Genealogical concordance,comparative species delimitation,and the specific status of the Caspian pipefish Syngnathus caspius (Teleostei: Syngnathidae)

To evaluate the specific validity of the Caspian pipefish Syngnathus caspius, we used a comparative molecular species delimitation method on a COI barcode library of Syngnathus, as well as principles of genealogical concordance. Comparative species delimitation allowed us to delineate putative species without a priori assignment of individuals to nominal species, while genealogical concordance extended our species delimitation results to multiple genes, multiple codistributed species, and comparisons with biogeographic evidence. All species delimitation analyses including two topology‐based, one network‐based, and one distance‐based analysis showed genetically isolated lineages of pipefish in the Black and Caspian Sea, corresponding to S. abaster and S. caspius, respectively. Mean evolutionary divergence between the two lineages (0.029) was within the range separating species of Syngnathus (0.024–0.217). The interclade/intraclade ratio of variation was comparable to the operational criterion of divergence between clades greater or equal to 10 × the level within clades to recognize separate species. Our argument on taxonomic validity of S. caspius is also supported by the principles of genealogical concordance as a conceptual basis for recognition of biological species. As a second objective, using a limited number of S. caspius specimens from two semi‐confined water bodies along the Caspian Sea south coastal zone (i.e., Anzali Wetland in the west and Gorgan Bay in the east), we searched for a possible matrilineal structure. The retrieved phylogeographic pattern was characterized by a shallow genealogy and lineage distributions varied, most probably caused by low to modest contemporary gene flow between populations of S. caspius across the southern Caspian Sea that are linked tightly through history.     

A multiscaling‐based intensity–duration–frequency model for extreme precipitation

Rainfall intensity–duration–frequency (IDF) curves are a standard tool in urban water resources engineering and management. They express how return levels of extreme rainfall intensity vary with duration. The simple scaling property of extreme rainfall intensity, with respect to duration, determines the form of IDF relationships. It is supposed that the annual maximum intensity follows the generalized extreme value (GEV) distribution. As well known, for simple scaling processes, the location parameter and scale parameter of the GEV distribution obey a power law with the same exponent. Although, the simple scaling hypothesis is commonly used as a suitable working assumption, the multiscaling approach provides a more general framework. We present a new IDF relationship that has been formulated on the basis of the multiscaling property. It turns out that the GEV parameters (location and scale) have a different scaling exponent. Next, we apply a Bayesian framework to estimate the multiscaling GEV model and to choose the most appropriate model. It is shown that the model performance increases when using the multiscaling approach. The new model for IDF curves reproduces the data very well and has a reasonable degree of complexity without overfitting on the data.     

A sequential sparse polynomial chaos expansion using Bayesian regression for geotechnical reliability estimations

Polynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least-square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)-PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL-PCE-IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples.     

台风风暴潮情景构建与时空推演

因台风风暴潮的突发性、情景演变时间的连续性和路径的不确定性,导致应急决策者在应急救援中难以做出正确决策,针对这一现状,将“情景—应对”应用在台风风暴潮应急决策中。本文在分析台风风暴潮情景、情景要素的概念模型基础上,首先通过资料搜集、属性识别等方法提取关键情景要素,采用框架表示法构建情景;然后分析台风风暴潮情景演变规律及演变路径;其次通过动态贝叶斯网络法构建台风风暴潮动态情景网络;最后利用先验概率与条件概率计算情景状态概率,实现了台风风暴潮的关键情景推演。本文以2018年9月16日11时至17时山竹台风对广东省沿海城市影响为例,演示了台风风暴潮的情景推演流程及关键技术。实证分析结果表明,溃堤、海水倒灌、洪水、滑坡发生的概率分别为85%、81%、74%、54%,验证了情景推演在风暴潮中应用的合理性。     

WiFi指纹定位的一种新组合算法

探讨AP选取策略和贝叶斯位置估计算法对基于RSS的WiFi室内定位技术位置估计精度的影响。国内外学者分别对AP选取算法和贝叶斯位置估计算法进行了大量的研究。为了进一步深入研究不同算法的优劣性,利用组合优化的思想对不同算法进行组合,通过找出最优算法组合从而提升WiFi室内定位系统的性能。基于互信息最小化的AP选取策略和考虑AP相关性的贝叶斯位置估计算法,提出一种新的WiFi指纹定位组合算法。实验分析表明:新算法具有良好的实用性和定位性能。     

扰动重力梯度的球冠谐分析建模

从球冠谐理论出发,详细推导了球冠坐标系下扰动重力梯度的无奇异性计算公式。基于Tikhonov正则化方法,利用GOCE卫星实际观测数据解算局部重力场球冠谐模型。数值计算表明,基于扰动重力梯度的球冠谐分析建模方法能够有效地恢复局部重力场中的短波信号,与GO_CONS_GCF_2_DIR_R5模型的差异在±0.3×10~(-5) m/s~2水平。     

基于贝叶斯网络的水源涵养服务空间格局优化

以渭河流域关中—天水经济区段（简称“渭河流域关天段”）为研究区,基于贝叶斯网络和水量平衡原理建立了水源涵养服务网络模型;将CA-Marcov模型与贝叶斯网络模型相结合,预测了2050年不同土地利用情景及其水源涵养服务分布概率;提出了关键变量关键状态子集方法,对研究区水源涵养服务空间格局进行优化。结果表明：① 保护情景下,林地面积增长了18.12%,其主要来源为耕地;草地和城市面积增长缓慢,分别增加了0.73%和0.38%;水体和未利用地分别减少了5.08%和0.92%,该情景下的水源涵养量值偏高的概率在3种情景中最大,保护情景的设计对未来的土地利用政策制定具有一定参考价值。② 水源涵养服务的关键影响因子是降水、蒸散发和土地利用,水源涵养量最高状态对应的关键变量关键状态子集是：﹛降水= 1,蒸散发= 2,土地利用= 2﹜,该子集主要分布在年平均降雨量和蒸散发量较大,植被覆盖率高的地区。③ 研究区适宜优化水源涵养的区域主要分布在天水市麦积区南部、宝鸡市陇县西南部和渭滨区南部、咸阳市旬邑县东北部和永寿县西北部,以及铜川市耀州区西部。结合贝叶斯网络模型研究水源涵养服务的优化区域,不仅有助于提升对生态系统水源涵养服务过程的直观认识,而且增加了情景设计和格局优化的合理性。在此基础上提出的关键状态关键因子方法,对研究区水源涵养生态环境建设和政策制定都具有重要的意义。     

基于BPSO-NBayes的雷暴释用预报技术研究

提出了一种新的雷暴预报法,即二进制粒子群-朴素贝叶斯分类器(Binary Particle Swarm Optimization-Naive Bayesian Classifiers,BPSO-NBayes)方法,以福州、连城、宁波3站为例,对使用T511数值预报产品站点的雷暴释用预报技术进行研究。利用2010—2014年T511数值预报产品和单站观测资料,使用BPSO-NBayes方法,建立了0~72 h雷暴预报模型,并与Fisher判别准则和Bayes判别准则进行比较。预报结果表明,BPSO-NBayes模型临界成功指数都在0.29以上,平均值达到0.33以上,是3种方法中最好的,空报率都在0.59以下,漏报率在0.60以下,而且变化幅度很小。BPSO-NBayes模型明显优于Fisher判别准则和Bayes判别准则,具有良好的稳定性和预报能力。     

瑞雷波多阶模式频散曲线稀疏正则化反演方法研究

目前瑞雷波多阶模式频散曲线反演中仅考虑数据的拟合,缺乏对模型的约束,不能很好地刻画地层间断面的问题,针对此问题,研究了瑞雷波多阶模式频散曲线稀疏正则化反演方法.正演模拟基于广义反射-透射系数法,数值计算上采用一种快速求根方法,与二等分方法相比,能够在很短的时间内达到最优的收敛效果;反演建模时采用L1范数正则化方法对模型...