基于支持向量回归机的南黄海浒苔分布面积预测模型

绿潮作为一种新型的海洋灾害,已经引起了各个国家的重视.依据2012年南黄海海域浒苔遥感监测分布面积数据,选取了温度、天气状况、风向、风力、浪高5种影响浒苔扩散的气候因子,建立了基于SVR的浒苔分布面积预测模型,并与经典的最近邻点插值模型、线性插值模型、3次样条函数插值模型和分段3次Hermite插值模型进行了回归效果的对比.分析结果表明,基于SVR的浒苔分布面积预测模型能够为浒苔遥感数据的插补提供一种方法,且回归效果优于传统的回归方法,为浒苔的防治提供辅助决策信息.     

SELECTING TREND TESTS FOR WATER QUALITY SERIES WITH SERIAL CORRELATION AND MISSING VALUES1

ABSTRACT: The use of nonparametric tests for monotonic trend has flourished in recent years to support routine water quality data analyses. The validity of an assumption of independent, identically distributed error terms is an important concern in selecting the appropriate nonparametric test, as is the presence of missing values. Decision rules are needed for choosing between alternative tests and for deciding whether and how to pre-process data before trend testing. Several data pre-processing procedures in conjunction with the Mann-Kendall tau and the Seasonal Kendall test (with and without serial correlation correction) are evaluated using synthetic time series with generated serial correlation and missing data. A composite test (pre-testing for serial correlation followed by one of two trend tests) is evaluated and was found to perform satisfactorily.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

Evaluation of the temporal variations of air quality in Taipei City, Taiwan, from 1994 to 2003

Data collected from the five air-quality monitoring stations established by the Taiwan Environmental Protection Administration in Taipei City from 1994 to 2003 are analyzed to assess the temporal variations of air quality. Principal component analysis (PCA) is adopted to convert the original measuring pollutants into fewer independent components through linear combinations while still retaining the majority of the variance of the original data set. Two principal components (PCs) are retained together explaining 82.73% of the total variance. PC1, which represents primary pollutants such as CO, NO(x), and SO(2), shows an obvious decrease over the last 10 years. PC2, which represents secondary pollutants such as ozone, displays a yearly increase over the time period when a reduction of primary pollutants is obvious. In order to track down the control measures put forth by the authorities, 47 days of high PM(10) concentrations caused by transboundary transport have been eliminated in analyzing the long-term trend of PM(10) in Taipei City. The temporal variations over the past 10 years show that the moderate peak in O(3) demonstrates a significant upward trend even when the local primary pollutants have been well under control. Monthly variations of PC scores demonstrate that primary pollution is significant from January to April, while ozone increases from April to August. The results of the yearly variations of PC scores show that PM(10) has gradually shifted from a strong correlation with PC1 during the early years to become more related to PC2 in recent years. This implies that after a reduction of primary pollutants, the proportion of secondary aerosols in PM(10) may increase. Thus, reducing the precursor concentrations of secondary aerosols will be an effective way to lower PM(10) concentrations.     

Prenatal prediction of childhood-onset spinal muscular atrophy (SMA) in Turkish families

Childhood-onset spinal muscular atrophy (SMA) is one of the most common neurodegenerative genetic disorders. SMN1 is the SMA-determining gene deleted or mutated in the majority of SMA cases. There is no effective cure or treatment for this disease yet. Thus, the availability of prenatal testing is important. Here we report prenatal prediction for 68 fetuses in 63 Turkish SMA families using direct deletion analysis of the SMN1 gene by restriction digestion. The genotype of the index case was known in 40 families (Group A) but unknown in the remaining 23 families (Group B). A total of ten fetuses were predicted to be affected. Eight of these fetuses were derived from Group A and two of these fetuses were from Group B families. Two fetuses from the same family in Group A had the SMNhyb1 gene in addition to homozygous deletion of the NAIP gene. One fetus from Group A was homozygously deleted for only exon 8 of the SMN2 gene, and further analysis showed the presence of both the SMN1 and SMNhyb1 genes but not the SMN2 gene. In addition, one carrier with a homozygous deletion of only exon 8 of the SMN1 gene was detected to have a SMNhyb2 gene, which was also found in the fetus. To our knowledge, these are the first prenatal cases with SMNhyb genes. Follow-up studies demonstrated that the prenatal predictions and the phenotype of the fetuses correlated well in 33 type I pregnancies demonstrating that a careful molecular analysis of the SMN genes is very useful in predicting the phenotype of the fetus in families at risk for SMA. Copyright © 2002 John Wiley & Sons, Ltd.     

人工神经网络在预测深基坑周边地表沉降变形中的应用研究

深基坑开挖引起的周边地表变形预测是一个复杂非线性问题,引起地表沉降的影响因素很多,各因素之间呈高度的非线性关系。传统的基坑用边地表沉降变形预测方法存在着一定的局限性,其预测精度有待提高,而人工神经网络是一种多元非线性动力学系统,可以灵活方便地对多成因的复杂未知系统进行高度建模,实现全面考虑各种主要影响因素的深基坑周边地表沉降变形预测。本文介绍了误差反向传播(BP)网络模型的结构、学习过程及其算法的改进,径向基函数(RBF)网络模型的结构及其学习过程;分析了影响深基坑开挖周边土体沉降变形的主要影响因素;以25个基坑工程的地表沉降实测资料为训练样本,建立了11个输入影响因素的BP神经网络模型和RBF神经网络模型,通过对样本的学习训练过程及对5个检验样本的预测精度,说明了人工神经网络用于预测基坑周边地表沉降的可行性和准确性。     

随机振动响应峰值因子的预计理论研究

简要介绍了超越概率理论、超越频次理论、损伤等效理论和功率谱密度(PSD)的时域拟合理论等4种常见的峰值因子预计理论,并基于三角级数提出了一种新的预计理论。结合试飞加速度数据样本,对比分析超越频次理论、PSD时域拟合理论和三角级数理论的预估精度。研究表明,上述4种常见的预计理论本质上属于统计学理论;PSD时域拟合理论预计的峰值因子波动较大,峰值因子与归一化次数满足高斯分布;三角级数理论的预估精度较高,但缺乏离散峰个数的合理判据。     

基于FLUS模型的湖北省生态空间多情景模拟预测

改革开放以来,中国经济在飞速发展的同时,生态环境问题日益严峻。为保障国家和地区的生态安全,对未来生态空间进行模拟预测十分必要。在长江大保护和长江经济带绿色发展背景下,以湖北省为研究区,利用FLUS模型基于湖北省2010年、2015年土地利用数据及包含自然和人文因素的15种驱动因子数据,对2035年的湖北省生态空间进行模拟预测。结果表明：利用2010年土地利用现状模拟出的2015年湖北省土地利用变化情况,总体精度达到0.976,Kappa系数达到0.961,模拟精度较高。设置的生产空间优先、生活空间优先、生态空间优先以及综合空间优化4种不同情景,基本满足未来湖北省不同发展导向的需求。从地貌单元角度来看,在不同情景下,湖北省生态空间主要分布于湖北省边陲四大山区,中部江汉平原生态空间零星分布。从数量规模上来看,不同情景下各个用地类型数量规模差异较为明显,生产空间优先情景下耕地面积增加1216 km²,生活空间优先情景下城镇用地规模增加5959 km²,生态空间优先情景下生态空间用地增长722 km²,综合空间优化情景下生态空间用地规模变化更趋于平缓。从生态空间变化分布来看,四大山区的生态空间变化不大,但中部江汉平原生态空间变化较为明显,其中从行政区划上来看,变化范围主要分布于武汉城市圈、襄阳市、宜昌市中西部地区及随州市中部地区。总而言之,FLUS模型对于湖北省生态空间模拟的适用性较好,多情景模拟结果可为湖北省未来国土空间规划及未来生态空间管控提供多角度、多方向的政策决策参考。     

黄河三角洲典型地区耕地土壤养分空间预测

掌握土壤养分的分布特点是实现养分优化管理的重要基础。论文选择黄河三角洲典型地区山东省垦利县为研究区,通过田间采样与实验室化验分析获取了1 278个样本（0~20 cm）的土壤碱解氮、有效磷、速效钾数据。在经典统计分析的基础上,用地统计学方法分析了土壤养分的空间变异特征,并拟合了养分的变异函数模型。利用普通克里格法（OK）、反距离权重法（IDW）、泛克里格法（UK）、径向基函数法（RBF）和局部多项式法（LP）5种方法进行空间插值,并采用独立数据集验证对插值结果进行精度评价,进而分析了各养分空间分布规律。为深入探索各方法的适用性规律,基于AN数据设计了离散、随机、聚集3种空间分布模式的数据,利用各模型的自动优化进行试验,对比分析了不同插值方法在土壤养分空间预测中的自适应性。结果表明：1）研究区碱解氮、有效磷、速效钾均为中等强度的空间变异和中等程度的空间自相关,其变异函数模型分别为球状模型、指数模型和球状模型,决定系数依次为0.951、0.892和0.787;2）在空间分布上,土壤碱解氮、有效磷、速效钾含量与地形和土地利用类型等有关,西南部地势较高,以水浇地和旱田为主,东北部沿黄农田受黄河淡水影响,耕地质量较好,而中部地区地势低平,以水田为主,养分含量偏低;3）相对于块金系数/基台值,Moran’s I是更为稳健有效的衡量土壤养分空间自相关性的方法;4）论文认为,空间分布模式、样本量、空间自相关性和空间聚集程度（最近邻比）均影响插值精度。在离散模式下,各方法自适应性均较差;在随机模式下,IDW与RBF自适应性优于OK和LP;在聚集模式下,各方法自适应性与样本量和空间自相关性有关,直至样本足够多时,4种插值方法精度接近。论文探明了研究区主要土壤养分的最佳插值预测方法,分析了土壤养分的变异特征和空间分布规律,为黄河三角洲典型地区耕地土壤养分利用管理和农业可持续发展提供了理论依据。     

大气自净能力指数的气候特征与应用研究

为了定量地评估污染气象条件对空气污染的作用并实现对空气污染潜势的预报,本文在城市大气污染数值预报系统（CAPPS）预报原理的基础上,定义了大气自净能力指数,并分别给出了采用气象站观测资料和通过数值模拟计算大气自净能力指数的方法.基于气象站观测资料的全国大气自净能力指数分析计算表明,全国大气自净能力最差的地区分布在四川盆地和新疆塔里木盆地,大气自净能力最强的地区分布在青藏高原、蒙古高原、云贵高原、以及东北平原和三江平原、山东半岛和海南岛;1961~2017年,京津冀、长三角和珠三角地区的大气自净能力指数呈下降的变化趋势,全年低自净能力日数呈上升的变化趋势.采用大气自净能力指数评估2014年北京APEC会议期间大气污染防控效果,表明在11月8~10日极端不利扩散气象条件发生时,减排措施使北京市空气质量AQI平均降低77%,使京津冀平原地区11个城市的空气质量AQI平均降低37%.基于国家气候中心月动力延伸气候预测模式（DERF2.0）的预报产品和中尺度模式（WRF）,建立了可以预测全国未来40d逐日大气自净能力指数的延伸期-月尺度大气污染潜势预测系统,回报实验表明,在大多数情况下可以提前15d预报出大气重污染过程;月尺度的大气重污染过程预报效果更大程度上取决于月动力延伸气候预测模式（DERF2.0）的预报准确率.