基于小波变换与神经网络的石羊河流域夏季地温预测模型研究   总被引：1，自引：1，他引：0  

贾东于  李开明  聂晓英  袁春霞  李清峰  高福元 《冰川冻土》2020,42(2):412-422

地温变化在气候反馈效应中起着重要作用， 理解地温及其与影响因素之间的时空关系对预测全球温度变化至关重要。利用1998 - 2017年石羊河流域的逐日常规气象观测资料， 采用小波分析结合BP（Back Propagation）神经网络构建了石羊河流域夏季地温预报模型， 结果表明： 日平均地温预测效果在不同站点均为最佳， 其中预测值和观测值的相关系数均大于0.87， 3 ℃以内的预测概率均大于84%。其中， 民勤地区地温预测效果最好， 预测值和观测值的相关系数达到0.91， 3 ℃以内的预测概率达到86%。日最高地温的预测值与观测值的相关系数高于0.8， 但误差平方和、 标准差较大。永昌地区日最高地温的模拟效果最好， 3 ℃以内的预测概率达到83%。日最低地温的预测与观测值的平均相关系数高于0.66， 3 ℃以内的预报概率高于83%， 但预测值略低。其中， 武威地区日最低地温的预测效果最好， 预测值与观测值的相关系数为0.72， 3 ℃以内的预测概率达到94%。研究成果可为有效弥补干旱、 半干旱区地温观测资料缺失和探讨其与局地气候的关系提供一些参考。  相似文献   

基于U形卷积神经网络的震相识别与到时拾取方法研究   总被引：8，自引：0，他引：8       下载免费PDF全文

赵明  陈石  房立华  David A Yuen 《地球物理学报》2019,62(8):3034-3042

精确获取震相到时是地震定位和地震走时成像等研究的重要基础.近年来，随着地震台站的不断加密，地震台网监测到的地震数量成倍增长，发展快速、准确、适用性强的震相到时自动拾取算法是地震行业的迫切需求.本文在前人工作基础上，发展了Pg、Sg震相自动识别与到时拾取的U网络算法（Unet_cea），使用汶川余震和首都圈地震台网记录的89344个不同震级、不同信噪比的样本进行训练和测试.研究表明，U网络能够较好地识别Pg、Sg震相类型和拾取到时，Pg、Sg震相的正确识别率分别为81%和79.1%，与人工标注到时的均方根误差分别为0.41 s和0.54 s.U网络在命中率、均方根误差等性能指标上均明显优于STA/LTA和峰度分析自动拾取方法.研究获得的最优模型可以为区域地震台网的自动处理提供辅助.  相似文献   

基于卷积神经网络与条件随机场方法提取乡镇非正规固体废弃物     

刘懿兰  黄晓霞  李红旮  柳泽  陈崇  王新歌 《地球信息科学学报》2019,21(2):259-268

随着村镇经济建设发展,生活垃圾和工业固体废弃物造成的污染问题日益突出,已经成为制约新农村建设发展和生态文明建设的关键问题,而目前针对乡镇非正规固体废弃物的调查与统计主要依赖全国各乡镇相关部门逐级调查上报,工作量较大。本文基于高分辨率遥感影像,将深度学习模型和条件随机场模型相结合引入到乡镇固体废弃物的提取研究中,探索一种基于深度卷积神经网络的乡镇固体废弃物提取模型。由于固体废弃物在影像上表现为面积小,分布破碎等特点,为了提高工作效率,将模型特分为识别和提取2个部分：① 通过全连接卷积网络（CNN）对固体废弃物进行快速识别判断,筛选感兴趣区域影像块;② 在传统的全卷积神经网络(FCN)的基础上加入条件随机场模型(CRF)提取固体废弃物边界,提高整体分割精度。根据安徽、山西等地区相关部门上报固体废弃物堆放点以及住房与城乡建设部城乡规划管理中心进行野外检查的结果,实验最终识别精度达到86.87%以上;形状提取精度为89.84%,Kappa系数为0.7851,识别与提取精度均优于传统分类方法。同时,该方法已经逐步应用于住房和城乡建设部有关成都、兰州、河北等部分乡镇非正规固体废弃物的核查工作,取得了较为满意的结果。  相似文献   

经济地理学视角下创新网络与区域增长研究述评     

曹贤忠  曾刚 《热带地理》2019,39(3):472-478

创新是引领经济发展的第一动力，创新与区域增长之间的关系成为经济地理学者关注的重点领域。文章通过梳理近年来有关创新网络测度、创新网络与区域增长关系、创新网络作用于区域增长方式等方面的文献发现：网络资本可以弥补社会资本在解释企业创新结网经济价值方面的不足，区域增长呈现出网络化特征已成为学界共识，知识流与邻近性能较好地解释创新网络与区域增长的关系机理。然而，当前研究对社会资本如何促进区域增长，网络资本与区域增长关系模型如何构建，不同类型的邻近性与知识对区域增长的影响有何差异等问题尚不明确，建议重视网络资本对区域增长的作用并实证检验二者的关联，同时还应重视创新网络中企业家精神、创新个体心理行为特征等因素对区域增长的影响。  相似文献   

基于改进的BP神经网络构建区域精密对流层延迟模型   总被引：4，自引：0，他引：4       下载免费PDF全文

肖恭伟  欧吉坤  刘国林  张红星 《地球物理学报》2018,61(8):3139-3148

利用神经网络算法挖掘海量数据的规律已成为科技发展的一种趋势，本文针对卫星信号的天顶对流层延迟进行建模.对流层延迟是影响卫星定位精度的重要因素之一，建立精密区域对流层模型对高精度定位有着重要的意义.对区域测站对流层延迟数据的分析，考虑到实时建模中传统BP（Back Propagation）神经网络计算量大，易出现"过拟合"现象、不稳定等因素，通过改进的BP神经网络建立了区域精密对流层模型.详细介绍了新模型的建立过程，并与常用的对流层区域实时模型进行了对比.还讨论了建模测站数目对预报精度的影响.相比现有的其他对流层延迟模型，基于改进的BP神经网络构建的区域精密对流层延迟模型无论在拟合和预报方面都有较好的精度，且随着测站数目的增加模型精度趋于平稳.改进的模型参数较少，可以进行实时的区域精密对流层延迟改正；需要播发的信息量小，适用于连续运行参考站系统（Continuously Operating Reference Stations，CORS）的应用.研究表明：改进的BP神经网络模型能够更好的充分利用大规模历史数据描述卫星信号对流层延迟的空间分布情况，适用于实时大区域精密对流层建模.基于日本地区2005年近1000多个测站的NCAR（National Center Atmospheric Research）对流层数据进行区域对流层延迟建模，结果表明改进的BP神经网络模型在拟合和预报精度上都有较大提升，RMSE（Root Mean Square Error）分别为：7.83 mm和8.52 mm，而四参数模型拟合、预报RMSE分别18.03 mm和16.60 mm.  相似文献   

Detecting regional dominant movement patterns in trajectory data with a convolutional neural network     

Can Yang  Győző Gidófalvi 《International journal of geographical information science》2020,34(5):996-1021

  相似文献   

MARC: a robust method for multiple-aspect trajectory classification via space,time, and semantic embeddings     

Lucas May Petry  Camila Leite Da Silva  Andrea Esuli  Chiara Renso  Vania Bogorny 《International journal of geographical information science》2020,34(7):1428-1450

ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.  相似文献   

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification,visual interpretation and machine learning approaches     

《International Journal of Applied Earth Observation and Geoinformation》2020

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected.  相似文献   

基于深度学习理论的无人机影像分类     

阳成 《北京测绘》2020,(4):481-484

针对无人机影像深度学习分类方法缺乏现状,本文利用深度学习理论卷积神经网络方法对无人机影像进行了分类。该法首先抽取无人机影像作为训练集和检验集,然后建立一个2个卷积层-池化层的卷积神经网络模型进行深度学习,通过设定参数并运行模型实现无人机影像分类。实验表明,本文提出的方法可完成较复杂地区无人机影像分类,其分类精度与支持向量机方法相当,为无人机遥感影像分类提供了一个崭新的技术视点。  相似文献   

Application of open tools and datasets to probabilistic modeling of road traffic disruptions due to earthquake damage     

Catarina Costa  Rui Figueiredo  Vitor Silva  Paolo Bazzurro 《地震工程与结构动力学》2020,49(12):1236-1255

The disruption of a transportation network can have a high social and economic impact on the welfare of a society, as it can significantly affect the daily routines of a community. Although many studies have focused on the estimation of physical risk in the components that compose these networks, only a limited number have analyzed their interconnections and impact in the traffic flow. The present study analyzes how earthquake damage can disrupt the road network in an urban environment, and how this will influence the ability of the population to travel. Traffic due to daily commutes is modeled for different layouts of the network, corresponding to possible disruptions caused by earthquake damage. The duration and length of each trip were calculated both for the undamaged network conditions and for the disrupted network. The increase in the median duration and length of each trip allows estimating the economic loss for each event due to drivers' delay. By combining the probability of a specific road being blocked with its number of users, the average number of affected vehicles was estimated, and the most critical segments identified. The methodology was applied to a case study concerning the road network of the area around the Italian city of Messina in Sicily. The results were calculated for both a repetition of the well-known historical event of 1908 and a set of simulated earthquakes consistent with the national probabilistic seismic hazard model of Italy.  相似文献