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遥感图像处理常见的困难有数据量巨大、噪声信息多，高度非线性及其导致的难以用解析或表述处理模型等。人工神经网络（artificial neural network,ANN)是由大量简单神经元广泛相互联接而成的非线性映射或自适应动力系统，可以解决上述问题，使用ANN进行遥感图像处理在遥感图像复原，变换和分类中有如下应用：（1）使用ANN和必要辅助数据从TM图像中提取地下火热辐射数据；（2）构造ANN非线性映射，利用TM1－5，7图像提高TM6图像空间分辨率；（3）模糊神经网络（FNN）遥感图像分类。 相似文献
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人工神经网络在地球科学中的应用综述 总被引：7，自引：0，他引：7
人工神经网络（ANN）是一种动态信息处理系统，它具有联想记忆、自组织、自适应、自学习和容错性等特性。ANN可对地质环境和各类地质对象进行判别分类；可根据地震及测井中的各种波谱曲线进行地质构造模式识别，并实现自动编辑和解释；可对各种遥感信息进行图像处理和地质构造解译；在勘查地球物理方面，可解决某些反演问题；在矿产勘查中可用于矿产资源定量预测与评价勘查过程中的组合优化及多目标决策等问题。 相似文献
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基于MATLAB-NNT的高速公路软基处理方案决策模型 总被引：3，自引：2，他引：3
首先介绍了中国高速公路软基处理技术决策问题的研究现状,然后介绍了人工神经网络(ANN)BP模型和MATLAB神经网络工具箱(NNT)。最后以沪宁高速公路软基地质条件为实例,以MATLAB-NNT为工具,建立了高速公路软基处理方案的ANN决策模型,对所建立的ANN模型进行训练、回判和预测,得到了满意的结果,证明模型是有效的。 相似文献
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FORECAST OF PREFERRED FAULT BASED ON NEURAL NETWORK 总被引：10，自引：2，他引：8
基于优势面区域稳定性评价理论和人工神经网络 (ANN)的原理和方法 ,探讨了基于 ANN的优势断裂预报神经网络算法及模型 ,并结合实例检验表明应用反传 (BP)神经网络模型判定优势断裂的新方法是有效的 ,且取得了理想的结果。 相似文献
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商丘试区地下水动态的ANN模型研究 总被引：3，自引：1，他引：3
利用人工神经网络 (ANN)技术 ,建立了商丘试验区地下水动态的 ANN模型 ,对试区的地下水动态进行了模拟 ,并在引黄条件下 ,对未来 5年商丘市地下水位作出了预报 相似文献
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首先介绍了中国高速公路软基处理技术决策问题的研究现状，然后介绍了人工神经网络（ANN）BP模型和MATLAB神经网络工具箱（NNT）。最后以沪宁高速公路软基地质条件为实例，以MATLAB－NNT为工具，建立了高速公路软基处理方案的ANN决策模型，对所建立的ANN模型进行训练、回判和预测，得到了满意的结果，证明模型是有效的。 相似文献
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神经网络技术在地下水资源管理中的应用现状与发展趋势 总被引：2，自引：0，他引：2
人工神经网络（ANN）能够识别输入输出数据间复杂的非线性关系,因而在解决其过程难以用物理方程描述的问题上得到了广泛的应用与研究。综述国外在90年代以来ANN技术在地下水资源管理中的研究与应用概况,简介我国在这一领域的研究和应用,指出这一技术的不足并展望其发展。 相似文献
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本文首先论述了GIS与ANN技术在矿产资源评价中应用的意义和特点及其相互间的联系;然后提出了一种基于GIS的SPV型人工神经网络矿产资源评价系统,介绍了如何在GIS平台上应用这种ANN对金矿资源进行预测评价的方法以及与实际对比的结果. 相似文献
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基于GA-ANN的苏锡常地裂缝危险性评价 总被引：4，自引：1，他引：3
文章以苏锡常地区地裂缝危险性评价为例,利用遗传算法(GA)对人工神经网络(ANN)进行改进,先用GA优化BP网络初始权重,再用BP算法修改网络权重,实现不同尺度的同步调整。选择30点的不同地质条件组成样本对所建模型进行训练,评价指标包括:基岩埋深、基岩起伏度、地下水位、地面沉降梯度、含水层导水系数和粘性土层厚度。经过500次GA迭代,得到苏锡常地区地裂缝ANN模型的最佳权重组合,该耦合模型能对全区地裂缝地质条件进行正确分类,精度接近1‰。 相似文献
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人工神经网络在泥石流风险评价中的应用 总被引：14，自引：0，他引：14
泥石流风险评价是对泥石流灾害的预评估,在泥石流防灾减灾实践中具有重要的意义,可直接服务于国民经济建设。人工神经网络具有良好的非线性信息处理能力,特别适宜于解决风险评价中多指标复杂性和不确定性的问题。实例证明,经过训练的网络模型对于泥石流风险评价具有较好的适用性,可以作为泥石流风险评价技术的补充。 相似文献
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Confined groundwater pollution mechanism and vulnerability assessment in oilfields, North China 总被引：3，自引：3，他引：0
Zhiqiang Wang Qiang Wu Yueting Zhang Jingmeng Cheng 《Environmental Earth Sciences》2011,64(6):1547-1553
The confined groundwater in North China oilfield is contaminated by oil and oilfield gas. Based upon the analysis of the geological structures and hydrogeology of the study area, the pollution mechanism of the confined groundwater is studied. The main influencing factors are the oil–gas deposit size, the distribution of faults, oil exploitation well distribution, water input well distribution, aquifer media permeability, etc. The vulnerability assessment model is established for the confined groundwater in the study area, and the artificial neural networks (ANN) assessment method is adopted in this paper. The assessment results of the confined groundwater vulnerability fits well with the actual situations in the study area, which indicates the ANN assessment method is correct, accurate and objective. 相似文献
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Sainath Aher Sambhaji Shinde Shantamoy Guha Mrinmoy Majumder 《Journal of Earth System Science》2017,126(2):21
An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification. 相似文献
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矿井煤层底板突水预测新方法研究 总被引：8，自引：1，他引：7
本文针对煤矿矿井煤层底板突水系统为一非线性系统的特性,提出采用对非线性问题具有良好适用性的人工神经网络系统（以下简称神经网络）,进行煤层底板突水预测。以作者们研制,使用神经网络的实践为基础,阐述系统、建模方法、适用条件和应用问题,并在焦作矿务局演马庄矿、焦作金科尔集团方庄煤矿对所建立的煤层底板突水预测神经网络进行生产性检验,取得良好的结果,说明该系统应用于煤层底板突水预测的可靠性。 相似文献
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Pile foundations are usually used when the conditions of the upper soil layers are weak and unable to support the super-structural loads. Piles carry these super-structural loads deep into the ground. Therefore, the safety and stability of pile-supported structures depends largely on the behavior of the piles. In addition, accurate prediction of pile behavior is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile behavior based on the results of cone penetration test (CPT) data. Approximately 500 data sets, obtained from the published literature, are used to develop the ANN model. The paper compares the predictions obtained by the ANN with those given by a number of traditional methods and it is observed that the ANN model significantly outperforms the traditional methods. An important advantage of the ANN model is that the complete load-settlement relationship is captured. Finally, the paper proposes a series of charts for predicting pile behavior that will be useful for pile design. 相似文献
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Prediction of pile settlement using artificial neural networks based on standard penetration test data 总被引：4，自引：0，他引：4
F. Pooya Nejad Mark B. Jaksa M. Kakhi Bryan A. McCabe 《Computers and Geotechnics》2009,36(7):1125-1133
In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions. 相似文献
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对用人工神经网络方法来解决钻探生产的实际问题, 在不取心的情况下识别所钻地层的岩性进行了研究.根据钻探生产的特点, 设计了人工神经网络的结构和输出方式, 开发了人工神经网络识别所钻地层的软件, 分析了影响人工神经网络应用效果的各因素, 在人工神经网络的优化设计方面作了较深入的研究.研究表明: 人工神经网络用于识别所钻地层有很好的效果; 人工神经网络的参数, 如学习率、隐含层层数、隐含层单元数和数据处理方式等对人工神经网络的应用效果有影响. 相似文献
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The stability problem of natural slopes, filled slopes, and cut slopes are commonly encountered in Civil Engineering Projects. Predicting the slope stability is an everyday task for geotechnical engineers. In this paper, a study has been done to predict the factor of safety (FOS) of the slopes using multiple linear regression (MLR) and artificial neural network (ANN). A total of 200 cases with different geometric and shear strength parameters were analyzed by using the well-known slope stability methods like Fellenius method, Bishop’s method, Janbu method, and Morgenstern and Price method. The FOS values obtained by these slope stability methods were used to develop the prediction models using MLR and ANN. Further, a few case studies have been done along the Jorabat-Shillong Expressway (NH-40) in India, using the finite element method (FEM). The output values of FEM were compared with the developed prediction models to find the best prediction model and the results were discussed. 相似文献
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三种基于神经网络的洪水实时预报方案的比较研究 总被引：8，自引：1，他引：7
在总结神经网络应用的基础上,归纳了3种基于神经网络的洪水实时预报方案。第一种是神经网络水文模型的模拟模式加模拟误差的自回归校正模型,第二种是权重系数固定的神经网络实时预报方案,第三种是权重系数自动更新的神经网络实时预报方案。采用10个不同流域的日流量资料对这3种方案进行率定和校核。比较这3种方案的实时预报精度。结果发现,第三种方案不仅预报精度要高于其他两种方案,而且比第一种方案少了一个自回归校正模型,结构简洁。本文建议采用第三种洪水实时预报方案。 相似文献