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。     

小波包分解与多个机器学习模型耦合在风速预报中的对比

准确预报风速是提高风电利用率以及电力系统稳定性的有效方法。学者们提出了大量风速预报模型,但针对不同下垫面不同风速预报模型的对比研究较少。该研究主要探究小波包分解和12个机器学习模型耦合对3种下垫面（戈壁、绿洲和沙漠）风速预报能力,探索风速预报的优化耦合模型。设置3组模型实验进行对比：单一机器学习模型、小波包分解-机器学习混合模型和小波包分解-机器学习-卷积神经网络混合模型。结果表明：具有特征选择和记忆功能的深度学习模型（如卷积长短时记忆网络）以及极限学习机对风速具有较好的预报能力,小波包分解可以显著提高模型精度。小波包分解与卷积长短时记忆网络、卷积门控循环单元和极限学习机的耦合模型在风速预报中具有较好的表现。这表明信号分解和深度学习的耦合模型,能有效提高预报精度,值得推广。     

遥感技术提取海岸线的研究进展

海岸带是比较活跃和脆弱的地段,快速而准确地监测海岸线的动态变化对于海域的使用管理具有十分重要的意义.遥感技术具有宏观、快速、综合、高频、动态和低成本等突出优势.重点介绍了利用阈值分割、边缘检测、色差算子提取、区域生长提取及神经网络分类等方法自动提取瞬时水边线,通过潮位校正进而提取海岸线的研究进展,分析了各种提取方法的优缺点,并就其存在的不足展望了今后的研究方向.     

具有逆辨识结构的模糊神经网络控制及其应用

该文提出一种新型模糊神经网络结构及算法。在这种控制方案中,采用三层模糊神经网络控制器和神经网络逆辨识控制器相结合的结构。计算机仿真研究和实际应用表明,采用新型模糊神经网络控制方法,对大滞后非线性系统的控制是有效的。     

改进型BP神经网络对民勤绿洲地下水位的模拟预测

以具有代表性的民勤绿洲为研究对象,以Matlab7.0为工作平台,对沙漠绿洲地下水埋深预测的三层前馈神经网络（BP神经网络）进行了改进。输入端因子选取民勤绿洲逐月灌溉量、红崖山水库下泄水量、月降水量、月蒸发量（20 cm）、月平均气温、时间序列6项,输出因子为民勤绿洲地下水位。通过在模型的输入层增加时间序列引导因子的方法使BP神经网络对输入端数据具备时间敏感性;通过Levenberg-Marquardt算法使网络误差最小化,并配合Bayesian正则化使网络的误差平方和、网络权重以及阈值平方和实现最优组合,最后使用相关系数、相对误差、效率系数等指标对模型的模拟结果进行检验。结果表明,通过以上一系列改进可以有效提高模型的模拟精度,增强模型的稳定性,并使模型具有良好的“泛化性”。     

地质灾害的非线性数据处理与建模技术

本文简略地介绍了几种地质灾害数据处理与建模的非线性方法,主要包括GMDH自组织建模技术、神经网络方法。GMDH是一种高阶非线性回归建模方法,它是以简单的二元二次回归方程为基础,通过"代复一代"的"生产"过程,客观、自动地求得实际资料的非线性模型。而神经网络则是用工程技术手段模拟生物神经网络的结构特征和功能特征的一类人工系统。与常规统计方法相比,神经网络最突出的优点为它是通过对网络的学习和训练,来掌握变量之间的非线性关系。因此,其处理复杂问题的能力更强大。实例检验效果表明,这些非线性数据处理与建模技术考虑了地质灾害问题的非线性特性,其比基于常规统计理论的数据处理方法的精度要高得多。     

运用单字记忆 减负省时见效

地理教学经常要求学生掌握一定数量的地名知识、地理分布知识、地理景观、地理数据等地理事实材料。这往往容易导致学生死记硬背,甚至失去学习地理的积极性。其实,记忆地理知识的方法有多种,如分解记忆法、联想记忆法、比较记忆法、综合记忆法等。笔者通过多年教学实践,总结并采纳了单字记忆法,并且在初中地理教学中加以实践、实施,     

汉江流域安康站日径流预测的LSTM模型初步研究

论文基于2003—2014年水文资料,采用长短期记忆神经网络(Long-Short Term Memory,LSTM),构建了汉江上游安康站日径流预测模型,评价了不同输入条件下日径流预测的精度。结果表明：当预见期为1 d时,在仅以安康站前期日径流量作为输入的条件下,LSTM模型在训练期和检验期的效率系数分别达到0.68和0.74;如再将流域前期面雨量和上游石泉站前期日径流量加入LSTM网络作为输入变量,安康站日径流量预测效果将更好,训练期和检验期的效率系数最高可达到0.83和0.84,均方根误差也有显著削减,且对主要洪峰流量的预测能力也有一定提高。此外,LSTM可以有效避免过拟合等问题,具有较好的泛化性能。但当预见期从1 d延长至2、3 d时,LSTM的预测精度显著降低。     

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。     

利用联想思维来加强理解记忆

地理教学的课时少，而要记忆的地理概念和地理事象多，如何通过复习强化学生的记忆痕迹，避免遗忘；又无枯燥无味的“抄冷饭”之感，根据教材内容我常采用联想思维来加强学生的理解记忆。     

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.     

陇中黄土丘陵沟壑区人工草地土壤水蚀预测模型

针对陇中黄土丘陵沟壑区土壤水蚀过程复杂且难以有效预测的问题，以定西市安家沟水土保持试验站2005—2016年1~12月人工草地径流场试验数据为主要来源，将流域月降雨量、月侵蚀性降雨量、月径流量、月降雨强度、径流场面积、径流场坡度、土壤砂粒含量、土壤粘粒含量8个因子作为输入因子，月土壤水蚀量作为输出，运用偏最小二乘法(Partial Least-Squares Regression,PLSR)和长短期记忆(Long Short-Term Memory,LSTM)循环神经网络建立人工草地土壤水蚀预测模型，并利用BP(Back Propagation)、RNN(Recurrent Neural Network)、LSTM常见神经网络模型，对模型的有效性进行评估。结果表明：PLSR将模型8个输入因子减少为4个，从而有效解决LSTM神经网络模型对样本数量要求过高的问题； PLSR和LSTM神经网络模型的结合可以有效提高模型对人工草地土壤水蚀过程的预测精度和收敛速度，预测结果的平均相对误差小于4%，相关系数高于其他3种神经网络模型，而迭代次数、均方根误差和平均绝对误差均低于其他3种模型；研究发现坡度对人工草地土壤水蚀过程影响较为明显，降雨量小于25 mm时，人工草地土壤水蚀量不会随坡度增加而明显增长，但当降雨量超过25 mm时，人工草地土壤水蚀量会随坡度明显增加。 PLSR LSTM神经网络土壤水蚀预测模型可以准确预测陇中黄土丘陵沟壑区人工草地土壤水蚀量，为该地区水土流失的准确预报提供新的思路和方法。     

中国机场体系的空间格局及其服务水平

利用定量模型和GIS方法,从空间布局、服务范围以及航空客流分布等方面来研究中国的机场体系及其服务水平。中国机场存在空间布局不均衡和等级结构不合理等问题,各机场的服务水平空间表现不一致,整体服务格局与全国社会经济发展的格局基本协调。航空客流趋向东部沿海地区集聚,空间联系和拓展具有明显的层级性,具有一定的轴-辐式网络特征。整个机场体系表现为以“京沪穗”为核心的“鼎形”空间系统,并将在未来一段时间内得以维持。研究表明,中国机场体系的结构与全国或区域城市体系结构有一定的相互联系,随着航空运输需求的快速发展,未来机场体系的建设既要注意平衡机场区域布局,又需重视优化网络与等级体系,从而合理引导航空网络结构的演变。     

Application of a Modular Feedforward Neural Network for Grade Estimation

This article presents new neural network (NN) architecture to improve its ability for grade estimation. The main aim of this study is to use a specific NN which has a simpler architecture and consequently achieve a better solution. Most of the commonly used NNs have a fully established connection among their nodes, which necessitates a multivariable objective function to be optimized. Therefore, the more the number of variables in the objective function, the more the complexity of the NN. This leads the NN to trap in local minima. In this study, a new NN, in which the connections based on the final performance are eliminated, is used. Toward this aim, several network architectures were tested, and finally a network which yielded the minimum error was selected. This selected network has low complexity and connection among nodes which help the learning algorithm to converge rapidly and more accurately. Furthermore, this network has this ability to deal with the small number of data sets. For testing and evaluating this new method, a case study of an iron deposit was performed. Also, to compare the obtained results, some common techniques for grade estimation, e.g., geostatistics and multilayer perceptron (MLP) were used. According to the obtained results, this new NN architecture shows a better performance for grade estimation.     

Comparing Human and Neural Network Learning of Climate Categories

This study compares how humans and neural networks classify climate types. Human subjects were asked to classify climates from monthly temperature and precipitation patterns. To model their learning process, the same data were used to produce input vectors that trained a pattern associator neural network. Both human subjects and the neural network classified climates accurately after 10 rounds of supervised learning. The neural network successfully modeled the rate of human learning and the ability to learn specific climate categories. Moreover, the neural network weights used to classify climates correspond to distinct visual characteristics in temperature and precipitation. These results suggest that neural networks can model the formation of visual categories.     

最优子集神经网络高温预报模型

 利用2003-2007年6～9月ECMWF格点场资料,使用差分法、天气诊断、因子组合等方法构造出能反映本地天气动力学特征的预报因子库,采用press准则初选因子,尝试用最优子集方法进行神经网络夏季6～9月≥35℃高温预报模型的建模方法研究。2008年7月预报系统投入业务应用,检验证明所构造的神经网络高温预报模型具有更好的拟合和预报效果,为神经网络在灾害性天气预报的应用研究提供了新的思路和方法。