基于灰色模糊集理论的船舶搁浅概率分析

为深入分析船舶搁浅概率,在随机性和模糊性的基础上,考虑了系统的灰色性,建立灰色模糊集,模部为正态模糊数,灰部以系统的灰度熵来表示。建立了船舶航行计划失效造成船舶搁浅的事件树,构造了船舶搁浅概率的灰色模糊隶属度函数,根据各基本事件的失效概率来分析因航行计划失效造成船舶搁浅事故的灰色模糊概率分布情况。结果表明,与经典概率论和模糊概率相比,采用灰色模糊概率更能全面地反映船舶搁浅概率的可能性与不确定性。并采用RRW法和RAW法对基本事件的重要度进行了分析,找出了事故主要诱因及对降低事故风险价值最大的基本事件。     

基于SA-ELM的爆破震动速度预测

针对爆破震动速度与其影响因素之间的复杂非线性,结合模拟退火算法(SA)的全局寻优性,提出了一种新的SA-ELM算法.以矿山周边建筑物爆破震动实测数据作为训练样本,选取总药量、最大段药量、测点与爆破点距离、地面震动特性、建筑物震动特性等8个影响因素作为输入变量,建立了爆破震动速度预测的SA-ELM模型.模型训练值和预测值与实测值的均方误差(MSE)分别为0.20和3.26,平均相对误差控制在5％以内,显示出该模型具有良好的训练精度和泛化能力.对比传统ELM模型,SA-ELM模型不但提高了精度和泛化能力,而且降低了隐层节点数变化对训练结果的影响,提高了模型的稳定性.     

Estimated Injury Risk for Specific Injuries and Body Regions in Frontal Motor Vehicle Crashes

Objective: Injury risk curves estimate motor vehicle crash (MVC) occupant injury risk from vehicle, crash, and/or occupant factors. Many vehicles are equipped with event data recorders (EDRs) that collect data including the crash speed and restraint status during a MVC. This study's goal was to use regulation-required data elements for EDRs to compute occupant injury risk for (1) specific injuries and (2) specific body regions in frontal MVCs from weighted NASS-CDS data.

Methods: Logistic regression analysis of NASS-CDS single-impact frontal MVCs involving front seat occupants with frontal airbag deployment was used to produce 23 risk curves for specific injuries and 17 risk curves for Abbreviated Injury Scale (AIS) 2+ to 5+ body region injuries. Risk curves were produced for the following body regions: head and thorax (AIS 2+, 3+, 4+, 5+), face (AIS 2+), abdomen, spine, upper extremity, and lower extremity (AIS 2+, 3+). Injury risk with 95% confidence intervals was estimated for 15–105 km/h longitudinal delta-Vs and belt status was adjusted for as a covariate.

Results: Overall, belted occupants had lower estimated risks compared to unbelted occupants and the risk of injury increased as longitudinal delta-V increased. Belt status was a significant predictor for 13 specific injuries and all body region injuries with the exception of AIS 2+ and 3+ spine injuries. Specific injuries and body region injuries that occurred more frequently in NASS-CDS also tended to carry higher risks when evaluated at a 56 km/h longitudinal delta-V. In the belted population, injury risks that ranked in the top 33% included 4 upper extremity fractures (ulna, radius, clavicle, carpus/metacarpus), 2 lower extremity fractures (fibula, metatarsal/tarsal), and a knee sprain (2.4–4.6% risk). Unbelted injury risks ranked in the top 33% included 4 lower extremity fractures (femur, fibula, metatarsal/tarsal, patella), 2 head injuries with less than one hour or unspecified prior unconsciousness, and a lung contusion (4.6–9.9% risk). The 6 body region curves with the highest risks were for AIS 2+ lower extremity, upper extremity, thorax, and head injury and AIS 3+ lower extremity and thorax injury (15.9–43.8% risk).

Conclusions: These injury risk curves can be implemented into advanced automatic crash notification (AACN) algorithms that utilize vehicle EDR measurements to predict occupant injury immediately following a MVC. Through integration with AACN, these injury risk curves can provide emergency medical services (EMS) and other patient care providers with information on suspected occupant injuries to improve injury detection and patient triage.     

基于可靠性理论的事件树分析方法研究

事件树分析过程中事件树的编制和分析方法缺乏系统性,导致实际运用中事件树的编制缺乏可操作性。从目标系统的选择、环节事件逻辑关系的确定、定量分析中基本数据的来源和确定方法等三个问题进行了系统研究,并以可靠性的相关理论为基础,提出"从控制和影响初始事件发展演化的角度,调查分析对初始事件做出响应的安全功能,按系统工作原理对目标系统进行重构,构建目标系统的功能结构图,画出系统的可靠性框图"入手,对"传统的事件树绘制程序"进行修正,解决了事件树分析过程中目标系统的选择、环节事件逻辑关系的确定以及定量分析基本数据的确定方法等问题。修正后的方案对实现事件树的正确编制和系统分析逻辑更清晰,操作指导性更强。     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

基于余震序列分布信息的地震极灾区快速判断方法研究

利用震级加权标准差椭圆法研究了基于震后短期内(24 h)余震序列判定地震极灾区的方法,研究结果表明:1利用该方法判断宏观震中是一种比较可行的方法,不同震源破裂方式的震例利用该方法判断宏观震中的精度不同,倾滑-近倾滑型破裂方式的震例利用余震序列判定宏观震中的精度最高;2整体上看,利用该方法判断极灾区长轴方向的准确性低于利用距震中最近活动断裂判断的准确性,但对于倾滑-近倾滑型破裂方式的震例,利用该方法的准确性高于利用断层数据判定的准确性。     

基于Copula函数的汉江中上游流域极端降雨洪水联合分布特征

选取汉江中上游流域作为研究对象,根据流域九个气象站点1969~2008年逐日降雨资料以及丹江口水库同时期日入库流量资料,采用年最大值法(AM)和百分位法两种选样方式选取1 d、3 d降雨和1 d、3 d洪量极值样本,分别运用广义极值分布(GEV)、广义帕累托分布(GPD)、伽玛分布(Gamma)3种极值统计模型对样本进行单变量边缘分布拟合,运用Gumbel、Clayton以及Frank Copula函数模型对样本进行多变量联合分布拟合,遴选出描述流域降雨和洪水联合分布规律的最优概率模型。结果显示:对于AM选样样本,边缘分布为GEV时降雨洪量的二维和三维联合分布Frank Copula函数拟合效果最优;对于百分位选样样本,边缘分布为GPD时降雨洪量的二维联合分布Gumbel Copula函数拟合效果最优,三维联合分布则是Frank Copula函数拟合效果最优;比较二维和三维Copula函数模拟结果,三维联合Copula函数推求的设计值更大,说明三维联合分布考虑了更多的变量和极值信息,能更全面地反映极端降雨洪水事件的真实特征,对工程设计更显安全。     

2021年中国北方首次沙尘天气的多源遥感分析

利用多源遥感数据和NCEP再分析资料,从沙尘源区的地理环境和气候特征出发,对2021年中国北方首次沙尘天气事件每日的大气环流形势以及沙尘的水平,垂直分布特征进行深入的研究分析.结果表明:源区内异常增温,降水稀少的气候背景下致使大面积裸露松散的土壤含水量较低,为大范围,高强度沙尘天气的形成提供了物质基础;频繁活动的冷空气,是沙尘天气爆发的动力因子.沙尘在强风中沿东南方向向下游地区输送和扩散,中国西北,华北,黄淮,江淮,江汉地区和江南北部等地先后受到沙尘天气的影响,空气质量迅速恶化,首要污染物为PM₁₀.此外,沙尘气溶胶东移也波及朝鲜半岛,日本等下游地区.在输送过程中,内陆地区沙尘主要分布在1~6km,而下游地区的沙尘则集中分布在2km高度附近,粒径较大的沙尘出现在近地表的频率较高,较小的颗粒主要分布在对流层中下层.     

极端梯度提升与随机森林融合的天然气露点预测方法

目的 解决目前水露点数据多为人工采用测量仪器测得,时效性低且成本高昂的问题。方法 建立一种基于极端梯度提升(XGBoost)和随机森林(RF)的天然气水露点预测方法。采用XGBoost方法对所有监测工艺参数进行分析,筛选出主要影响水露点的关键工艺特征参数,以排除无关特征参数对预测的干扰。建立RF预测模型,输入关键特征集参数,实现对水露点的实时预测。以重庆气矿某脱水监测系统监测数据与生产数据为例,对所提预测方法进行对比分析验证。结果 相较于XGBoost、SVM等预测方法,RF模型具有最佳的预测性能,且经过XGBoost特征选择后,RF预测结果的MAE值降低了0.016 9 ℃,RMSE值降低了0.014 6 ℃。结论 基于极端梯度提升与随机森林融合的水露点预测方法具有更优的预测精度与鲁棒性,对指导脱水现场生产具有积极作用。     

过去两千年长江干流历史洪水事件的时空变化研究

理解极端洪水事件活动特征及其与气候变化、人类活动的相互关系一直是全球变化研究的热点问题。现代器测数据较短对洪水事件变化规律预测存在不足,而利用历史文献记录研究百年-千年尺度洪水事件的发生规律则成为一个较好的选择。洪水灾害是长江流域最为严重的自然灾害之一。本文以官方现存历史记载为依据,构建了过去2000年长江干流上、中、下游洪水事件序列,并将其与气候和人类活动代理指标进行对比。结果表明,1350年之前,长江干流洪水事件受东亚季风(EASM)、西南季风(ISM)、厄尔尼诺-南方涛动(ENSO)、热带辐合带(ITCZ)移动等多种气候因素驱动,且气候驱动机制在同期不同区、同区不同期存在显著差异。此外,1350年之后人类活动的增强可能明显加剧了长江干流洪水事件的频率和强度。