煤矿立井井筒非采动破裂的人工神经网络预测

应用人工神经网络的基本原理,建立了一个基于神经网络的煤矿立井井筒非采动破裂的预测系统,实现了立井井筒破裂预测的智能化。最后将神经网络预测结果与数值计算结果对比,认为应用人工神经网络对立井井筒破裂时间的预测比较准确、实用。     

Uncertainty analysis in bed load transport prediction of gravel bed rivers by ANN and ANFIS

River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved.     

基于模糊人工神经网络识别的水质评价模型

人工神经网络和模糊识别理论作为模拟生物体的信息处理系统,在实践中得到广泛的应用,且各有所长,将二者相结合,构造出模糊人工神经识别网络,从而使识别系统的柔性处理能力得到很大提高.阐述了模型和方法,将其用于长江支流沱江枯水期的水质综合评价,结果表明,模糊人工神经网络综合评价具有客观性和实用性.     

人工神经网络模型在地下水水质评价分类中的应用

人工神经网络(Artificial Neural Network以下简称ANN)是一种行之有效的数据处理和分析方法,它的应用领域不断扩大并逐渐完善,本文在传统ANN方法基础上进行了进一步的探讨,立足于BP算法,通过调整ANN输出结构,提高其鲁棒性能,从而使其更具有适应性.将改进后的ANN应用于地下水水质评价分类,并和模糊综合评判评价结果进行了比较,分类结果令人满意.     

基于人工神经网络模型的岩石特性预测

近年来,软计算技术被用作替代的统计工具。如人工神经网络（ANN）被用于开发预测模型来估计所需的参数。在本研究中,通过利用冲击钻进过程中的一些钻进参数（气压、推力、钻头直径、穿透率）和所产生的声级,建立了预测岩石性质的神经网络模型。在实验室中所产生的数据,用于开发预测岩石特性（如单轴抗压强度、耐磨性、抗拉强度和施密特回弹数）的神经网络模型,并使用各种预测性能指标对所建模型进行检验,结果表明人工神经网络模型适用于岩石性质的预测。     

人工神经网络在矿井构造定量评价中的应用

探讨了矿井构造定量评价的人工神经网络方法, 结合东坡井田实际, 重点讨论了 BP模型的输入层、隐含层和输出层的构置和优选等问题, 并使用有序地质量最优分割方法和插值法得到学习样本, 经过学习样本的训练, 对未知单元进行评价取得了良好的效果。     

An enhanced approach to predict permeability in reservoir sandstones using artificial neural networks (ANN)

A key challenge in the oil and gas industry is the ability to predict key petrophysical properties such as porosity and permeability. The predictability of such properties is often complicated by the complex nature of geologic materials. This study is aimed at developing models that can estimate permeability in different reservoir sandstone facies types. This has been achieved by integrating geological characterization, regression models and artificial neural network models with porosity as the input data and permeability as the output. The models have been developed, validated and tested using samples from three wells and their predictive accuracy tested by using them to predict the permeability in a fourth well which was excluded from the model development. The results indicate that developing the models on a facies basis provides a better predictive capability and simpler models compared to developing a single model for all the facies combined. The model for the combined facies predicted permeability with a correlation coefficient of 0.41 which is significantly lower than the correlation coefficient of 0.97, 0.93, 0.99, 0.96, 0.96 and 0.85 for the massive coarse-grained sandstones, massive fine-grained sandstones-moderately sorted, massive fine-grained sandstones-poorly sorted, massive very fine-grained sandstones, parallel-laminated sandstones and bioturbated sandstones, respectively. The models proposed in this paper can predict permeability at up to 99% accuracy. The lower correlation coefficient of the bioturbated sandstone facies compared to other facies is attributed to the complex and variable nature of bioturbation activities which controls the petrophysical properties of highly bioturbated rocks.     

人工神经网络在徐州岩溶地面塌陷评价中的应用

岩溶地面塌陷的影响因素很多,发展过程也复杂。在众多的对岩溶地面塌陷的评价方法中,神经网络具有自学习、自适应与高度非线性映射的特点,是一种非常有效的评价手段。在徐州岩溶石地面塌陷的评价中,成功地运用了人工神经网络技术,它具有的强大非线性映射能力,能够建立评价因子和评价对象之间的关系,正确选取评价因子,避免主观判断取值,从而得出可靠的预测模型和岩溶塌陷危险性分区图。     

Validation of an artificial neural network model for landslide susceptibility mapping

The aim of this study was to validate an artificial neural network model at Youngin, Janghung, and Boeun, Korea, using the geographic information system (GIS). The factors that influence landslide occurrence, such as the slope, aspect, curvature, and geomorphology of topography, the type, material, drainage, and effective thickness of soil, the type, diameter, age, and density of forest, distance from lineament, and land cover were either calculated or extracted from the spatial database and Landsat TM satellite images. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the artificial neural network model. The landslide susceptibility analysis results were validated and cross-validated using the landslide locations as study areas. For this purpose, weights for each study area were calculated by the artificial neural network model. Among the nine cases, the best accuracy (81.36%) was obtained in the case of the Boeun-based Janghung weight, whereas the Janghung-based Youngin weight showed the worst accuracy (71.72%).     

人工神经网络在海浪数值预报中的应用

探讨将人工神经网络技术和传统的数值模式相结合,以期得到一个更有效的海浪预报方法.以第3代海浪模式的模拟结果作为输入,浮标观测资料作为输出,采用人工神经网络进行训练,训练的初步结果显示,人工神经网络可以改进海浪数值模式的预报精度,但在波高比较大时,改进的效果并不令人满意.为此,对观测值大于1.5m时的有效波高进行再训练,从而结果有了进一步的改善.研究结果证明人工神经网络技术可以提高海浪数值预报的精度.     

An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia

Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) techniques are being increasingly used for flood modeling. Previously, this ANN method was frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors using ANN techniques and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN model for this study was developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, remote sensing data, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. To measure the performance of the model, four criteria performances, including a coefficient of determination (R ²), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor.     

A risk evaluation model for karst groundwater pollution based on geographic information system and artificial neural network applications

The risk analysis on karst groundwater pollution is a research hotspot in current international hydrogeological field as well as the premise of preventing and controlling groundwater pollution. According to the characteristics of groundwater pollution in the typical study area, the study selected main-control factors of risk evaluation on karst groundwater pollution in mountainous areas at first. Based on this, the research determines the method for quantifying the factors and established a risk evaluation index system for karst groundwater pollution. To overcome drawbacks of the method for determining weights of factors in traditional evaluation method, the study determines the structure of the artificial neural network model by combining the selected evaluation factors. And also, the weight coefficients of evaluation factors on each layer are calculated. On this basis, the model for evaluating the risk of karst groundwater pollution is established. Moreover, the risk zoning evaluation map of groundwater pollution in the typical study area is prepared after conducting the weighted stacking of various sub-layers using the geographic information system. The method applied in the study can comprehensively and objectively reflect that the groundwater pollution is controlled by multiple factors and reveal the nonlinear characteristic of the pollution process. Additionally, the evaluation result is institutive and visible, which can provide a certain basis and reference for relevant researches.     

人工神经网络在边坡滑移预测中的应用

利用一项实际边坡工程的多年实测滑移资料，基于人工神经网络方法，建立了非线性人工神经网络分析模型，从而能够较精确地对该边坡的滑移进行预测，通过本工程实际的应用实践，发现它可避开复杂的本构模型而成为解决岩土工程问题的新途径。     

Storm surge prediction using an artificial neural network model and cluster analysis

In this study, an artificial neural network model was developed to predict storm surges in all Korean coastal regions, with a particular focus on regional extension. The cluster neural network model (CL-NN) assessed each cluster using a cluster analysis methodology. Agglomerative clustering was used to determine the optimal clustering of 21 stations, based on a centroid-linkage method of hierarchical clustering. Finally, CL-NN was used to predict storm surges in cluster regions. In order to validate model results, sea levels predicted by the CL-NN model were compared with results using conventional harmonic analysis and the artificial neural network model in each region (NN). The values predicted by the NN and CL-NN models were closer to observed data than values predicted using harmonic analysis. Data such as root mean square error and correlation coefficient varied only slightly between CL-NN and NN model results. These findings demonstrate that cluster analysis and the CL-NN model can be used to predict regional storm surges and may be used to develop a forecast system.     

Liquefaction resistance evaluation of soils using artificial neural network for Dhaka City,Bangladesh

Natural Hazards - Soil liquefaction resistance evaluation is an important site investigation for seismically active areas. To minimize the loss of life and property, liquefaction hazard analysis is...     

Developing artificial neural network models to predict allowable bearing capacity and elastic settlement of shallow foundation in Sharjah,United Arab Emirates

This research proposes the use of artificial neural network to predict the allowable bearing capacity and elastic settlement of shallow foundation on granular soils in Sharjah, United Arab Emirates. Data obtained from existing soil reports of 600 boreholes were used to train and validate the model. Three parameters (footing width, effective unit weight, and SPT blow count) are considered to have the most significant impact on the magnitude of allowable bearing capacity and elastic settlement of shallow foundations, and thus were used as the model inputs. Throughout the study, depth of footing was limited to 1.5 m below existing ground level and water table depth taken at the level of the footing. Performance comparison of the developed models (in terms of coefficient of determination, root mean square error, and mean absolute error) revealed that the developed artificial neural network models could be effectively used for predicting the allowable bearing capacity and elastic settlement. As such, the developed models can be used at the preliminary stage of estimating the allowable bearing capacity and settlements of shallow foundations on granular soils, instead of the conventional methods.     

人工神经网络在铀矿测井解释中的应用

本文探讨了运用人工神经网络方法完成铀矿测井解释任务的有关问题。采用了改进的BP算法，提高了网络收敛速度，优化了网络结构。研究使用了一种基于统计的学习样本生成方法，提高了样本的质量。实际应用网络进行岩性识别和孔隙度预测，取得了令人满意的结果。