Neural Network-Based Estimation of Principal Metal Contents in the Hokuroku District,Northern Japan,for Exploring Kuroko-Type Deposits

The Hokuroku district, extending over 40 × 40 km² in northern Japan, is known to be dominated by kuroko-type massive sulfide deposits that have a genetic relation to submarine volcanic activity. The deposits are hosted in a specific stratigraphic zone of Miocene volcanic rocks. Because kuroko-type deposits are under exploration in several countries, it is important to integrate the geologic and geochemical data that have been accumulated in the Hokuroku district to characterize the distribution of deposits and produce a map of mineral potential. Thus, we collected data on multiple chemical components from 1917 rock cores at 143 drillhole sites and concentrated on components with relatively large amounts of data, which are SiO₂, Al₂O₃, and Fe₂O₃ as major elements and Cu, Pb, and Zn as trace elements. Although frequencies of these data can be approximated by normal or lognormal distributions, spatial correlation structures cannot be extracted from the semivariograms of each component nor from the cross-semivariograms between two components of the major or minor elements. To handle such complexity, a spatial method of modeling content distribution, SLANS, is developed by applying a feedforward neural network. The principle of SLANS is to train a network repeatedly to recognize the relation between the data value and the location and lithology of a sample point. One-hundred outputs for each element are obtained by changing the numbers of neurons in a middle layer from 1 to 10 and sample data used for training from 3 to 12, and finally one output is selected based on the estimation precision of the network which is restricted near the target point. After constructing a geologic distribution model from the geological column classified into 25 rock codes, three-dimensional distributions of Cu, Pb, and Zn contents are estimated over the study area. The content models are considered to be valid because high-content zones are located on the known mine sites and the margins of ancient volcanoes or calderas. Some zones are distributed along strikes of major deep-seated fractures in the district.     

Characterizing Content Distributions of Impurities in a Limestone Mine Using a Feedforward Neural Network

The factors determining the suitability of limestone for industrial use and its commercial value are the amounts of calcium oxide (CaO) and impurities. From 244 sample points in 18 drillhole sites in a limestone mine, southwestern Japan, data on four impurity elements, SiO₂, Fe₂O₃, MnO, and P₂O₅ were collected. It generally is difficult to estimate spatial distributions of these contents, because most of the limestone bodies in Japan are located in the accretionary complex lithologies of Paleozoic and Mesozoic age. Because the spatial correlations of content data are not clearly shown by variogram analysis, a feedforward neural network was applied to estimate the content distributions. The network structure consists of three layers: input, middle, and output. The input layer has 17 neurons and the output layer four. Three neurons in the input layer correspond with x, y, z coordinates of a sample point and the others are rock types such as crystalline and conglomeratic limestones, and fossil types related to the geologic age of the limestone. Four neurons in the output layer correspond to the amounts of SiO₂, Fe₂O₃, MnO, and P₂O₅. Numbers of neurons in the middle layer and training data differ with each estimation point to avoid the overfitting of the network. We could detect several important characteristics of the three-dimensional content distributions through the network such as a continuity of low content zones of SiO₂ along a Lower Permian fossil zone trending NE-SW, and low-quality zones located in depths shallower than 50 m. The capability of the neural network-based method compared with the geostatistical method is demonstrated from the viewpoints of estimation errors and spatial characteristics of multivariate data. To evaluate the uncertainty of estimates, a method that draws several outputs by changing coordinates slightly from the target point and inputting them to the same trained network is proposed. Uncertainty differs with impurity elements, and is not based on just the spatial arrangement of data points.     

基于遗传算法优化BP神经网络的土壤盐渍化反演

应用于土壤盐分含量(Soil Salinity Content,SSC)反演的BP神经网络(Back Propagation Neural Network,BPNN)较少关注对模型精度影响较大的结构参数和初始权重的优化。该文利用Landsat-8 OLI、Sentinel-1 SAR影像数据及SRTM高程数据,基于谷歌地球引擎(GEE)平台构建反演参数,并建立3种反演模型:先利用遗传算法(Genetic Algorithm,GA)同步优化输入层反演参数子集和隐含层神经元数量,再优化初始权重的BPNN(GA-BP)模型;将变量投影重要性(Variable Importance in Projection,VIP)算法分割阈值分别设为1和0.5,优化出两组输入层反演参数子集并将其分别代入GA优化隐含层神经元数量,再优化初始权重的BPNN(VIP1-GA-BP、VIP2-GA-BP)模型。在玛纳斯流域和三工河流域各选一靶区进行SSC反演,对比分析GA-BP、VIP1-GA-BP、VIP2-GA-BP模型的反演精度,并统计各类盐渍土的面积比例,结果表明:1)两靶区3组模型反演精度由高到低排序均为GA-BP、VIP1-GA-BP、VIP2-GA-BP;2)盐分指数和植被指数在SSC反演中起到重要作用,同一模型筛选的反演参数存在空间分异性,但高程适用于不同的筛选模型,具有较强的鲁棒性;3)两靶区3组模型反演的SSC值域范围与实际采样点SSC值域范围的差异均较小,各子区GA-BP反演的SSC空间分布地物轮廓最清晰,且地物内SSC的均质性最好;4)玛纳斯靶区和三工河靶区面积占比最大的盐渍土类型分别为盐渍土和中度盐渍土。研究结果为构建具有一定推广性的干旱区土壤盐分含量反演模型奠定了基础。     

基于PCA和BP神经网络的径流预测

径流预测为流域水资源的合理开发利用与统筹配置提供依据。运用多元线性回归、主成分回归、BP神经网络及主成分分析和BP神经网络相结合的方法,对新疆呼图壁河流域石门水文站2009-2011年各月径流量进行预测,并采用相关系数、确定性系数及均方根误差对各模型预测精度进行比较。结果表明：（1）神经网络等智能算法具有高速寻优的能力,对短时间尺度的月径流量的预测结果较好;（2）主成分回归等常规算法能充分反映出某地区径流的年际的稳定性,对全年径流总量的模拟精度较高;（3）主成分分析和BP神经网络相结合的方法,提高了神经网络的收敛速度,同时降低了局部极值的影响,优于简单的BP神经网络,适用于呼图壁河月径流量预测。     

流域水资源丰富度评价的自组织神经网络模型

水资源丰富度评价是开展水文区划和水利化区划工作重要的科学依据[1] 。针对水资源丰富度与其影响因素之间复杂的非线性关系 ,该文提出应用自组织神经网络模型来评价流域水资源的丰富度 ,解决了在水文条件复杂的地区训练 (学习 )样本难以获得的难题。     

水文模型参数敏感性快速定量评估的RSMSobol方法

水文模型参数敏感性分析是模型不确定性量化研究的重要环节,其可以有效识别关键参数,减少模型率定的不确定性,提高模型优化效率。然而如何快速有效地定量评估参数敏感性已成为当前大尺度分布式水文模型优化的瓶颈。针对传统的全局定量敏感性分析方法在多参数复杂水文模型的不足,本文采用基于统计学习理论的支持向量机(SVM) 建立非参数响应曲面(称为代理模型),再结合基于方差的Sobol 方法,建立了基于响应曲面方法的Sobol 定量全局敏感性分析方法(RSMSobol 方法),实现复杂模型系统参数敏感性的快速定量化评估。本文选用淮河流域的日尺度分布式时变增益水文模型进行实例研究,采用水量平衡系数(WB),Nash-Sutcliffe 效率系数(NS) 和相关系数(RC) 三个目标函数综合评价模拟效果。研究结果显示RSMSobol方法在实现定量全局敏感性分析的同时降低了模型运行时耗,提高了模型评估效率,且与传统定量方法Sobol 方法具有同样的评估效果。该方法的有效应用为大型复杂水文动力模拟系统的参数定量化敏感性评价提供了参考,为模型参数进一步优化提供了可靠依据。     

Use of a Probabilistic Neural Network to Reduce Costs of Selecting Construction Rock

Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.     

基于新型联想记忆神经网络的非线性系统辨识

Hopfield网络模型具有联想存储器功能,但对系统辨识不适用。具有动态记忆功能的Elman神经网络的泛化能力比较低。该文提出了一种新型联想记忆神经网络结构和学习算法,通过引入联想记忆衰减因子,提高了对非线性系统的辨识能力。通过与Elman动态神经网络辨识方法的仿真比较,说明联想记忆神经网络辨识方法具有很好的动态辨识能力和泛化能力。     

甘肃省县域城市化水平差异的人工神经网络测定

运用人工神经网络的理论和方法,构建BP神经网络,评价2009年甘肃省县域城市化水平,将87个县域城市化水平分为5级。对频数分布特征、变异系数、威廉森系数和最大与最小系数的分析表明,甘肃省县域城市化空间分异显著。具体表现为:呈正偏态分布,第三、四级别的县市比例较大;城市化水平发展不均衡,呈现西北—东南差异;经济区内部差异大,表现为西北高、东南低的趋势。利用Spearman’s rho相关分析得出影响城市化水平的因素及相关度。     

基于Hopfield神经网络的中国近40年气候要素时空变化分析

利用1961~2000年中国大部分省区(香港、澳门、台湾、海南地区数据暂缺)194个气象站点逐日降水量、气温和相对湿度数据,通过克里格插值、Hopfield神经网络聚类以及方差分析,对中国气候变化的时空特征进行分析。分析结果表明:中国以增温为主导趋势,其次是多雨趋势;东部地区出现变干趋势,而西部地区在逐渐增湿;对3种要素进行聚类分析,并利用方差分析检验差异性是否显著,最后分析了聚类结果变化趋势,结果与插值分析一致,说明克里格插值结果的可信性。表明在全球增温的驱动下,中国气候变化格局处于调整状态,湿润地区干旱化,干旱地区变得湿润。     

BP神经网络模型在伏旱预测中的应用——以河西走廊为例

利用河西走廊伏旱和伏期降水资料序列,张掖观象台的地面气温、降水、探空等气象资料,以及国家气候中心提供的74个环流因子,借助BP神经网络可以逼近任意非线性函数的能力和特点,构建了一个用于预测伏旱和伏期降水的模型,并对模型的预报效果进行验证。结果表明:BP神经网络模型能够对伏期干旱进行有效地预测,该预测模型对伏旱和伏期降水有比较理想的预报效果,伏旱预报历史拟合率高达97.6%、模型试报准确率为84.6%,伏期降水预测历史拟合率高达97.6%、模型试报准确率为76.9%,其性能指标符合实际要求,具有很好的实际应用价值。     

GEO-Environmental Suitability Evaluation of Land for Urban Construction Based on A Back-Propagation Neural Network and GIS: A Case Study of Hangzhou

Evaluating the geo-environmental suitability of land for urban construction is an important step in the analysis of urban land use potential. Using geo-environmental factors and the land use status of Hangzhou, China, a back-propagation (BP) neural network model for the evaluation of the geo-environmental suitability of land for urban construction was established with a geographic information system (GIS) and techniques of grid, geospatial, and BP neural network analysis. Four factor groups, comprising nine separate subfactors of geo-environmental features, were selected for the model: geomorphic type, slope, site soil type, stratum steadiness, Holocene saturated soft soil depth, groundwater abundance, groundwater salinization, geologic hazard type, and geologic hazard degree. With the support of the model, the geo-environmental suitability of Hangzhou land for urban construction was divided into four suitability zones: zone I, suitable for super high-rise and high-rise buildings; zone II, suitable for multi-story buildings; zone III, suitable for low-rise buildings; and zone IV, not suitable for buildings. The results showed that a BP neural network can capture the complex non-linear relationships between the evaluation factors and the suitability level, and these results will support scientific decision-making for urban-construction land planning, management, and rational land use in Hangzhou.     

不同尺度流域月径流模拟对比与敏感性分析——以西江流域为例

通过新安江月径流模型耦合加速遗传算法对西江流域3个不同尺度子流域——新兴江流域(1 790 km2)、罗定江流域(3 164 km2)和贺江流域(8 326 km2)进行了模拟,同时对模型参数进行了敏感性分析.结果表明:1)在模型参数较多的情况下,用加速遗传算法能很快找到最优化参数,通过参数的初始区间的合理设定可以使参数值达到全局最优,并且使其符合参数的实际取值情况;2)在1 000～9 000 km2流域面积内只有1个气象站的情况下,利用新安江月径流模型模拟的Nash-Sutcliffe效率系数为65%以上;3)降雨径流的相关性越强,则模拟精度越高;4)20世纪80年代后,罗定江流域径流的变化主要受降雨的影响,人类活动影响相对其他2个子流域弱;5)新安江月径流模型的敏感参数为蒸发系数K、地下水出流系数KG、地下水消退系数CG和自由水蓄水容量SM(0～10),参数的取值范围会影响到模型参数的敏感性;异参同效仍然是流域水文模型的不确定性的一个重要方面,GLUE方法能够反映这一点.     

武汉气候舒适度与游客网络关注度年内变化相关性分析

气候舒适度对旅游地旅游需求量及游客网络关注度具有重要影响．通过对武汉市气候舒适度的评价,并结合2009-2011年游客网络关注度统计资料,采用OLS方法构建了游客网络关注度月指数与气候舒适度的回归模型．研究表明,武汉市气候舒适度指数以及游客网络关注度都呈现出“M”形变化特征,而且游客网络关注度月指数的气候弹性系数为0．883％．本研究为预测武汉市旅游需求量年内变化、游客接待和景区管理提供科学依据．     

城市气候舒适度与游客网络关注度时空相关分析

本文在系统收集城市气候及游客网络关注度数据的基础上,对30个城市气候舒适度和游客网络关注度的时空变化进行了分析,并利用综合舒适指数、经济发展水平、旅游资源丰度、节假日虚拟因子,采用OLS方法建立了游客网络关注度与气候舒适度的时空相关模型,结果显示:①气候舒适度的时空变化主要受地理纬度的影响,按城市气候舒适指数的年内变化...