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1.
T. G. Sitharam Pijush Samui P. Anbazhagan 《Geotechnical and Geological Engineering》2008,26(5):503-517
Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock
is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site
amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore,
India is arrived from the 652 boreholes data in the area covering 220 km2. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability
of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector
Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of
rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore,
where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the
Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation
algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine
based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the
reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced
level of rock has been presented and discussed. 相似文献
2.
Unlike in the open sea, the use of wind information for forecasting waves may encounter more ambiguous uncertainties in the coastal or harbor area due to the influence of complicated geometric configurations. Thus this paper attempts to forecast the waves based on learning the characteristics of observed waves, rather than the use of the wind information. This is reported in this paper by the application of the artificial neural network (ANN), in which the back-propagation algorithm is employed in the learning process for obtaining the desired results. This model evaluated the interconnection weights among multi-stations based on the previous short-term data, from which a time series of waves at a station can be generated for forecasting or data supplement based on using the neighbor stations data. Field data are used for testing the applicability of the ANN model. The results show that the ANN model performs well for both wave forecasting and data supplement when using a short-term observed wave data. 相似文献
3.
用Matlab中的Neural Network Toolbox仿真赤道东太平洋SST的预报模型 总被引:2,自引:0,他引:2
基于NCEP/NCAR再分析资料和COADS海洋资料中的全球月平均海平面气压场、850hPa纬向风场及海洋温度场,利用Matlab中的Neural Network Toolbox仿真环境和BP模型改进算法比较准确地仿真和反演出了南方涛动指数、赤道纬向风指数和滞后的赤道东太平洋海温之间的动力结构和预报模型。该模型具有很好的拟合精度和可行的预报效果。可在一定时效内预测赤道东太平洋月平均海温的变化趋势。由于所建系统是具有直接因果关系的预报模型,因此不仅可直接用于预测,而且可有效避免类拟非线性微分方程组在积分过程中由于对初值敏感性而可能产生的对预报结果的不确定性。 相似文献
4.
5.
基于退火BP神经网络的GPS高程转换 总被引:1,自引:0,他引:1
阐述模拟退火算法的基本思想和原理,提出并介绍模拟退火算法优化的BP神经网络模型在GPS高程转换中的具体应用,同时编写相应的MATLAB处理程序,结合大量数据进行仿真实验,结果表明文中提出的退火BP神经网络模型具有收敛速度快、精度高、避免陷入局部最小的优良特性。 相似文献
6.
Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India 总被引:2,自引:0,他引:2
D. Ramakrishnan T. N. Singh N. Purwar K. S. Barde Akshay. Gulati S. Gupta 《Computational Geosciences》2008,12(4):491-501
This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network
(ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising
parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction
sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted
results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality
of the ANN technique in mapping the liquefaction susceptibility of the area. 相似文献
7.
运用神经网络模型的一典型模型——“反向传播”模型的改进形式,处理矿产资源统计预测问题,得出与数量化理论Ⅱ处理极为相似的结果. 相似文献
8.
现代地裂缝在世界许多国家普遍存在 ,已成为当今世界范围内的主要地质灾害之一。本文在详尽分析了山西榆次地裂缝的各个致灾因子的基础上 ,利用GIS技术建立了地质学意义上的专题层 ;然后采用人工神经网络技术构建出了地裂缝灾害活动性的评价模型 ,并建立了地裂缝活动性的评价系统 ,对榆次地裂缝进行了灾害活动性评价 ,为榆次市城建和国土规划等部门的正确决策提供了重要的科学依据 相似文献
9.
One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively. 相似文献
10.
Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach. 相似文献