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61.
Winter conditions play an important role for the largest lake in Europe—Lake Ladoga. The ice cover lasts for 171 ± 3 days on average from the early November until the mid‐May. We investigated the ice regime of Lake Ladoga using a constructed ice database of aircraft surveys and satellite images. More than 1250 surveys of the lake's ice cover from 1943 to 2010 were collected and analysed to determine mean and extreme ice conditions for winters of different types of severity. The time series of ice cover percentage over the lake was plotted. On average, 18 observational ice charts were made every winter. Individual ice phenology records show considerable year‐to‐year variation. For this reason, records typically have been combined and analysed as groups (categories). Extremely cold winters were determined as winters with complete ice cover that lasts more than three months which is approximately 90% quartiles from all winters with complete ice cover. The lake surface was completely covered with ice for more than three months during 5 seasons. Extremely warm winters when the maximum ice cover was less than 70% of the lake area occurred during 5 seasons as well. A basic relationship between the winter severity as winter maximum of accumulated freezing degree‐days (AFDD) and the earlier derived Relative Ice Cover Index (RICI) was established. We have used teleconnection indices such as North Atlantic Oscillations (NAO) and Arctic Oscillation (AO) for the period from October to May for estimation of different types of Lake Ladoga's ice conditions. The AO index in winter months and local winter maximum of AFDD explained much of the interannual variation in ice cover. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   
62.
复杂卫星图像中的小目标船舶识别   总被引:1,自引:1,他引:0  
姚红革  王诚  喻钧  白小军  李蔚 《遥感学报》2020,24(2):116-125
船舶作为海上的重要目标,实现对船舶自动识别有重要的意义。针对卫星图像中云雾、海岸背景等复杂海情对船舶识别带来的干扰,以及小目标船舶高漏检率问题,本文提出一种多尺度深度学习模型训练策略,在此基础上构建了一种船舶识别的深度学习网络,该网络可分为多尺度训练、特征提取、生成目标建议区域、船舶分类这4个部分。首先,采用多尺度的训练策略,将多尺度的船舶样本送入网络中进行训练,这样在训练样本中加入了大量小目标船舶的样本,使网络充分提取到小目标船舶的特征;其次,通过卷积神经网络对目标船舶进行特征自适应提取;然后,目标区域建议网络可依据卷积神经网络提取到的特征,在图像中找到感兴趣目标区域,即框定船舶的位置;最后,通过多个全连接层的组合,将高维特征映射到一个4元组中,再运用分类函数输出每一类船舶的概率值,概率值最大的则为该船舶的类别。同时为解决云雾遮挡和海岸背景的干扰,采用了一种负样本增强学习的方法,在样本数据集中加入了大量只含有云雾和海岸背景的图片,进行负样本扩充,增强网络模型对云雾及海岸背景的特征学习能力,以此解决复杂海情的影响。实验结果表明,所提方法有效解决了复杂海情条件下的船舶识别难,以及小目标船舶识别难的问题,实现了复杂海情条件下的船舶识别。同时,与现有成熟的深度学习目标识别算法相比,本文算法的精确度和召回率分别提升了6.98%和18.17%,所训练的模型具有良好的泛化能力和鲁棒性。  相似文献   
63.
水下采油树井口连接器是连接采油树和井口的关键设备,对深海水下勘探开发及采油树的安装连接具有不可或缺的重要意义。以一种锁块式水下连接器为例,介绍其结构组成和工作原理,通过有限元建立连接器的三维模型,分析连接器在下放安装工况、连接锁紧工况、正常生产工况和钻完井工况下的连接性能,得出井口与采油树本体接触面作用力、VX钢圈接触应力和锁块受力情况随外部载荷的相互关系。结果表明,VX钢圈在整个过程中都能够保持良好密封,井口与采油树本体接触面未发生分离,弯矩对连接器连接性能影响很大,在额定弯矩作用时连接器部分锁块已发生弹塑性失效。  相似文献   
64.
气象变量常作为重要的影响因子出现在环境污染、疾病健康和农业等领域,而高分辨率的气象资料可作为众多研究的基础数据,对推进相关研究的发展意义重大。本文以中国大陆为研究区域,利用2015年824个气象站点的气温、相对湿度和风速3套数据,结合不同的解释变量组合,分别构建了各自的GAM和残差自编码器神经网络(简称残差网络)模型,以10倍交叉验证判断模型是否过拟合。研究结果表明:① GAM和残差网络方法都不存在过拟合问题,同GAM相比,残差网络显著提高了模型预测的精度(3个气象因素的交叉验证CV R2平均提高了0.21,CV RMSE平均降低了37%),其中相对湿度模型的提升幅度最大(CV R2:0.85 vs. 0.52,CV RMSE:7.53% vs. 13.59%);② 残差模型的结果较普通克里格插值结果和再分析资料更接近站点观测数据,表明残差网络可作为高分辨率气象数据研制的可靠方法。此外,研究还发现在相对湿度模型中加入臭氧浓度和气温、在风速模型中加入GLDAS风速再分析资料,可提升模型的性能。  相似文献   
65.
在全球气候变化背景下,植被动态变化以及植被对气候变化的响应方式已经成为生态学和地理学领域的热点。本文对比分析了南方亚热带季风区将乐县不同类型森林植被对不同时间尺度的干旱响应的差别。基于2000-2017年MODIS-EVI数据及气象站点数据,用最大值合成法、趋势分析法以及相关分析法,分析了森林植被及气象因子的动态变化特征,并对比不同森林植被对气候变化响应的差别。研究表明:① 2000-2017年,研究区植被覆盖度、EVI和降水均显著增加,区域内湿度增加,森林长势渐趋良好;② EVI在生长季初期和末期与同期的降水、温度均显著正相关(P<0.1),初期森林受降水因子的影响更大,末期受温度因子的影响大;③ 1-3月和周年的气候变化对森林的生长至关重要,长时间尺度的湿度增加对森林生长具有显著的促进作用,SPEI的时间尺度越长与EVI的相关性也越大;④ 针阔混交林与同期温度、降水的相关系数最高,并且与不同时间尺度的SPEI相关性均比较高,属于气候敏感型林型,在生产经营中要谨慎预防气候变化对该林型带来的伤害;⑤ 森林覆盖度变化与降水和SPEI_24的相关性极显著,长时间尺度的降水变化是影响森林植被覆盖率变化的重要因素之一。  相似文献   
66.
This paper uses Biot's poroelasticity approach to examine the consolidation behaviour of a rigid foundation with a frictionless base in contact with a poroelastic halfspace. The mathematical development of the mixed boundary value problem involves a set of dual integral equations in the Laplace transform domain which cannot be conveniently solved by employing conventional procedures. In this paper, a numerical solution is developed using a scheme where the contact normal stress is approximated by a discretized equivalent. The influence of limiting drainage boundary conditions at the entire surface of the halfspace on the degree of consolidation of the rigid circular foundation is investigated. The results obtained in this study are compared with the corresponding results given in the literature. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   
67.
The complexity of formulations for the hydromechanical coupled mechanics of porous media is typically minimised by simplifying assumptions such as neglecting the effect of inertia terms. For example, three formulations commonly employed to model practical problems are classified as fully dynamic, simplified dynamic and quasi‐static. Thus, depending on the porous media conditions, each formulation will have advantages and limitations. This paper presents a comprehensive analysis of these limitations when solving one‐dimensional fully saturated porous media problems in addition to a new solution that considers a more general loading situation. A phase diagram is developed to assist on the selection of which formulation is more appropriate and convenient regarding particular cases of porosity and hydraulic conductivity values. Non‐dimensional formulations are proposed to achieve this goal. Results using the analytical solutions are compared against numerical values obtained with the finite element method, and the effect of porosity is investigated. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   
68.
国内外岩溶塌陷监测方法综述   总被引:3,自引:0,他引:3       下载免费PDF全文
岩溶塌陷是全球广泛分布的地质灾害问题,严重影响和制约了岩溶地区社会、经济发展,因此,开展岩溶塌陷监测工作,对塌陷预测预报具有及其重要的作用。岩溶塌陷的监测技术与塌陷形成的机理关系密切。该文分析了岩溶塌陷的分布规律和形成的条件,系统总结了国内外岩溶塌陷的监测内容、技术方法、各自优缺点,如BOTDR监测、TDR监测、地质雷达监测、水动力条件监测及地-沉降等方法,为系统性地开展岩溶塌陷监测工作提供技术和方法参考,展望了未来的岩溶塌陷监测工作,认为岩溶塌陷监测需结合塌陷的形成机理、塌陷地区覆盖层特征及水动力条件,综合利用多种监测手段联合进行,并形成完善的监测网络。  相似文献   
69.
In high elevation cold regions of the Tibetan Plateau, suspended sediment transfer from glacier meltwater erosion is one of the important hydrological components. The Zhadang glacier is a typical valley‐type glacier in the Nyainqentanglha Mountains on the Tibetan Plateau. To make frequent and long period records of meltwater runoff and sediment processes in the very high elevation and isolated regions, an automatic system was installed near the glacier snout (5400 m a.s.l) in August 2013, to measure the transient discharge and sediment processes at 5‐min interval, which is shorter than the time span for the water flow to traverse the catchment from the farthest end to the watershed outlet. Diurnal variations of discharge, and suspended sediment concentration (SSC) were recorded at high frequency for the Zhadang glacier, before suspended sediment load (SSL) was computed. Hourly SSC varied from the range of 0.2 kg/m3 to 0.5 kg/m3 (at 8:00–9:00) to the range of 2.0 kg/m3 to 4.0 kg/m3 (at 17:00–18:00). The daily SSL was 32.24 t during the intense ablation period. Hourly SSC was linearly correlated with discharge (r = 0.885**, n = 18, p < 0.01). A digit‐eight hysteresis loop was observed for the sediment transport in the glacier area. Air temperature fluctuations influence discharge, and then result in the sediment variations. The results of this study provide insight into the responses of suspended sediment delivery processes with a high frequency data in the high elevation cold regions. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   
70.
Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.  相似文献   
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