耦合GF-2遥感影像与街景影像的广州市城中村识别

及时准确地获取城中村的空间分布及其环境质量信息对于优化城市空间、改善人居环境具有重要意义。本文以广州市越秀区为例,提出了耦合GF-2高分遥感影像和百度街景影像的城中村识别方法。首先,从街景影像中提取越秀区的街道空间品质特征;其次,在对高分遥感影像预处理并进行多尺度分割的基础上计算光谱、形状、纹理、场景特征和建筑结构5类共计23个特征;最后,融合两种影像的特征用于构建随机森林分类器进行城中村识别。结果表明,基于高分影像和基于街景影像的城中村识别整体精度分别为94.5%和85.7%,Kappa系数分别为0.58和0.31,而两者融合后的分类精度和Kappa系数为96.1%和0.67;其中基于街景影像获取的度量街道空间品质的5个指标贡献了31.6%的特征重要性。鸟瞰视野高分影像和人本视角街景影像提供的信息综合互补,构建了更有区分度的特征空间,减少了城中村的错分现象。本文证实了高分影像和街景影像在特征尺度的融合提升了城中村识别精度。街景影像中的信息可以融入到高分遥感影像等数据源中,辅助进行城中村等非正规居住空间的识别。     

Late Holocene monsoon climate as evidenced by proxy records from a lacustrine sediment sequence in western Guangdong,South China

The study of a 300-cm-thick exposed lacustrine sediment section in the Hedong village in Zhaoqing area which is located in sub-tropical west Guangdong Province in South China, demonstrates that the lacustrine sedimentary sequence possibly contains evidence for exploring variation of Asian monsoon climate. Multi-proxy records, including the humification intensity, total organic carbon, and grain size fractions, reveal a general trend towards dry and cold conditions in the late Holocene that this is because of a decrease in solar insolation on an orbital scale. Three intensified Asian summer monsoon (ASM) intervals (∼3300–3000 cal yr BP, ∼2600–1600 cal yr BP, and ∼900–600 cal yr BP), and three weakened ASM intervals (∼4000–3300 cal yr BP, ∼3000–2600 cal yr BP, and ∼1600–900 cal yr BP) are identified. Our humification record (HD_cal) shows a good correlation on multi-centennial scale with the tree ring Δ¹⁴C record, a proxy of solar activity. A spectral analysis of HD_cal reveals four significant cycles, i.e., ∼1250 yr, 300 yr, 110 yr, and 70 yr, and most of these cycles are related to the solar activity. Our findings indicate that solar output and oceanic–atmospheric circulation probably have influenced the late Holocene climate variability in the study region.     

多种风电场风速预报订正方法的适用性研究与集成应用

以内蒙古中部某风电场为实验风电场,采用随机森林（Random forest,RF）方法、相似误差订正（Analogue correction of errors,ACE）方法以及概率密度匹配方法（Probability density function matching method,PDF）分别对风电场风速预报进行订正及适用性研究。结果表明：3种方法在各季均对中尺度天气预报模式（Weather research and forecasting model, WRF）风速预报具有不同程度的订正效果,RF方法可以有效改善WRF误差较大的问题,但兼具误差过分放大情况,ACE方法和PDF虽然对较大误差的改善能力不及RF方法,但是能够较好地控制误差过分放大问题。此外,3种方法针对小于5 m·s^-1的小风速段,订正效果不理想,随着风速的增加,订正能力逐渐增强。参照预报模型各自的优势,尝试开展多种预报模型的分风速等级集成应用,可以对不同风速等级下的WRF预报起到较好的改善作用。     

Bayesian modelling the retreat of the Irish Sea Ice Stream

We present an 8000‐year history spanning 650 km of ice margin retreat for the largest marine‐terminating ice stream draining the former British–Irish Ice Sheet. Bayesian modelling of the geochronological data shows the ISIS expanded 34.0–25.3 ka, accelerating into the Celtic Sea to reach maximum limits 25.3–24.5 ka before a collapse with rapid marginal retreat to the northern Irish Sea Basin (ISB). This retreat was rapid and driven by climatic warming, sea‐level rise, mega‐tidal amplitudes and reactivation of meridional circulation in the North Atlantic. The retreat, though rapid, is uneven, with the stepped retreat pattern possibly a function of the passage of the ice stream between normal and adverse ice bed gradients and changing ice stream geometry. Initially, wide calving margins and adverse slopes encouraged rapid retreat (～550 m a^?1) that slowed (～100 m a^?1) at the topographic constriction and bathymetric high between southern Ireland and Wales before rates increased (～200 m a^?1) across adverse bed slopes and wider and deeper basin configuration in the northern ISB. These data point to the importance of the ice bed slope and lateral extent in predicting the longer‐term (>1000 a) patterns and rates of ice‐marginal retreat during phases of rapid collapse, which has implications for the modelling of projected rapid retreat of present‐day ice streams. Copyright © 2013 John Wiley & Sons, Ltd.     

Implementing quality control on a random number stream to improve a stochastic weather generator

For decades, stochastic modellers have used computerized random number generators to produce random numeric sequences fitting a specified statistical distribution. Unfortunately, none of the random number generators we tested satisfactorily produced the target distribution. The result is generated distributions whose mean even diverges from the mean used to generate them, regardless of the length of run. Non‐uniform distributions from short sequences of random numbers are a major problem in stochastic climate generation, because truly uniform distributions are required to produce the intended climate parameter distributions. In order to ensure generation of a representative climate with the stochastic weather generator CLIGEN within a 30‐year run, we tested the climate output resulting from various random number generators. The resulting distributions of climate parameters showed significant departures from the target distributions in all cases. We traced this failure back to the uniform random number generators themselves. This paper proposes a quality control approach to select only those numbers that conform to the expected distribution being retained for subsequent use. The approach is based on goodness‐of‐fit analysis applied to the random numbers generated. Normally distributed deviates are further tested with confidence interval tests on their means and standard deviations. The positive effect of the new approach on the climate characteristics generated and the subsequent deterministic process‐based hydrology and soil erosion modelling are illustrated for four climatologically diverse sites. Copyright © 2007 John Wiley & Sons, Ltd.     

Simulation of probability distributions commonly used in hydrological frequency analysis

Random variable simulation has been applied to many applications in hydrological modelling, flood risk analysis, environmental impact assessment, etc. However, computer codes for simulation of distributions commonly used in hydrological frequency analysis are not available in most software libraries. This paper presents a frequency‐factor‐based method for random number generation of five distributions (normal, log–normal, extreme‐value type I, Pearson type III and log‐Pearson type III) commonly used in hydrological frequency analysis. The proposed method is shown to produce random numbers of desired distributions through three means of validation: (1) graphical comparison of cumulative distribution functions (CDFs) and empirical CDFs derived from generated data; (2) properties of estimated parameters; (3) type I error of goodness‐of‐fit test. An advantage of the method is that it does not require CDF inversion, and frequency factors of the five commonly used distributions involves only the standard normal deviate. Copyright © 2006 John Wiley & Sons, Ltd.     

基于机器学习模型的淮河流域中长期径流预报研究

准确可靠的中长期径流预报是支撑水资源科学调配、提高水资源利用效率的关键。本研究采用AdaBoost模型(AdB)、随机森林模型(RF)和支持向量机模型(SVM)进行淮河流域王家坝和蚌埠站当年11月至次年10月共12个月的中长期径流预报研究。采用置换准确度重要性度量法从130项气象-气候因子及前期降雨/流量构建的1 562个因子变量中筛选出影响各月径流的关键因子,构建了基于AdB、RF和SVM模型的各月径流预报模型,模型参数采用随机搜索技术并结合交叉验证方式确定。采用变幅误差合格率和等级(五级)预报合格率指标对模型的预报精度进行了评估。变幅误差合格率指标表明,王家坝12个月的平均合格率分别为99.8%(AdB)、96.6%(RF)和95.9%(SVM),蚌埠站分别为100%(AdB)、94.8%(RF)和93.8%(SVM);等级预报合格率指标表明,王家坝12个月的平均合格率分别为79.0%(AdB)、76.4%(RF)和79.9%(SVM),蚌埠站分别为81.0%(AdB)、75.6%(RF)和76.6%(SVM)。模型均具有较好的预报效果,但RF和SVM模型对于高流量值的预报存在偏低现象,AdB模型整体上优于RF和SVM模型。     

ERA-Interim和CMFD气象驱动数据在新疆额尔齐斯河流域的适用性评价

气象驱动数据质量是影响流域水文过程模拟精度的一个重要因素。基于新疆额尔齐斯河流域及周边区域8个气象站记录的数据,对ERA-Interim再分析资料和中国区域地面气象要素驱动数据集（CMFD）在流域的适用性进行了评价,并对比了ERA-Interim和CMFD气象要素年均值在流域的空间分布。结果表明：ERA-Interim和CMFD记录气温、相对湿度、向下短波辐射和向下长波辐射数据与观测数据具有较高的一致性,但降水和风速数据与观测数据的一致性比较差。小时尺度上ERA-Interim记录的气温、相对湿度、降水量、向下短波辐射准确度略高于CMFD数据,而日尺度上CMFD记录的所有气象要素的准确度均高于ERA-Interim数据,结合Noah-MP模型的模拟结果,认为CMFD数据在新疆额尔齐斯河流域的适用性整体优于ERA-Interim数据。从两种驱动数据获取的流域气象要素空间分布来看,ERA-Interim和CMFD获取的年平均气温、风速、相对湿度、降水量、向下长波辐射在流域空间具有高度一致性,但向下短波辐射空间分布差别较大。     

基于机器学习的表层土壤成矿元素空间预测: 以稀有金属铷元素为例

近些年随着土地质量地球化学调查工作的开展,获取了大量表层土壤样品数据。然而,这些数据也存在一个明显的缺陷,即1∶50 000大比例尺表层土壤数据往往缺少成矿元素。鉴于土壤成矿元素含量对于矿产资源勘查的重要指示作用,尝试基于现有数据对大比例尺表层土壤成矿元素含量提供一个补全方案。以稀有金属铷元素为例,采用随机森林算法把同一区域2 548组1∶250 000小比例尺表层土壤数据按照8∶2的比例随机分为两组,用80%的数据进行训练建模,20%的数据对模型进行验证。采用变量重要性度量排序和构建学习曲线的组合方法优选了8种元素(K、B、Ni、V、Zn、As、Co、Cu)作为预测变量,模型对训练数据和测试数据的拟合优度R²分别达到0.983 2和0.895 6,说明预测变量的优选方法是有效的。随后将1∶50 000表层土壤的上述预测变量数据作为输入变量导入模型中,得到预测的Rb元素含量,预测结果比较符合实际特征。本研究表明将大数据机器学习随机森林算法引入表层土壤地球化学元素含量空间定量预测具有可行性,可进一步拓展土地质量地球化学数据的服务应用维度。     

Generating non-stationary random fields of auto-correlated,normally distributed CPT profile by matrix decomposition method

This paper emphasises the true realisation of Cone Penetration Test (CPT) profiles considering non-stationary nature of the data. Formulation of stationary random field theory has been modified and adapted to non-stationary state in order to take into account the mean and variance variability for soil properties. Multi-variance correlation matrix along with the Cholesky decomposition technique was employed to produce realisations of non-homogenous and non-stationary random fields of CPT profiles. A piecewise and segmental data realisation according to the lithology and site class specifications acquired directly from CPT data is adopted in this study so as to render an accurate data simulation. For validation of proposed method 8 CPT test profiles collected from Urmia Lake site have been introduced and simulated by the stationary and non-stationary algorithms. The mean correlation coefficient between the actual CPT data profiles and related realisations along with some other important statistical parameters and their coefficients of variation strongly demonstrate that non-stationary random field generation technique gives quite better accuracy, by comparison to the conventional stationary random field generation scheme.