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1.
基于模糊识别的水资源承载能力综合评价   总被引:14,自引:0,他引:14       下载免费PDF全文
应用模糊识别模型解决水资源承载能力综合评价问题,根据水资源承载能力相关因子自身的数据结构特性,通过模糊识别模型得到样本的级别特征值,从而达到对待评价区域的识别、归类,同时可以得到各相关因子在评价过程中的贡献率,实例部分将模型应用于全国30个省市、自治区的水资源承载能力评价,结果表明该方法的实用性和客观性。  相似文献   

2.
物化探综合异常模糊分级评价及识别   总被引:1,自引:1,他引:0  
针对物化探综合异常反映出的高维空间复杂结构和相互之间复杂关系等现象,通过对多维信息的降维处理,提取综合异常的特征参量,建立了表述物化探综合异常的初始模型。以模糊数学为手段,对已知区的综合异常进行模糊分类评价,建立了物化探综合异常评价模型。利用人工神经网络,通过学习建立识别系统,对未知综合异常进行识别,完成了对复杂物化探综合异常的评价。实例表明,该评价方法是合理可行的。  相似文献   

3.
针对金刚石钻头综合评价中各指标的不相容性和模糊性,提出基于模糊物元分析的金刚石钻头综合评价方法.该方法将待评价金刚石钻头、评价指标及其特征值作为物元,根据专家经验和工程实际建立关联函数,通过计算综合关联度得到最优的金刚石钻头.用实例与模糊优化理论模型等进行了对比,得到了一致的结果.  相似文献   

4.
利用模糊神经网络进行砂土液化势评判   总被引:9,自引:0,他引:9  
利用模糊信息分析表达知识和人工神经网络在映射能力方面的优势, 选取应力比、震级、地面运动最大加速度、标贯击数、地下水位作为评价参数指标, 构造砂土液化势识别的模糊神经网络模型。验证和应用结果表明, 模糊神经网络模型可提供更高的映射能力, 是砂土液化势评价预测的有效手段。  相似文献   

5.
王丽娟  潘俊  杨鑫  韩春阳 《地下水》2011,33(3):103-104
针对目前关于水环境质量评价方法研究广泛,每一种方法的研究是为了客观和准确地反映水体水质的实际情况.选择B-P人工神经网络法、模糊综合评价法和灰色关联分析法,进行分析和比较,并以沈阳市南运河水环境质量评价为例,试图找出3种评价方法的优缺点和适用范围.  相似文献   

6.
模糊综合评价法在地下水水质评价中的应用   总被引:13,自引:1,他引:13  
李进  陈益滨  师伟  梁煦枫 《地下水》2006,28(2):4-5,22
将模糊数学应用于水质评价中.模糊综合评价法通过建立和确定因子集、评价集、隶属函数和权重集等模糊集合,进行环境评价.选择咸阳市地表水环境质量对模糊综合评价法进行验证.评价结果符合实际情况.说明模糊综合评价法可用于环境质量综合评价.  相似文献   

7.
基于模糊神经网络的泥石流危险性评价   总被引:1,自引:1,他引:0       下载免费PDF全文
将T-S模糊系统理论和人工神经网络相结合,利用模糊理论隶属度对模糊性有很强的识别精度,将泥石流危险性指标隶属度作为神经网络的激活函数输入,使用正态分布方法产生训练数据,再利用BP神经网络的误差反向传播对TS模糊系统隶属度函数等的参数进行训练调整,从而建立泥石流危险性评价的模糊神经网络模型。利用建立好的模型对云南东川八条典型泥石流沟的危险性进行评价,获得客观合理的评价结果。与刘希林的灰色聚类法和可拓物元方法的纵向对比和与线性内插产生训练数据方法横向对比表明:使用该方法能较真实地反映实际泥石流沟的危险性等级,证明模糊神经网络理论应用于泥石流危险性评价的有效性和可行性。  相似文献   

8.
基于人工神经网络的区域地质灾害危险性预测评价   总被引:7,自引:0,他引:7  
地质灾害危险性预测评价的准确性,主要取决于基础资料的可靠性和数学模型的合理性。论文结合工程实例,尝试用人工神经网络方法(改进的神经网络BP模型)对区域地质灾害危险性预测进行评价研究。然后与目前常用的方法(如层次分析法、信息量法和模糊综合评判法等)所得出的结果相比较。结果表明,运用人工神经网络方法对区域地质灾害危险性预测评价相对常用方法更准确、可靠,具有一定的实用意义及推广价值。  相似文献   

9.
洞室围岩质量多因素模糊综合评价模型及应用   总被引:3,自引:0,他引:3       下载免费PDF全文
由于洞室围岩质量及其影响因素的复杂性和不确定性,以往采用定指标、定权重的评价方法不尽合理,因此,有必要建立一多因素模糊评价模型.首先,应用模糊数学方法,建立了考虑岩石的单轴抗压强度σc,RQD指标,结构面间距J1,结构面摩擦系数f和岩体的声波速度V的多因素洞室围岩综合评价模型;然后,以地下洞室围岩为工程背景,对岩体质量进行了模糊综合评价,结果表明:模糊评价方法适用于不同岩体工程,能最大限度地利用工程勘察成果,是解决多因素、多指标综合问题的有效决策方法.得到的结果与可拓学评价法以及RMR法得到的结果大致相同,符合实际情况,说明多因素综合评价模型的正确性.  相似文献   

10.
以玛纳斯河流域水资源承载力为研究对象,采用基于变异系数的模糊物元分析处理分类界限多层次、多因素的模糊边界问题,对流域水资源承载力进行综合评价.评价结果表明,变异系数法得到的权重客观合理,物元分析法通过欧氏贴近度判定评价对象和标准之间的贴近程度,能比较客观、有效地对流域水资源承载力进行综合评价.  相似文献   

11.
针对红板岩材料在岩土工程中所表现的大量模糊的和不确定的因素等特点,基于人工神经网络的学习能力,借助于室内岩石力学试验,进行了对该材料的力学本构特性进行了神经网络模拟研究,提出了隐式本构模型的思想和方法,并通过该方法对该岩石的流变试验结果进行学习,获得了以网络权值结构保存的力学特性知识,由此得到了表征红板岩应力应变本构关系的隐式本构模型。应用结果表明,该方法对岩土类材料本构关系的模拟研究具有很好的应用前景。  相似文献   

12.
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   

13.
径流预报的模糊神经网络方法   总被引:3,自引:0,他引:3  
径流预报是水资源系统优化调度的重要因素。探讨了神经网络及模糊模式识别的部分理论,在通常的神经网络预报模型及模糊模式识别预报模型的基础上提出了一种模糊模式识别神经网络预报模型及其相应算法,加强了系统的知识表达能力,使预报结果更为可信。通过实例计算,验证了模型的可行性以及所给算法对模型训练的有效性,从而为径流预报提供了一种新方法。  相似文献   

14.
Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619).  相似文献   

15.
针对岩溶隧道突水风险评估的不确定性和复杂性以及传统的数学方法在评估安全风险等级中的局限性,将人工神经网络理论、小波分析及模糊评价法有机结合,建立了基于模糊小波神经网络的岩溶突水安全风险评估模型。根据各种物探方法的优缺点和对岩溶水预报的敏感性,结合综合超前地质预报方法和原则,提出地质分析、风险等级划分、分级综合预报及施工地质灾害临近警报技术相结合的综合地质预报方案。通过在齐岳山岩溶隧道实施,成功预报了隧道掌子面前方的岩溶水,证实了该方案的科学性和可行性。  相似文献   

16.
Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices.  相似文献   

17.
Landslide-related factors were extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, and integrated techniques were developed, applied, and verified for the analysis of landslide susceptibility in Boun, Korea, using a geographic information system (GIS). Digital elevation model (DEM), lineament, normalized difference vegetation index (NDVI), and land-cover factors were extracted from the ASTER images for analysis. Slope, aspect, and curvature were calculated from a DEM topographic database. Using the constructed spatial database, the relationships between the detected landslide locations and six related factors were identified and quantified using frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) models. These relationships were used as factor ratings in an overlay analysis to create landslide susceptibility indices and maps. Three landslide susceptibility maps were then combined and applied as new input factors in the FR, LR, and ANN models to make improved susceptibility maps. All of the susceptibility maps were verified by comparison with known landslide locations not used for training the models. The combined landslide susceptibility maps created using three landslide-related input factors showed improved accuracy (87.00% in FR, 88.21% in LR, and 86.51% in ANN models) compared to the individual landslide susceptibility maps (84.34% in FR, 85.40% in LR, and 74.29% in ANN models) generated using the six factors from the ASTER images.  相似文献   

18.
Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.  相似文献   

19.
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   

20.
This paper aims to provide a spatial and temporal analysis to prediction of monthly precipitation data which are measured at irregularly spaced synoptic stations at discrete time points. In the present study, the rainfall data were used which were observed at four stations over the Qara-Qum catchment, located in the northeast of Iran. Several models can be used to spatially and temporally predict the precipitation data. For temporal analysis, the wavelet transform with artificial neural network (WTANN) framework combines with the wavelet transform, and an artificial neural network (ANN) is used to analyze the nonstationary precipitation time-series. The time series of dew point, temperature, and wind speed are also considered as ancillary variables in temporal prediction. Furthermore, an artificial neural network model was used for comparing the results of the WTANN model. Therefore, four models were developed, including WTANN and ANN with and without ancillary data. Several statistical methods were used for comparing the results of the temporal analysis. It was evident that at three of the four stations, the WTANN models were more effective than the ANN models, and only at one station, the ANN model with ancillary data had better performance than the WTANN model without ancillary data. The values of correlation coefficient and RMSE for WTANN model with ancillary data for the validation period at Mashhad station which showed the best results were equal to 0.787 and 13.525 mm, respectively. Finally, an artificial neural network model was used as an alternative interpolating technique for spatial analysis.  相似文献   

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