唐山地震人员震亡率与房屋倒塌率的相关分析

运用震后航空像片抽样统计了唐山地震的房屋倒塌率,分析了造成人员伤亡的主要原因是房屋毁坏。据此,建立了地震人员伤亡率与房倒率的回归方程,该方程可用于震前人员伤亡预测和震后快速粗估     

TRP 模型及其在航空气象中的应用

通过对有关原始资料，采用等价正态偏差统计处理方法，得到了一系列原始资料与其正态化值之间的正态化转换关系；又通过引进TRP概率模型，建立了南京地区关于低云量和能见度的TRP方程；并在此基础上讨论了TRP模型在航空气象中的应用。     

REVIEW ARTICLE MULTIVARIATE EXPLORATORY DATA ANALYSIS AND GRAPHICS:A TUTORIAL

Exploratory data analysis(EDA)is a toolbox of data manipulation methods for looking at data to seewhat they seem to say,i.e.one tries to let the data speak for themselves.In this way there is hope thatthe data will lead to indications about'models'of relationships not expected a priori.In this respect EDAis a pre-step to confirmatory data analysis which delivers measures of how adequate a model is.In thistutorial the focus is on multivariate exploratory data analysis for quantitative data using linear methodsfor dimension reduction and prediction.Purely graphical multivariate tools such as 3D rotation andscatterplot matrices are discussed after having introduced the univariate and bivariate tools on which theyare based.The main tasks of multivariate exploratory data analysis are identified as'search for structure'by dimension reduction and'model selection'by comparing predictive power.Resampling is used tosupport validity,and variables selection to improve interpretability.     

Modeling surficial sand and gravel deposits

Mineral-deposit models are an integral part of quantitative mineral-resource assessment. As the focus of mineral-deposit modeling has moved from metals to industrial minerals, procedure has been modified and may be sufficient to model surficial sand and gravel deposits. Sand and gravel models are needed to assess resource-supply analyses for planning future development and renewal of infrastructure. Successful modeling of sand and gravel deposits must address (1) deposit volumes and geometries, (2) sizes of fragments within the deposits, (3) physical characteristics of the material, and (4) chemical composition and chemical reactivity of the material. Several models of sand and gravel volumes and geometries have been prepared and suggest the following: Sand and gravel deposits in alluvial fans have a median volume of 35 million m³. Deposits in all other geologic settings have a median volume of 5.4 million m³, a median area of 120 ha, and a median thickness of 4 m. The area of a sand and gravel deposit can be predicted from volume using a regression model (log [area (ha)] =1.47+0.79 log [volume (million m³)]). In similar fashion, the volume of a sand and gravel deposit can be predicted from area using the regression (log [volume (million m³)]=–1.45+1.07 log [area (ha)]). Classifying deposits by fragment size can be done using models of the percentage of sand, gravel, and silt within deposits. A classification scheme based on fragment size is sufficiently general to be applied anywhere.     

三门峡市2005—08—17暴雨天气过程分析

利用“9210”实时资料，欧洲ECMWF和T213格点资料、物理量分析场，分析了2005—08—17三门峡市暴雨过程中500hPa、700hPa、地面天气形势及流场、水汽通量、总温度平流、散度，结果表明：副高东退，随着短波槽的东移加深，850hPa和700hPa槽前形成了≥6m／s的偏南气流，打开了水汽通道，为暴雨形成提供了充足的水汽来源；中高层低槽后部冷空气的斜压作用以及地面西路冷锋的触发抬升，加之散度场上低层强烈辐合、中层上升、高层辐散的耦合机制，为暴雨形成提供了充足的动力条件。     

水稻叶片不同光谱形式反演叶绿素含量的对比分析研究

通过对常优1号和武粳15两个品种水稻叶片的反射率R、lg(1/R)、反射率一阶微分（FD）和反射率归一化（BN）等光谱形式的测量和计算，分析了叶片光谱不同变化形式与叶绿素含量的相关关系，建立了统计方程，并进行了比较与评价，同时，对反演方程的最佳波段选择进行了探讨。结果表明，叶绿素含量与反射率一阶微分光谱方程的相关性最强，而采用lg(1/R)的光谱形式能够提高遥感反演叶绿素含量的效果。经验证，两个水稻品种叶绿素含量的模拟值与实测值的复相关系数R2分别达到0.641和0. 818。     

Satellite mapping of Baltic Sea Secchi depth with multiple regression models

Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea’s optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea’s special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model’s mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003–2012 come with this paper as Supplementary materials.     

Prediction of the presence of topsoil nitrogen from spaceborne hyperspectral data

Conventional methods of soil nitrogen extraction are time consuming, expensive and tedious. Remote sensing and Geographical Information System technologies can be used for the rapid and efficient prediction of the presence of soil nitrogen. However, studies are limited by and large to fields of larger and homogeneous units. This research concentrates on the prediction of topsoil nitrogen from harvested, scattered and small-sized agricultural fields of India using hyperspectral data. Spaceborne hyperspectral Hyperion data are used for the prediction of the presence of nitrogen. Multivariate partial least square regression method was used to predict the presence of nitrogen from reflectance. Reflectance data were pretreated using moving average and Savitzky–Golay filters which resulted in moderate prediction of R² 0.65 and 0.63 for calibration and validation, respectively. It can be inferred that Hyperion data can be effectively used for the prediction of the presence of soil nitrogen with a moderate level of accuracy even in case of scattered fields and fields of sizes approximately equal to the spatial resolution of the satellite.     

基于非参数回归的交通事件影响范围预测

基于非参数回归方法,创新性地利用拥堵信息建立模式库,解决了传统分类、回归、神经网络等预测方法的事件特征选取限制及训练样本不足等问题,提高了预测结果的准确性。     

Diagnostic study and modeling of the annual positive water temperature onset

A data-driven model is designed using artificial neural networks (ANN) to predict the average onset for the annual water temperature cycle of North-American streams. The data base is composed of daily water temperature time series recorded at 48 hydrometric stations in Québec (Canada) and northern US, as well as the geographic and physiographic variables extracted from the 48 associated drainage basins. The impact of individual and combined drainage area characteristics on the stream annual temperature cycle starting date is investigated by testing different combinations of input variables. The best model allows to predict the average temperature onset for a site, given its geographical coordinates and vegetation and lake coverage characteristics, with a root mean square error (RMSE) of 5.6 days. The best ANN model was compared favourably with parametric approaches.