基于卷积神经网络的极光图像分类

极光是由带电粒子经磁层—电离层碰撞大气而产生的。面对形态各异、演变过程复杂的极光图像,对其合理分类为进一步探究日地电磁活动和能量耦合等空间物理问题奠定了基础。针对该问题,引入深度学习的方法,通过卷积神经网络模型自主表征极光特征并实现极光图像分类。该方法对2003年北极黄河站越冬观测的38 044幅和8 001幅典型极光图像分类正确率达93.17%和91.5%;自动识别2004—2009年观测数据的极光形态,4类极光时间分布规律与三波段激发谱能量分布基本一致。实验结果表明,基于卷积神经网络的极光表征方法,能有效实现极光图像的自动分类。     

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。     

基于GIS与SOFM网络的中国综合自然区划

综合自然区划一直是中国地理学界的研究核心和热点之一,已有大量的区划方案应用于指导社会生产实践或教学活动中。已有的区划工作主要是基于传统地域划分研究范式,大多采用专家经验集成方法和技术。专家经验与知识的主观性和个体差异性会对区划方案的科学性和客观性产生影响。为了弥补传统区划范式的不足,丰富区划的方法与途径,本文探讨了自组...     

Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata

ABSTRACT

Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the exploration of the transition suitability of cells, leading to lower simulation accuracy. This study proposes a convolutional neural network (CNN)-VCA model that adopts the CNN to extract the high-level features of the driving factors within a neighborhood of an irregularly shaped cell and discover the relationships between multiple land use changes and driving factors at the neighborhood level. The proposed model was applied to simulate urban land use changes in Shenzhen, China. Compared with several VCA models using other machine learning methods, the proposed CNN-VCA model obtained the highest simulation accuracy (figure-of-merit = 0.361). The results indicated that the CNN-VCA model can effectively uncover the neighborhood effects of multiple driving factors on the developmental potential of land parcels and obtain more details on the morphological characteristics of land parcels. Moreover, the land use patterns of 2020 and 2025 under an ecological control strategy were simulated to provide decision support for urban planning.     

U-Net深度卷积神经网络在沙脊线提取中的应用

沙丘形态演变过程记录着近地表风况与环境演化的历史,然而对其特征研究一直受限于大范围沙脊线提取效率低和成本高等问题.本文基于深度卷积神经网络搭建U-Net模型,实现批量、高精度沙脊线的提取.将数据增强技术、随机失活神经元、批标准化以及迁移学习技术应用于模型训练和参数更新,使得模型的精度更高.结果 表明:U-Net模型以及...     

RecNet: a deep neural network for personalized POI recommendation in location-based social networks

How to exploit various features of users and points of interest (POIs) for accurate POI recommendation is important in location-based social networks (LBSNs). In this paper, a novel POI recommendation framework, named RecNet, is proposed, which is developed based on a deep neural network (DNN) to incorporate various features in LBSNs and learn their joint influence on user behavior. More specifically, co-visiting, geographical and categorical influences in LBSNs are exploited to alleviate the data sparsity issue in POI recommendation and are converted to feature vector representations of POIs and users via feature embedding. Moreover, the embedded POIs and users are fed into a DNN pairwise to adaptively learn high-order interactions between features. Our method is evaluated on two publicly available LBSNs datasets and experimental results show that RecNet outperforms state-of-the-art algorithms for POI recommendation.     

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SOFM网络的京津冀地区生态系统服务分区

生态系统服务与土地利用之间有密切关联,生态系统服务分区对区域生态系统服务的管理和土地利用政策的制定有重要意义。本文以京津冀地区为研究区,依据IGBP2001-2009年间的土地利用数据,在对谢高地等制定的中国陆地生态系统单位面积服务价值系数进行校正的基础上,核算了区域内各县(市)级行政单元单位面积生态系统服务的价值量,构建自组织特征映射网络(SOFM)对京津冀的生态系统服务进行分区,并用ArcGIS识别了不同服务类型的热点区,归纳和总结了每个分区的主导服务类型,并结合全国功能主体区划对该区域未来的发展重点和土地利用政策提出建议。依据分类结果,可将京津冀地区分为4 个区域:Ⅰ. 坝上高原和冀西北山区;Ⅱ. 燕山和太行山地;Ⅲ. 冀中南平原区;Ⅳ. 环渤海滨海区。2001-2009年间,除Ⅳ区的生态系统服务价值呈现增加趋势外,其他区域均有不同程度的减少,减少程度依次为Ⅱ>Ⅰ>Ⅲ。Ⅰ区域发展重点为防风固沙和水源涵养;Ⅱ区域为生物多样性重点保护区域;Ⅲ区域应重点调整城镇用地的比例,适当增加其他生态系统服务;Ⅳ区域应增加水源涵养价值,治理土壤盐渍化。     

利用LM-BP神经网络估算西北地区太阳辐射

气候、生态、水文等模型的应用需要空间连续分布的太阳辐射数据,由于地形等条件的制约,气象站点的分布有限,无法利用稀少的站点获得空间连续分布的辐射数据,而BP(Back-propagation)神经网络模型对太阳辐射具有很好的预测性,但以往的研究都是基于单个站点估算太阳辐射,而且BP神经网络模型存在收敛速度慢、学习时间长等问题,为解决BP算法存在的不足,采用LM(Levenberg-Marquardt)算法优化后的BP神经网络(简称LM-BP神经网络)结合DEM(Digital Elevation Model)数据估算西北地区128个气象站点2011年的太阳总辐射月均值,通过乌鲁木齐和银川两台站的实测数据进行验证,两台站的平均百分比误差分别为2.89%和3.24%,平均偏离误差分别为0.27 MJ·m-2和0.61 MJ·m-2,且拟合优度均0.90。该模型各项误差指标较小,估算精度较高。最后将模型模拟出的辐射值,结合已有的24个辐射站点的实测值进行空间插值,得到西北地区2011年逐月太阳辐射精细化空间分布图。     

BP神经网络和SVM在矿山环境评价中的应用分析

矿山环境的影响因素多样,定量评价过程易受人为因素干预。BP神经网络与SVM算法能够自动模拟各因子间的非线性关系。首次将其引入到矿山环境评价中,选取160个单元作为训练样本,以自然地理、基础地质、开发占地及地质环境等4个大类的14个变量指标为输入向量,以单元评价得分为输出向量,分别建立BP神经网络与SVM矿山环境评价模型。结果表明：两种模型均能满足矿山环境评价的精度要求;SVM模型收敛速度较BP神经网络快,MSE小于BP神经网络,更适合矿山环境评价工作;将定量模型应用于研究区,评价得分划分为4个级别,与定性评价结果一致,为矿山环境评价工作提供了新思路。     

Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network

Point cloud classification plays a critical role in many applications of airborne light detection and ranging (LiDAR) data. In this paper, we present a deep feature-based method for accurately classifying multiple ground objects from airborne LiDAR point clouds. With several selected attributes of LiDAR point clouds, our method first creates a group of multi-scale contextual images for each point in the data using interpolation. Taking the contextual images as inputs, a multi-scale convolutional neural network (MCNN) is then designed and trained to learn the deep features of LiDAR points across various scales. A softmax regression classifier (SRC) is finally employed to generate classification results of the data with a combination of the deep features learned from various scales. Compared with most of traditional classification methods, which often require users to manually define a group of complex discriminant rules or extract a set of classification features, the proposed method has the ability to automatically learn the deep features and generate more accurate classification results. The performance of our method is evaluated qualitatively and quantitatively using the International Society for Photogrammetry and Remote Sensing benchmark dataset, and the experimental results indicate that our method can effectively distinguish eight types of ground objects, including low vegetation, impervious surface, car, fence/hedge, roof, facade, shrub and tree, and achieves a higher accuracy than other existing methods.     

Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity

ABSTRACT

Geographically weighted regression (GWR) is a classic and widely used approach to model spatial non-stationarity. However, the approach makes no precise expressions of its weighting kernels and is insufficient to estimate complex geographical processes. To resolve these problems, we proposed a geographically neural network weighted regression (GNNWR) model that combines ordinary least squares (OLS) and neural networks to estimate spatial non-stationarity based on a concept similar to GWR. Specifically, we designed a spatially weighted neural network (SWNN) to represent the nonstationary weight matrix in GNNWR and developed two case studies to examine the effectiveness of GNNWR. The first case used simulated datasets, and the second case, environmental observations from the coastal areas of Zhejiang. The results showed that GNNWR achieved better fitting accuracy and more adequate prediction than OLS and GWR. In addition, GNNWR is applicable to addressing spatial non-stationarity in various domains with complex geographical processes.     

Three-dimensional distribution analysis of phosphorus content of limestone through a combination of geostatistics and artificial neural network

One of the factors that determines the suitability of limestone for industrial use and its commercial value is phosphorus (P) content, i.e., the weight percentage of phosphorus contained in small quantities of limestone. Because P content changes locally, geostatistical techniques including semivariogram, ordinary kriging, and conditional indicator sequential simulation were used in this study to identify the spatial correlation of P content and to estimate its three-dimensional distribution in an open-pit mine. The P content data at 43,000 points of five different bench levels were analyzed. It was found that the horizontal semivariograms produced by using the data at the same bench level show anisotropic behavior and are represented by the sum of two spherical models with different ranges and sills. The twelve vertical semivariograms were also constructed from P content in boring cores. After these semivariograms were classified into four types, a multilayered neural network was applied to clarify the horizontal distribution of each one. One of the twelve semivariograms was assigned to an arbitrary grid point in the study area by the criterion that its type is the same as the one estimated at the point and the borehole site producing the semivariogram is the nearest to the point. With this technique, ordinary kriging combined with the semivariogram of borehole data provided a proper estimation of P content in the depth direction.     

北京城市代谢预测研究——基于长短期记忆神经网络模型

城市化进程提升促使城市环境污染加剧、能源消耗激增、人口密度过大等问题的深层次原因在于城市代谢失调。为精准预测北京市城市代谢变化趋势,论文通过能源消费量及人类活动时间指标测算了1980—2016年北京市体外能代谢率,表征城市代谢程度。据此运用长短期记忆神经网络模型(LSTM)预测了2017—2022年北京各部门体外能代谢率。结果表明：① 基于长短期记忆神经网络的城市代谢预测模型精度较高,能够对北京各部门体外能代谢率进行更为精准的预测;② 2017—2022年间,北京第一产业和总体外能代谢率呈下降趋势,其中第一产业在2017年达到峰值,第二、第三产业及生活部门体外能代谢率将呈现增长趋势。③ 除第一、第三产业和总体外能代谢率外,历史变化的时间扰动幅度先小后大。④ 对各部门体外能代谢率EMR_T的影响贡献度最大的因子为第二产业体外能代谢率EMR₂,最小的为第一产业体外能代谢率EMR₁。论文研究结果可为政策制定者优化城市管理方案、提升城市综合实力提供理论依据和决策支持。     

基于主成分分析土壤水分扩散率单一参数模型的BP神经网络模型

应用水平土柱法测定了杨凌地区典型粘壤土的水分扩散率,利用土壤水分扩散率的单对数模型和双对数模型对其进行了拟合,建立了土壤水分扩散率单一参数模型,基于主成分分析建立了单一参数模型中参数B的BP神经网络模型。结果表明：利用主成分分析可将研究区域土壤容重、有机质含量、粘粒含量、粗粉粒含量和砂粒含量综合成3个主成分;基于主成分分析建立的BP神经网络模型拟合的单一参数模型参数[B]的均方根误差RMSE为0.308 2;将拟合得到的参数B代入单一参数模型中对土壤水分扩散率进行预测,除去其中较大值的预测结果偏低外,其余土壤水分扩散率预测结果都比较接近实测值,预测结果的均方根误差RMSE为0.257 8,可利用基于主成分分析建立的BP神经网络模型预测单一参数模型中的参数B。