基于神经网络的西藏拉萨地区生态环境分类

根据生态环境分类指标的科学性、完备性、简洁性和数据的可获取性,选取了影响拉萨地区生态环境的主要地形和气候因子高程、坡向、≥0℃积温、年平均温度、年平均降水量、潜在蒸散量和湿润度等7个有代表性的指标,利用GIS的空间内插方法将所有这些指标转成100 m×100 m的空间珊格数据,再根据每个指标特定的地理和环境意义进行指标的分带,对7个指标进行主成分分析后提取主要信息。通过选择4个典型样区作为训练区,对拉萨地区的生态环境进行了分类。结果表明,拉萨地区的主要生态环境类型包括河谷农业类型、山地草原类型、高山草甸类型及高山裸岩及冰雪类型。其中,高山草甸和山地草原生态环境类型占主导,分别为10 768.52 km2和10 646.6 km2,各占总面积的36.61%和36.20%,而河谷农业类型占总面积的10.75%。此外,拉萨地区分布有较大面积的高山裸岩及冰雪区生态环境类型,面积为总面积的14.16%。作为特殊类型的生态环境类型,拉萨地区境内的纳木错的湖泊面积是668.76 km2,占该湖面积的近一半和拉萨地区总面积的2.27%。     

基于径流分类的流域降雨-径流过程动态神经网络建模

利用聚类分析,将径流序列分为不同类型的子径流序列,对这些子序列建立神经网络模型,采用Elman动态神经网络对沂沭河流域上游临沂子流域日径流量进行预测分析,通过与不加分类的总体神经网络的模拟结果进行对比分析。确定性系数、相关系数、平均相对误差和平均相对均方根误差4个统计指数及流域径流过程线和次洪误差分析结果都表明：Elman动态神经网络能够对日径流量进行较好模拟,但基于径流分类的降雨-径流模型表现出更优良性能,能较大程度提高径流模拟精度。     

Mass and Metallurgical Balance Forecast for a Zinc Processing Plant Using Artificial Neural Networks

Natural Resources Research - The forecasting of ore concentrate and tailings mass and metallurgical recovery at a processing plant is not a simple task. It starts with data collection, which is...     

Application of Artificial Neural Networks to Complex Groundwater Management Problems

As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models.     

Mineral Favorability Mapping: A Comparison of Artificial Neural Networks,Logistic Regression,and Discriminant Analysis

A Probabilistic Neural Network (PNN) was trained to classify mineralized and nonmineralized cells using eight geological, geochemical, and geophysical variables. When applied to a second (validation) set of well-explored cells that had been excluded from the training set, the trained PNN generalized well, giving true positive percentages of 86.7 and 93.8 for the mineralized and nonmineralized cells, respectively. All artifical neural networks and statistical models were analyzed and compared by the percentages of mineralized cells and barren cells that would be retained and rejected correctly respectively, for specified cutoff probabilities for mineralization. For example, a cutoff probability for mineralization of 0.5 applied to the PNN probabilities would have retained correctly 87.66% of the mineralized cells and correctly rejected 93.25% of the barren cells of the validation set. Nonparametric discriminant analysis, based upon the Epanechnikov Kernel performed better than logistic regression or parametric discriminant analysis. Moreover, it generalized well to the validation set of well-explored cells, particularly to those cells that were mineralized. However, PNN performed better overall than nonparametric discriminant analysis in that it achieved higher percentages of correct retention and correct rejection of mineralized and barren cells, respectively. Although the generalized regression neural network (GRNN) is not designed for a binary—presence or absence of mineralization— dependent variable, it also performed well in mapping favorability by an index valued on the interval [0, 1]. However, PNN outperformed GRNN in correctly retaining mineralized cells and rejecting barren cells of the validation set.     

Predicting Blast-Induced Air Overpressure: A Robust Artificial Intelligence System Based on Artificial Neural Networks and Random Forest

Natural Resources Research - Blasting is the most popular method for rock fragmentation in open-pit mines. However, the side effects caused by blasting operations include ground vibration, air...     

基于GIS和ANN的农户生计脆弱性的空间模拟分析

基于农户生计脆弱性测定方式的不完善和云贵高原缺乏农户生计脆弱性研究,选取云南省宜良县为案例区,构建了农户生计脆弱性评价指标体系,包括农户面临的风险、农户生计资本和面对风险的应急能力,并运用GIS与BP神经网络模拟区域的风险度指数、农户生计资本和应急能力指数的空间分布格局。在此基础上,得到农户生计脆弱性指数,结果表明坝区的生计脆弱性指数为山区>半山区>坝区,且各自原因不同。可见,运用BP神经网络模拟生计脆弱性简便实用,是一种可行的实践方法。     

Typing Mineral Deposits Using Their Grades and Tonnages in an Artificial Neural Network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further.Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage).Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag.Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.     

应用人工神经网络识别闽南粤东沿海老红沙沉积

闽南、粤东沿海,断续分布着一种俗称"老红砂"的红色、棕红色半胶结的中细砂沉积物。近十多年来,对其成因和时代进行了较多研究。然而,关于它的成因,目前仍有争议,有的认为是属于一种近源的滨海相沉积,也有人认为是风成的。本文用经过华南沿海现代海岸风成沙和海滩沙样品训练的神经网络来识别闽南粤东沿海的老红砂。由这些海岸风沙和海滩沙的粒度参数、沉积物的各个粒级含量等作为输入,构成不同的神经网络的识别结果表明,大部分老红砂被神经网络判别为风沙沉积,同时也表明,粉沙/粘土的含量是判别沉积物是否为风沙搬运的有效指标。而单纯以沉积物各个粒级含量作为输入构成的网络无法用于沉积物的识别。     

应用人工神经网络模型研究福建省山仔水库叶绿素a动态

本文应用山仔水库2003-2006年叶绿素a浓度、总磷浓度、总氮浓度、水温、溶解氧浓度、高锰酸盐指数、pH值7个参数监测数据对人工神经网络模型进行训练,在此基础上应用1997-2002年除叶绿素a浓度外其他6个参数监测数据,推算出1997-2002年间缺失的叶绿素a浓度,并对1997-2006年春末夏初的叶绿素a浓度动态进行分析,结果表明:山仔水库1997年建库初期,叶绿素a浓度处于较高水平,2000年以后叶绿素a浓度开始降低,近几年基本保持稳定.2003-2006年叶绿素a浓度呈季节周期性变化,春末经夏季到初秋,叶绿素a浓度持续升高,冬季下降明显,春季又开始回升;说明近几年山仔水库水体春末夏季秋初处于富营养化水平,秋末冬季处于中营养水平.本研究结果将为山仔水库的富营养化防治提供科学依据.     

应用人工神经网络模型研究福建省山仔水库叶绿素a动态

本文应用山仔水库2003-2006年叶绿素 a浓度、总磷浓度、总氮浓度、水温、溶解氧浓度、高锰酸盐指数、pH值7个参数监测数据对人工神经网络模型进行训练,在此基础上应用1997-2002年除叶绿素a浓度外其他6个参数监测数据,推算出1997-2002年间缺失的叶绿素a浓度,并对1997-2006年春末夏初的叶绿素a浓度动态进行分析,结果表明:山仔水库1997年建库初期,叶绿素a浓度处于较高水平,2000年以后叶绿素a浓度开始降低,近几年基本保持稳定.2003-2006年叶绿素a浓度呈季节周期性变化,春末经夏季到初秋,叶绿素a浓度持续升高,冬季下降明显,春季又开始回升;说明近几年山仔水库水体春末夏季秋初处于富营养化水平,秋末冬季处于中营养水平.本研究结果将为山仔水库的富营养化防治提供科学依据.     

A Comparative Analysis of Favorability Mappings by Weights of Evidence,Probabilistic Neural Networks,Discriminant Analysis,and Logistic Regression

This study compares the performance of favorability mappings by weights of evidence (WOE), probabilistic neural networks (PNN), logistic regression (LR), and discriminant analysis (DA). Comparisons are made by an objective measure of performance that is based on statistical decision theory. The study further emphasizes out-of-sample inference, and quantifies the extent to which outcome is influenced by optimum variable discretization with classification and regression trees (CARTS).Favorability mapping methodologies are evaluated systematically across three case studies with contrasting scale and geologic information:

Prediction of Dust Emission Due to Open Pit Mine Blasting Using a Hybrid Artificial Neural Network

Natural Resources Research - This study developed a new perspective of artificial neural networks using dimensional analysis to be applicable to certain prediction problems. To this end,...     

Artificial Neural Networks in Proglacial Discharge Simulation: Application and Efficiency Analysis in Comparison to the Multivariate Regression; A Case Study of Waldemar River (Svalbard)

Artificial neural networks were applied to simulate runoff from the glacierized part of the Waldemar River catchment (Svalbard) based on hydrometeorological data collected in the summer seasons of 2010, 2011 and 2012. Continuous discharge monitoring was performed at about 1 km from the glacier snout, in the place where the river leaves the marginal zone. Averaged daily values of discharge and selected meteorological variables in a number of combinations were used to create several models based on the feed‐forward multilayer perceptron architecture. Due to specific conditions of melt water storing and releasing, two groups of models were established: the first is based on meteorological inputs only, while second includes the preceding day's mean discharge. Analysis of the multilayer perceptron simulation performance was done in comparison to the other black‐box model type, a multivariate regression method based on the following efficiency criteria: coefficient of determination (R²) and its adjusted form (adj. R²), weighted coefficient of determination (wR²), Nash–Sutcliffe coefficient of efficiency, mean absolute error, and error analysis. Moreover, the predictors' importance analysis for both multilayer perceptron and multivariate regression models was done. The performed study showed that the nonlinear estimation realized by the multilayer perceptron gives more accurate results than the multivariate regression approach in both groups of models.     

A Comparison of the Weights-of-Evidence Method and Probabilistic Neural Networks

The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of –1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.     

电导法测定LiCl在水溶液中的活度系数

应用电导法测定了LiCl水溶液的活度系数,首先在288~308 K温度范围内测定LiCl在极性水溶剂中的电导率,利用公式计算LiCl的摩尔电导率,应用Debye-Hücker和Osager-falkenhangen公式计算LiCl在水中的活度系数,并讨论了温度和浓度对LiCl水溶液活度系数的影响。     

Waveform Characteristics of Earthquakes Induced by Hydraulic Fracturing and Mining Activities: Comparison with Those of Natural Earthquakes

Natural Resources Research - Some industrial activities, such as underground mining, hydraulic fracturing (HF), can cause microearthquakes and even damaging earthquakes. In recent years, with the...     

Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine

Natural Resources Research - It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted...     

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。     

中国区域交通优势的甄别方法及应用分析

一个区域的交通优势反映在“质”、“量”和“势”三个方面, 每个方面具有相对独立而具体的内涵, 对区域社会经济的发展具有不同的作用, 其中任一方面的刻画和评价仅仅反映区域交通优劣的一个侧面, 只有三方面的综合集成刻画与评价才能真正反映一个区域交通环境的优劣。基于交通地理学的基本理论, 界定了交通优势度的基本概念, 并建立了交通优势度的基本表述结构, 包括交通网络密度、交通干线影响度和区位优势度; 同时基于GIS 技术, 从分项和综合集成两个角度构筑了交通优势度评价的空间数理模型。以我国2365 个地域 单元为样本的实证分析发现, 我国的区域交通优势度呈“偏正态”分布特征, 极少数的地域 (比例为1.4%) 具有非常突出的交通优势, 社会经济发展具有非常优越的交通环境; 大约1/8 的地域(12.4%) 交通条件处于非常明显的劣势, 交通环境是其社会经济发展的不利条件; 大约70%地域处于评价样本的中游或中游偏上水平。从区域上看, 交通优势大致由沿海逐渐向内陆依次递减; 长江三角洲、京津冀、珠江三角洲三大城镇密集区有着明显的交通优势, 覆盖范围广; 成渝地区和武汉都市圈也有较好的交通优势, 但尚未连续成面或覆盖范围较小, 其他城镇密集区和省会城市周边地区有相对较高的交通优势, 但覆盖地域较小。从经济发展措施看, 利用交通优势和规避交通劣势, 应是进行经济活动和产业选择需要考虑的重要因素。