共查询到18条相似文献，搜索用时 187 毫秒
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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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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。本文重要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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中国大陆构造环境监测网络(简称陆态网络)是以全球卫星导航定位系统(GNSS)为主,辅以多种空间观测技术,实时动态监测大陆构造环境变化,探求其对资源、环境和灾害的影响的地球科学综合观测网络.基于陆态网络约200个基准站的GPS观测数据,本文探讨了其在电离层空间天气监测与研究方面的应用.包括磁暴期间电离层暴扰动形态,大尺度电离层行进式扰动,太阳耀斑引起的电离层骚扰和低纬电离层不规则体结构等.研究结果表明:陆态网络布局合理,观测数据质量良好,完全可用于中国及周边地区电离层空间天气监测与研究,为进一步开展我国电离层空间天气预警和预报奠定了观测基础. 相似文献
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电离层是一个非常复杂的非线性系统.本文利用BP网络非线性输入-输出映射的特点预报电离层F2层临界频率.由于foF2在太阳活动高低年、不同经纬度、不同季节有着不同变化特点，本文仅以海口站、长春站的数据为例分别对1994年和2001年的foF2进行预报，预报结果表明发生电离层暴时用神经网络法预报foF2比国际参考电离层更有优势.此外，由于不同用户对预报精度的需求不同，本文以海口站2000、2001年的预报结果为例构建了残差模型. 相似文献
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当利用无线电电磁波进行远程通信、卫星导航时，传递信号要受到电离层的影响，因此，对电离层中电子含量的研究显得特别重要.虽然国际上有几种电离层的电子含量预报模型，但其预报只能精确到电子含量的50%～60%.本文提出了一种新的电离层电子含量预报方法:即用球谐函数对IGS(国际GPS服务)所给出的离地面450 km高的球面上的每一网点的电离层电子含量进行拟合，对不同的时间所得到的拟合系数所形成的时间序列用时间序列分析理论中的ARMA(p,q)模型进行预报，从而实现全球的电离层电子含量预报.利用本方法对2004年和2005年IGS所给电离层电子含量资料在地理框架中做了分析预报，5天内电子含量预报相对精度在90%左右. 相似文献
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本文介绍了我国曲靖非相干散射雷达的主要技术方案，结合实测数据分析表明该雷达具备了电离层电子密度与等离子体温度观测、空间碎片凝视探测与月球二维成像探测等能力，可用于研究电离层F层气候学特征、电子密度暴时变化与异常增强等天气事件、E-F谷区结构与变化、约3 cm以上尺寸空间碎片的分布特征与模型、月球不同区域的散射回波特性等.下一步将重点开展低电离层与北驼峰结构及演化过程、电离层暴时与扰动特性观测. 相似文献
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对国内外电离层参数短期预报方法进行了综述,重点介绍了几种作者最新研究的电离层foF2参数短期预报方法.包括基于混沌时间序列分析的电离层foF2参数提前15 min(分钟)准实时预测方法、基于人工神经网络技术的提前1 h(小时)现报方法、提前1～3 d(天)的神经网络预测方法、相似日短期预报方法以及综合预报模型方法.利用中国垂测站多年的观测数据对各种算法的预测精度进行了评估,并与国内外相关算法进行了定性或定量比较,各种预报方法都在前人的预报精度基础之上有了一定的提高.其中提前15 min(分钟)预测方法平均相对误差小于4%,平均绝对误差小于0.2 MHz,可以用于实时性和精度要求较高的短波系统;提前1小时预报方法在太阳活动高年平均预测相对误差小于6%,均方根误差小于0.6 MHz,太阳活动低年平均预测相对误差小于10%,均方根误差小于0.5 MHz,平均相对误差比前人研究的自相关方法提高3个百分点左右;对于提前1～3 d(天)短期预报,综合预报模型方法充分利用了神经网络方法、自相关方法以及相似日方法的优点,获得了高于任何一种单一方法的精度,对于中国9个垂测站(海口、广州、重庆、拉萨、兰州、北京、乌鲁木齐、长春、满洲里)在不同太阳活动性条件下的历史数据进行了精度测试,提前1天和提前3天预测的平均相对误差分别小于10%和小于15%,达到了国内先进水平.此外,该方法还可以综合更多预报方法,具有进一步提高预报精度的潜力.文中提出的针对不同尺度进行电离层参数预测的方法具有一定的理论基础,且精度高、易实现,对从事电离层短期预报算法研究及相关专业的学者具有一定的参考价值. 相似文献
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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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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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A Neural Network model has been developed for estimating the total electron content (TEC) of the ionosphere. TEC is proportional
to the delay suffered by electromagnetic signals crossing the ionosphere and is among the errors that impact GNSS (Global
Navigation Satellite Systems) observations. Ionospheric delay is particularly a problem for single frequency receivers, which
cannot eliminate the (first-order) ionospheric delay by combining observations at two frequencies. Single frequency users
rely on applying corrections based on prediction models or on regional models formed based on actual data collected by a network
of receivers. A regional model based on a neural network has been designed and tested using data sets collected by the Brazilian
GPS Network (RMBC) covering periods of low and high solar activity. Analysis of the results indicates that the model is capable
of recovering, on average, 85% of TEC values. 相似文献
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Modelling groundwater levels in an urban coastal aquifer using artificial neural networks 总被引：1，自引：0，他引：1
The prediction of groundwater levels in a basin is of immense importance for the management of groundwater resources, especially in coastal regions where the water table fluctuations are to be limited to avoid seawater intrusion. In this paper, an Artificial Neural Network (ANN) methodology is presented to predict groundwater levels in individual wells with one month lead. Groundwater levels were also predicted in neighboring wells using model parameters from the best network of a well. This methodology is applied to an urban coastal aquifer in Andhra Pradesh state, India. The results suggest that the feed forward neural network with Levenberg Marquardt (LM) algorithm is a good choice for predicting groundwater levels in individual wells. Bayesian Regularization (BR) model parameters of Balaji Nagar well are also used successfully to predict groundwater levels in the study area. It was observed that the ANN‐based algorithms were a better choice for the prediction of groundwater levels with limited hydrological parameters. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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The prediction of magnitude (M) of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine (SVM) and Gaussian Process Regression (GPR) for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are considered as inputs to the SVM and GPR. We give an equation for determination of reservoir induced earthquake M. The developed SVM and GPR have been compared with the Artificial Neural Network (ANN) method. The results show that the developed SVM and GPR are efficient tools for prediction of reservoir induced earthquake M. 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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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