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   

基于主成分分析法和监测数据的阿尔泰山采金废弃矿区人工恢复措施效益分析          下载免费PDF全文

艾沙江·艾力  徐海量  苑塏烨 《干旱区地理》2019,42(2):288-294

本文以阿尔泰山两河源保护区采金废弃矿区为研究区，从地形、土壤、水分和地表植被4个方面出发，结合矿区现有条件，设置11种恢复措施，选取丰富度指数、优势度指数、多样性指数、均匀度指数、生物量、植被盖度、物种数及土石比等7个评价指标，采用主成分分析法，得出不同恢复措施的主成分得分及生态效益排名。结果表明，单一恢复措施，如推平、羊群驻扎、泥浆等基本上是从某一个方面来考虑生态恢复的，存在一定的缺陷，恢复效益排名比较靠后；多种措施相结合，不仅能改善土壤环境，也会引起植物群落多样性格局的变化，生态恢复效益很好；同一种恢复措施，施行年限越长恢复效果越好。  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   

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   

Artificial recharge efficiency assessment by soil water balance and modelling approaches in a multi-layered vadose zone in a dry region     

Mojtaba Pakparvar  Hossein Hashemi  Meisam Rezaei  Wim M. Cornelis  Gholamali Nekooeian  Sayyed Ahang Kowsar 《水文科学杂志》2018,63(8):1183-1202

To assess recharge through floodwater spreading, three wells, approx. 30 m deep, were dug in a 35-year-old basin in southern Iran. Hydraulic parameters of the layers were measured. One well was equipped with pre-calibrated time domain reflectometry (TDR) sensors. The soil moisture was measured continuously before and after events. Rainfall, ponding depth and the duration of the flooding events were also measured. Recharge was assessed by the soil water balance method, and by calibrated (inverse solution) HYDRUS-1D. The results show that the 15 wetting front was interrupted at a layer with fine soil accumulation over a coarse layer at the depth of approx. 4 m. This seemed to occur due to fingering flow. Estimation of recharge by the soil water balance and modelling approaches showed a downward water flux of 55 and 57% of impounded floodwater, respectively.  相似文献   

Coordinative similitude law considering fluid‐structure interaction for underwater shaking table tests     

Zhong‐Xian Li  Kun Wu  Yundong Shi  Ning Li  Yang Ding 《地震工程与结构动力学》2018,47(11):2315-2332

Generally, when a model is made of the same material as the prototype in shaking table tests, the equivalent material density of the scaled model is greater than that of the prototype because mass is added to the model to satisfy similitude criteria. When the water environment is modeled in underwater shaking table tests, however, it is difficult to change the density of water. The differences in the density similitude ratios of specimen materials and water can affect the similitude ratios of the hydrodynamic and wave forces with those of other forces. To solve this problem, a coordinative similitude law is proposed for underwater shaking table tests by adjusting the width of the upstream face of the model or the wave height in the model test to match the similitude ratios of hydrodynamic and wave forces with those of other forces. The designs of the similitude relations were investigated for earthquake excitation, wave excitation, and combined earthquake and wave excitation conditions. Series of numerical simulations and underwater shaking table tests were performed to validate the proposed coordinative similitude law through a comparison of coordinative model and conventional model designed based on the coordinative similitude law and traditional artificial mass simulation, respectively. The results show that the relative error was less than 10% for the coordinative model, whereas it reached 80% for the conventional model. The coordinative similitude law can better reproduce the dynamic responses of the prototype, and thus, this similitude law can be used in underwater shaking table tests.  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

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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   

茅山竹矿断层气定点观测场地勘选     

戴波  吴晓峰  张敏  孟科 《大地测量与地球动力学》2019,39(11):1202-1206

拟在茅山断裂带沿线布设若干个断层气连续观测点，建组断层气观测网。以茅山竹矿地区为例，通过断层气测量的初步结果，结合地质、人工地震、跨断层短水准等多种手段联合分析研究区断层的活动性，从而确定断层气定点观测的点位，为组建断层气观测网的选点提供借鉴和参考。  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