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1.
为提高多种典型人工神经网络应用于降水预报的精度与稳定性并做出优选,对太湖流域湖西区丹徒、丹阳、金坛、溧阳、宜兴5站的年降水量时间序列建立基于组成成分分析的人工神经网络模型,并通过平均相对误差、平均绝对误差、均方根误差及合格率4项评价指标对比分析预报效果。该模型采用Mann-Kendall法、秩和检验法、谱分析法进行组成成分分析;建立BP网络、小波神经网络、RBF网络、GRNN网络及Elman网络模拟并预测随机成分,与确定性成分叠加得年降水量预报结果。在湖西区的研究结果表明,基于组成成分分析的人工神经网络模型的拟合及预测精度高于原始人工神经网络和线性自回归模型,GRNN网络的预测精度与稳定性高于其他4类神经网络。 相似文献
2.
Modeling soil collapse by artificial neural networks 总被引：1，自引：0，他引：1
The feasibility of using neural networks to model the complex relationship between soil parameters, loading conditions, and
the collapse potential is investigated in this paper. A back propagation neural network process was used in this study. The
neural network was trained using experimental data. The experimental program involved the assessment of the collapse potential
using the one-dimensional oedometer apparatus. To cover the broadest possible scope of data, a total of eight types of soils
were selected covering a wide range of gradation. Various conditions of water content, unit weights and applied pressures
were imposed on the soils. For each placement condition, three samples were prepared and tested with the measured collapse
potential values averaged to obtain a representative data point. This resulted in 414 collapse tests with 138 average test
values, which were divided into two groups. Group I, consisting of 82 data points, was used to train the neural networks for
a specific paradigm. Training was carried out until the mean sum squared error (MSSE) was minimized. The model consisting
of eight hidden nodes and six variables was the most successful. These variables were: soil coefficient of uniformity, initial
water content, compaction unit weight, applied pressure at wetting, percent sand and percent clay. Once the neural networks
have been deemed fully trained its accuracy in predicting collapse potential was tested using group II of the experimental
data. The model was further validated using information available in the literature. The data used in both the testing and
validation phases were not included in the training phase. The results proved that neural networks are very efficient in assessing
the complex behavior of collapsible soils using minimal processing of data.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
3.
遥感水深反演是水深测量的一种重要技术和手段.以美济礁水深反演为例,选择WorldView-02高分影像为数据源,在辐射定标和大气校正的基础上,构建BP（Back Propagation）和RBF（Radial Basis Function）人工神经网络水深反演模型,以遥感影像8个波段为输入层,通过tansig、logsig、高斯函数和purelin函数变换实现从输入层到隐含层、隐含层到输出层的转换,以便反演水深.最后对反演水深与实测水深采用回归分析,求解决定系数（coefficient of determination,R2）、平均决定误差（Mean Absolute Error,MAE）、均方根误差（Root Mean Square Error,RMSE）等进行比较,评价2种模型的精度.结果表明,RBF神经网络模型结构更简单,对样本要求更低,反演精度达到0.995,更适合遥感水深反演. 相似文献
4.
人工神经网络在基桩低应变完整性检测中的应用 总被引：2，自引：0，他引：2
目前基桩低应变完整性检测数据的后期处理有很多方法 ,但分析中人为干预较多。利用人工神经网络强大的非线性映射能力和学习训练功能 ,提出了基于BP网络的基桩完整性检测模型。该模型基于现场实测资料 ,避免了数据处理过程中各种人为干预。应用该模型对工程实例进行了分析 ,训练和测试网络结果说明该方法能够快速、方便地对基桩质量进行模式识别 相似文献
5.
Artificial neural networks have a wide application in many areas of science and engineering and, particularly, in geotechnical
problems with some degree of success due to the fact that the mechanical behavior of rocks are not salient. They are highly
nonlinear, quite complex and complicated. While applying neural network in such complicated problems, epoch determination
is based on hit-and-trail basis mainly. In this paper, the effect of different number of epochs is shown on the network and
a method is proposed to determine the optimum number of epoch with the help of self-organized map (SOM) to avoid overtraining
of the network. Data distribution is also done with the help of SOM and a statistical analysis is made to show consistency
between training and testing dataset for ensuring the optimal model performance. 相似文献
6.
A direct inversion scheme for deep resistivity sounding data using artificial neural networks 总被引：1，自引：0，他引：1
Initialization of model parameters is crucial in the conventional 1D inversion of DC electrical data, since a poor guess may
result in undesired parameter estimations. In the present work, we investigate the performance of neural networks in the direct
inversion of DC sounding data, without the need ofa priori information. We introduce a two-step network approach where the first network identifies the curve type, followed by the
model parameter estimation using the second network. This approach provides the flexibility to accommodate all the characteristic
sounding curve types with a wide range of resistivity and thickness. Here we realize a three layer feed-forward neural network
with fast back propagation learning algorithms performing well. The basic data sets for training and testing were simulated
on the basis of available deep resistivity sounding (DRS) data from the crystalline terrains of south India. The optimum network
parameters and performance were decided as a function of the testing error convergence with respect to the network training
error. On adequate training, the final weights simulate faithfully to recover resistivity and thickness on new data. The small
discrepancies noticed, however, are well within the resolvability of resistivity sounding curve interpretations. 相似文献
7.
小波神经网络在基桩动测信号处理中的应用 总被引：1，自引：0，他引：1
基于小波变换的时频局部化特性及人工神经网络的非线性映射特性，将小波变换和人工神经网络的优点结合起来，从基桩动测信号二进小波变换的频域中提取特征，最后将这些特征输入人工神经网络进行训练和分类，进而实现基桩缺陷的诊断。数值模拟试验显示了该方法的合理性，在此基础上进行了工程桩的现场试验研究，结果表明训练成功的神经网络可以作为智能分类器对基桩常见缺陷进行识别和诊断。 相似文献
8.
9.
人工神经网络模型在地学研究中的应用进展 总被引：41，自引：1，他引：40
近年来,随着人工神经网络（ANNs）自身技术的不断完善,应用ANNs模型成功解决各类地学问题的案例大量出现。通过对其发展历程进行分析发现,20世纪80年代末国际地学分析中已开始融入ANNs技术,国内则滞后 1～2年。在地学分析中使用的各类人工神经网络类型中,BP模型应用最广,占到85％以上。在10余年的应用过程中,虽然地学的各个分支学科都移植了一种或数种ANNs模型作为其分析工具,但水文、地质、大气、遥感等领域应用较为广泛。传统地学定量分析中的单变量或多变量预测成为人工神经网络地学模型的主要应用客体。同时,诸如模式识别和过程模拟等也是ANNs模型求解的对象。目前,随着建模经验和知识的积累,地学ANNs模型的发展呈现出多种技术综合集成的态势,遗传算法、小波转换、模拟退火算法以及模糊逻辑等方法与ANNs模型融合,成为解决地学分析中非线性问题的利器。 相似文献
10.
A Stanley Raj Y SRINIVAS D Hudson Oliver D Muthuraj 《Journal of Earth System Science》2014,123(2):395-411
The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model. 相似文献
11.
An intelligent neural network model for evaluating performance of immobilized cell biofilter treating hydrogen sulphide vapors 总被引：1，自引：1，他引：0
E. R. Rene Ph.D. J. H. Kim Ph.D. H. S. Park Ph.D. 《International Journal of Environmental Science and Technology》2008,5(3):287-296
Biofiltration has shown to be a promising technique for handling malodours arising from process industries. The present investigation pertains to the removal of hydrogen sulphide in a lab scale biofilter packed with biomedia, encapsulated by sodium alginate and poly vinyl alcohol. The experimental data obtained under both steady state and shock loaded conditions were modelled using the basic principles of artificial neural networks. Artificial neural networks are powerful data driven modelling tools which has the potential to approximate and interpret complex input/output relationships based on the given sets of data matrix. A predictive computerised approach has been proposed to predict the performance parameters namely, removal efficiency and elimination capacity using inlet concentration, loading rate, flow rate and pressure drop as the input parameters to the artificial neural network model. Earlier, experiments from continuous operation in the biofilter showed removal efficiencies from 50 to 100 % at inlet loading rates varying up to 13 g H2S/m3h. The internal network parameter of the artificial neural network model during simulation was selected using the 2k factorial design and the best network topology for the model was thus estimated. The results showed that a multilayer network (4-4-2) with a back propagation algorithm was able to predict biofilter performance effectively with R2 values of 0.9157 and 0.9965 for removal efficiency and elimination capacity in the test data. The proposed artificial neural network model for biofilter operation could be used as a potential alternative for knowledge based models through proper training and testing of the state variables. 相似文献
12.
Because of scarcity and high variability of rainfall in arid areas, from one hand, reliable prediction of precipitation in such regions is considerably difficult. Furthermore, in some cases, shortage of observation data and several other limitations may intensify complexity of the forecasting. On the other hand, these regions highly suffer from low availability of water which necessitates development of an appropriate modeling approach to provide as precise as possible predictions of precipitation. Artificial neural networks (ANNs) are expected to be a powerful tool in capturing and analyzing high interannual variability of precipitation in arid climates and, subsequently, in proper prediction of precipitation fluctuations in the future. The end of this paper is to improve ANN predictions of precipitation in arid climates using better training of the network. To this end, two approaches were applied. In the first one, just the rainfall monthly data were considered as input. In the second approach, in addition to precipitation, several exogenous variables of precipitation are considered as input to predict precipitation. The chosen exogenous parameters are either effective on or relevant to the precipitation patterns. Then, several lag times, hidden layer sizes, and training algorithms for different running sums are used in order to produce best forecasts. It was shown that the performance of networks increases significantly by importing more external factors as inputs. The bigger time scales also exhibited better performances. In all the five time scales, smaller lag times (especially one month), bigger hidden layer sizes (especially between 31 and 40), and GDX training algorithm presented the best performance. The highest obtained performance was presented by the network with 10 inputs, 1 month lag, 36 hidden layers, and CGF training method in 18 months running sum with R 2 of 0.93. 相似文献
13.
Pritpal Singh 《地学前缘(英文版)》2018,9(4):1243-1257
This study presents a model to forecast the Indian summer monsoon rainfall(ISMR)(June-September)based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,(1) training data set(1871-1960), and(2) testing data set(1961-2014).Statistical analyzes reflect the dynamic nature of the ISMR, which couldn't be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network(ANN). Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model. 相似文献
14.
Ahmad Neyamadpour W. A. T. Wan Abdullah Samsudin Taib 《Journal of Earth System Science》2010,119(1):27-40
The objective of this paper is to investigate the applicability of artificial neural networks in inverting quasi-3D DC resistivity
imaging data. An electrical resistivity imaging survey was carried out along seven parallel lines using a dipole-dipole array
to confirm the validation of the results of an inversion using an artificial neural network technique. The model used to produce
synthetic data to train the artificial neural network was a homogeneous medium of 100Ωm resistivity with an embedded anomalous
body of 1000Ωm resistivity. The network was trained using 21 datasets (comprising 12159 data points) and tested on another
11 synthetic datasets (comprising 6369 data points) and on real field data. Another 24 test datasets (comprising 13896 data
points) consisting of different resistivities for the background and the anomalous bodies were used in order to test the interpolation
and extrapolation of network properties. Different learning paradigms were tried in the training process of the neural network,
with the resilient propagation paradigm being the most efficient. The number of nodes, hidden layers, and efficient values
for learning rate and momentum coefficient have been studied. Although a significant correlation between results of the neural
network and the conventional robust inversion technique was found, the ANN results show more details of the subsurface structure,
and the RMS misfits for the results of the neural network are less than seen with conventional methods. The interpreted results
show that the trained network was able to invert quasi-3D electrical resistivity imaging data obtained by dipole-dipole configuration
both rapidly and accurately. 相似文献
15.
用遗传神经网络分析泥石流活动性 总被引：7，自引：0，他引：7
泥石流是我国山区的主要地质灾害之一。影响泥石流活动性的因素十分复杂，并且具有随机性和模糊性。遗传神经网络结合了神经网络和遗传算法的优点，可以模拟学习和进化之间的交互作用，很适合用于分析泥石流活动性。文章简要讨论了遗传神经网络的原理，建立了泥石流活动性分析的遗传神经网络模型，并将该模型用于川藏公路沿线30条泥石流沟的活动性分析。网络的拓扑结构为(9，6，4，3)，即输入节点(评价指标)、第l隐含层、第2隐含层和输出接点(分析结果)分别为9、6、4、3。首先以其中25条泥石流沟作为样本对网络进行训练，训练时网络的连接权采用遗传算法进行自适应演化，待模型稳定后将其余5条泥石流沟的数据输入模型，计算它们的活动性，计算结果与实际观测基本相符，证明模型是可行的，各个参数的选取也是合适的。 相似文献
16.
In this paper, we present a method for attenuating background random noise and enhancing resolution of seismic data, which takes advantage of semi-automatic training of feed forward back propagation (FFBP) artificial neural network (ANN) in a multiscale domain obtained from wavelet packet analysis (WPA). The images of approximations and details of the input seismic sections are calculated and utilized to train neural network to model coherent events by an automatic algorithm. After the modeling of coherent events, the remainder data are assumed to be related to background random noise. The proposed method is applied on both synthetic and real seismic data. The results are compared with that of the adaptive Wiener filter (AWF) in synthetic shot gather and real common midpoint gather and also with that of band-pass filtering on real common offset gather. The comparison indicates substantially higher efficiency of the proposed method in attenuating random noise and enhancing seismic signals. 相似文献
17.
作者借用线性代数方程组定解讨论中的相关概念，类比表述了前人工神经网络的定解和泛化问题，同时说明解决泛化问题仅公给定网络VC维是不够的，还需要研究本集规模、向量维度、相关性以及模拟对象的复杂度，文章出相应的算例，一定程度上讨论了前向神经网络的演进特征。 相似文献
18.
Hamid Mahmoudabadi Mohammad Izadi Mohammad Bagher Menhaj 《Computational Geosciences》2009,13(1):91-101
In the present paper, a new hybrid method is proposed for grade estimation. In this method, the multilayer perceptron (MLP)
network is trained using the combination of the Levenberg–Marquardt (LM) method and genetic algorithm (GA). Having a few samples
for grade estimation, it is difficult to get a proper result using some function approximation methods like neural networks
or geostatistical methods. The neural network training methods are very sensitive to initial weight values when there are
a few samples as a training dataset. The main objective of the proposed method is to resolve this problem. Here, our method
finds the optimal initial weights by combining GA and LM method. Having the optimal initial values for weights, the local
minima are avoided in the training phase and subsequently the neural network sustainability is trained optimally. Furthermore,
the hybrid method is applied for grade estimation of Gol-e-Gohar iron ore in south Iran. The proposed method shows significant
improvements compared to both conventional MLP and Kriging method. The efficiency of the proposed method gets more highlighted
when the training data set is small. 相似文献
19.
Phuong Thao Thi Ngo Mahdi Panahi Khabat Khosravi Omid Ghorbanzadeh Narges Kariminejad Artemi Cerda Saro Lee 《地学前缘(英文版)》2021,12(2):505-519
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies. 相似文献
20.
A. Tahershamsi M. R. Majdzade Tabatabai R. Shirkhani 《International Journal of Environmental Science and Technology》2012,9(2):333-342
Regime width of alluvial channels is a vital problem in river morphology and channel design. Many equations are available in the literature to predict regime width of alluvial rivers. In general, there are many approaches to estimate regime width; however, none of them is widely accepted at present. This is due to the fact that most hypotheses have many constrains which may lead to simplify governing conditions and also lack of knowledge of some physical processes associated with channel formation and maintenance. Intelligent models are a new approach to describe complex problems one of which is artificial neural networks. In this research, initially, gravel bed rivers database was used in bankfull discharge condition to train various dimensional and non-dimensional neural-network schemes with three and four variables as input data, respectively. Then, the same database was applied to fit regression analysis to estimate regime width; this led to drive dimensional and non-dimensional equations. Finally, dimensional and non-dimensional neural-network models and regression equations were compared together based on 50% error bands with other dataset. Results show that neural network can adequately estimate the regime width in gravel bed rivers and multilayer perceptron network with one hidden layer and eight hidden neurons based on dimensional data set was selected as optimum network to predict regime width. A sensitivity analysis also shows that bankfull discharge has a greater influence on regime width of gravel bed channels than the other independent parameters in dimensional scheme of neural network. 相似文献