规则的基础上，将基于专家知识的神经元网络方法引入到潜在震源区定量划分中，并以首都圈地区为例，对其潜在震源区作了定量划分，结果表明，规则样本能较好的反映专家的知识和规则，利用构造规则样本的方法，可以提高 相似文献
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Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India
Surajit Chattopadhyay 《Acta Geophysica》2007,55(3):369-382
In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial
Neural Network model. In formulating the ANN — based predictive model, three-layer network has been constructed with sigmoid
non-linearity. The monthly summer monsoon rainfall totals, tropical rainfall indices and sea surface temperature anomalies
have been considered as predictors while generating the input matrix for the ANN. The data pertaining to the years 1950–1995
have been explored to develop the predictive model. Finally, the prediction performance of neural net has been compared with
persistence forecast and Multiple Linear Regression forecast and the supremacy of the ANN has been established over the other
processes. 相似文献
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Nibedita Bisoyi Harish Gupta Narayan Prasad Padhy Govind Joseph Chakrapani 《国际泥沙研究》2019,34(2):125-135
Most of the studies on Artificial Neural Network (ANN) models remain restricted to smaller rivers and catchments. In this paper, an attempt has been made to correlate variability of sediment loads with rainfall and runoff through the application of the Back Propagation Neural Network (BPNN) algorithm for a large tropical river. The algorithm and simulation are done through MATLAB environment. The methodology comprised of a collection of data on rainfall, water discharge, and sediment discharge for the Narmada River at various locations (along with time variables) and application to develop a threelayer BPNN model for the prediction of sediment discharges. For training and validation purposes a set of 549 data points for the monsoon (16 June-15 November) period of three consecutive years (1996–1998) was used. For testing purposes, the BPNN model was further trained using a set of 732 data points of monsoon season of four years (2006–07 to 2009–10) at nine stations. The model was tested by predicting daily sediment load for the monsoon season of the year 2010–11. To evaluate the performance of the BPNN model, errors were calculated by comparing the actual and predicted loads. The validation and testing results obtained at all these locations are tabulated and discussed. Results obtained from the model application are robust and encouraging not only for the sub-basins but also for the entire basin. These results suggest that the proposed model is capable of predicting the daily sediment load even at downstream locations, which show nonlinearity in the transportation process. Overall, the proposed model with further training might be useful in the prediction of sediment discharges for large river basins. 相似文献
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Evaluation of total load sediment transport formulas using ANN 总被引：2，自引：0，他引：2
The calculated results from various sediment transport formulas often differ from each other and from measured data. Some parameters in the sediment transport formulas are more effective than others to estimate total sediment load. In this study, an Artificial Neural Network （ANN） model is trained using four dominant parameters of sediment transport formulas. ANN models are able to reveal hidden laws of natural phenomena such as sediment transport process. The results of ANN and some total bed material load sediment transport formulas have been compared to indicate the importance of variables which can be used in developing sediment transport formulas. To train ANN, average flow velocity, water surface slopes, average flow depth, and median particle diameter are used as dominant parameters to estimate total bed material load. Two hundreds and fifty samples are used to train the ANN model. Twenty-four sets of field data not used in the training nor calibration of ANN are used to compare or verify the accuracy of ANN and some well-known total bed material load formulas. The test results show that the ANN model developed in this study using minimum number of dominant factors is a reliable and uncomplicated method to predict total sediment transport rate or total bed material load transport rate. Results show that the accuracy of formulas in descending order are those by Yang （1973）, Laursen （1958）, Engelund and Hansen （1972）, Ackers and White （1973）, and Toffaleti （1969）. These results are similar to those made by ASCE （1982） based on laboratory and field data not used in this paper. Study results also show that the formulas based on physical laws of sediment transport, like those formulas that were developed based on power concept, are more accurate than other formulas for estimating total bed material sediment load in rivers. 相似文献
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介绍了一种基于辐射传递模拟和人工神经网络技术(ANN)的二类水体水色要素(CHL,SPM,CDOM)的反演算法.在辐射传递模拟计算中,纯海水吸收和散射、浮游植物吸收的数据或模型是已发表的被广泛采用的结果.黄色物质和非浮游植物颗粒吸收以及海洋颗粒物的散射模型从COASTLOOC数据中导出.另外,还利用了一个新的海洋颗粒物后向散射概率模型,在该模型中颗粒物后向散射概率是颗粒有机物与SPM比值和波长的甬数.把上述定义的固有光学性质作为输入,经过辐射传递模拟得到海表面以下辐照度反射比数据集,然后将该模拟数据集用于训练不同的人工神经网络,获取水色和水色要素浓度之间甬数关系的最佳近似.利用以上建立的基于人工神经网络的算法,把COASTLOOC数据集和PMNS数据集的辐照度反射比作为输入进行水色要素反演,通过比较反演值和真实测量值来评价算法性能.结果显示,建立的基于ANN的二类水体水色要素反演算法具有很好的性能. 相似文献