函数模型与随机模型双约束的GPS-InSAR数据融合方法建立三维形变场

针对InSAR技术研究地表三维形变时监测信息不足的问题,以GPS监测信息为先验信息,建立附有随机模型约束的地表三维形变模型。考虑到SAR卫星极轨方式运行导致LOS向观测量对南北向形变不敏感的问题,以GPS南北向形变观测值作为强约束,构建三维形变解算的函数约束条件。模拟数据与西安地区实测数据的计算结果表明,基于随机模型与函数模型共同约束的地表三维形变参数最小二乘解的精度优于仅有函数模型约束或仅有随机模型约束及无任何约束的参数解精度。     

中国旅游城市生态安全系统评估与类型划分研究 ——基于节点权重约束网络DEA模型

采用 DPSIR概念模型作为旅游生态安全系统的基本形态,引入网络 DEA模型测度系统运行效率,从“投入-产出”的研究视角探究旅游生态安全系统内部作用机理。采用结构方程模型对网络 DEA模型的结构及权重加以限制,构建具有节点权重约束的网络 DEA评价模型,对主要旅游城市生态安全系统实例进行实证研究。研究结果认为：将生态安全系统分为自然运行阶段、管理反馈阶段2阶段进行效率评价,加权综合得出各旅游城市生态安全系统的综合评价值,以此为依据将样本城市划分为按系统效率的评价结果将其分划为绿色发展型、稳步发展型、高效发展型、双向提升型和管理能力缺失型,针对各类型旅游城生态安全系统特点提出改善其生态安全系统状况的建议。     

基于贝叶斯正则化BP神经网络的DEM趋势面逼近

趋势面从宏观上揭示了研究对象的特性,在各领域发挥着重要作用。BP神经网络可以对复杂系统进行无限逼近,进而进行预测。建立了基于贝叶斯正则化BP神经网络的数字高程模型趋势面,与二次多项式建立的数字高程模型趋势面进行比较分析,证明了该方法的可行性和有效性。     

Web服务驱动的地理信息系统研究

Web服务具有松散耦合、高度集成、基于标准规范等优点。将Web服务的这些特性应用到地理信息系统的开发中,提出了Web服务驱动的地理信息系统架构。该架构对现有的Web服务技术进行了改进,引入了服务语义化描述、动态服务组合以及主动服务等概念。最后,对Web服务驱动的地理信息系统进行了测试（以服务发现为例）,验证了系统架构的可行性和合理性。     

GA-BP网络模型在GPS似大地水准面精化中的应用

针对误差反向传播（BP）算法训练速度慢和易于陷入局部最小值的缺点,提出了利用遗传算法（GA）的全局寻优性,结合GA和BP的各自优点,分析和建立了进化神经网络（GA-BP）模型,并将该模型应用于似大地水准面模型精化。最后以南方某市E级GPS控制网高程数据为例,进行BP和GA-BP模型的对比实验,通过对内、外符合精度及MAPE（平均绝对误差百分比）指标分析,验证了该方法的可行性。     

逐点内插法建立DEM的研究

本文讨论了移动拟合法和加权平均法两种逐点内插法建立DEM的原理及算法实现,并对其中的关键技术——动态确定阈值R和权函数的确定与实现进行了详细讨论。文章在分析常规权函数的基础上提出了一种新的定权方法。最后通过大量试验数据对两种内插方法的精度以及不同权函数的精度分别进行了评定。     

Water level variations in the Neuse and Pamlico Estuaries,North Carolina due to local and remote forcing

Water level time series records from the Neuse and Pamlico River Estuaries were statistically compared to local and distant wind field data, water level records within the Pamlico Sound and also coastal ocean sites to determine the relative contribution of each time series to water levels in the Neuse and Pamlico Estuaries. The objectives of this study were to examine these time series data using various statistical methods (i.e. autoregressive, empirical orthogonal function analysis (EOF), exploratory data analysis (EDA)) to determine short- and long-time-scale variability, and to develop predictive statistical models that can be used to estimate past water level fluctuations in both the Neuse Estuary (NE) and Pamlico Estuary (PE). Short- and long-time-scale similarities were observed in all time series of estuarine, Pamlico Sound and subtidal coastal ocean water level and wind component data, due to events (nor'easters, fronts and tropical systems) and seasonality. Empirical orthogonal function analyses revealed a strong coastal ocean and wind field contribution to water level in the NE and PE. Approximately 95% of the variation was captured in the first two EOF components for water level data from the NE, sound and coastal ocean, and 70% for the PE, sound and coastal ocean. Spectral density plots revealed strong diurnal signals in both wind and water level data, and a strong cross correlation and coherency between the NE water level and the North/South wind component. There was good agreement between data and predictions using autoregressive statistical models for the NE (R² = 0.92) and PE (R² = 0.76). These methods also revealed significant autoregressive lags for the NE (days 1 and 3) and for the PE (days 1, 2 and 3). Significant departures from predictions are attributed to local meteorological and hydrological events. The autoregressive techniques showed significant predictive improvement over ordinary least squares methods. The results are considered within the context of providing long time-scale hindcast data for the two estuaries, and the importance of these data for multidisciplinary researchers and managers.     

Nonlinear numerical simulation on extreme-wave kinematics

A fully nonlinear numerical model based on a time-domain higher-order boundary element method (HOBEM) is founded to simulate the kinematics of extreme waves. In the model, the fully nonlinear free surface boundary conditions are satisfied and a semi-mixed Euler-Lagrange method is used to track free surface; a fourth-order Runga-Kutta technique is adopted to refresh the wave elevation and velocity potential on the free surface at each time step; an image Green function is used in the numerical wave tank so that the integrations on the lateral surfaces and bottom are excluded. The extreme waves are generated by the method of wave focusing. The physical experiments are carried out in a wave flume. On the horizontal velocity of the measured point, numerical solutions agree well with experimental results. The characteristics of the nonlinear extreme-wave kinematics and the velocity distribution are studied here.     

5种中草药对美国红鱼生长和免疫机能的影响

用分别含连翘(Forsythia suspensa)、猪苓(Polyporus umbellatus)、黄芩(Radix scutellariae)、茯苓(Poria cocos)和黄连(Coptis chinensis)水提取物的配合饵料饲喂美国红鱼(Sciaenops ocellatus),研究其对美国红鱼(Sciaenops ocellatus)生长和免疫机能的影响.结果表明,投喂含有黄芩和茯苓的药饵可使美国红鱼的体长增长率和相对增质量率显著高于对照组(P<0.05);投喂5种含中草药的药饵后,美国红鱼血液白细胞的吞噬活性有明显提高,投药后3 d,除茯苓组外,其余各组吞噬百分比(PP)与对照组之间均差异极显著(P<0.01);在投药后3 d或投药后7 d,连翘、黄芩和黄连组的吞噬指数(PI)与对照组相比有显著(P<0.05)或极显著(P<0.01)差异.在停投药饵后7 d,除茯苓组外,其余各组PP、PI与对照组相比仍有显著差异(P<0.05).同时,美国红鱼血清溶菌酶活性也有显著提高,投药后7 d,溶菌酶活性与对照组之间差异极显著(P<0.01),但在停投药饵后7 d,溶菌酶活性与对照组之间无显著差异(P>0.05);投药28 d后,利用哈维氏弧菌(Vibrio harveyi)人工感染美国红鱼,黄芩组的相对免疫保护率最高,达88.9%,连翘组次之,茯苓组最低.     

基于集合预报极端天气预测指数的浙江分类强对流预报模型

利用2016—2021年ECWMF集合预报资料、浙江自动站实况资料等,计算浙江短时强降水、雷暴大风和冰雹等强对流天气相关物理量的极端天气预报指数（EFI：Extreme Forecast Index）,分析EFI分布特征,并构建了分类强对流预报模型。结果表明：强对流天气与物理量的EFI有密切联系,发生短时强降水时,对流有效位能、整层可降水量、850 hPa与500 hPa温差和位温差的EFI较大,而垂直风切变的EFI为负值,因而较小的垂直风切变更有利于出现极端降水;发生雷暴大风和冰雹时,对流有效位能、850 hPa与500 hPa温差和位温差以及850 hPa温度露点差的EFI较大,700 hPa露点温度的EFI为负值,与上层干冷下层暖湿的有利层结条件有关。利用支持向量机多分类方法,将强对流天气相关物理量的EFI作为特征值开展训练,构建的预报模型对于非局地强对流天气有较好的预报效果,其中短时强降水的误判率明显低于雷暴大风。