一种系统化的生产安全事故分级新方法研究

为了强化《生产安全事故报告和调查处理条例》(493号令)对事故管理及其影响评价的重要作用。笔者在论证了关于提出事故分级新方法的必要性后,在前人已有研究成果的基础上,结合生产安全事故特点,初步提出了一种基于"人员伤亡、财产损失、社会影响和环境污染"四个指标的系统化事故分级新方法。该事故分级理念及方法能更好地切合生产安全事故定义,对新时期下事故分级理念中事故影响多样性内涵的引入是一种积极和有效的探索,具有一定意义。     

Fish recruitment prediction, using robust supervised classification methods

Improving our ability to predict recruitment is a key element in fisheries management. However, the interactions between population dynamics and different environmental factors are complex and often non-linear, making it difficult to produce robust predictions. ‘Machine-learning’ techniques (in particular, supervised classification methods) have been proposed as useful tools, to overcome such difficulties. In this study, a methodology is proposed to build a robust classifier for fish recruitment prediction with sparse and noisy data. The methodology consists of 4 steps: (1) a semi-automated recruitment discretization method; (2) supervised discretization of predictors; (3) multivariate and non-redundant predictors selection; (4) learning a probabilistic classifier. In terms of fisheries management, the classifier estimated performance has important consequences and, to be useful, the manager needs to know the risk that is being taken when using this number. Probabilistic classifiers such as ‘naive Bayes’, have the advantage that, in addition to the predictions, estimate also the probability of each possible outcome. Anchovy (Engraulis encrasicolus) and hake (Merluccius merluccius) recruitments are used as application examples. ‘Two-intervals’ recruitment discretization accomplishes 70% accuracies and Brier scores of around 0.10, for both anchovy and hake recruitment. In comparison, ‘three-intervals’ recruitment discretization accomplishes 50% accuracies; and Brier scores of around 0.25 for anchovy and 0.30 for hake recruitment. These statistics are the result of validating not only the classifier, but also the previous steps, as a whole methodology.     

Automated detection of frog calls and choruses by pulse repetition rate

Anurans (frogs and toads) are among the most globally threatened taxonomic groups. Successful conservation of anurans will rely on improved data on the status and changes in local populations, particularly for rare and threatened species. Automated sensors, such as acoustic recorders, have the potential to provide such data by massively increasing the spatial and temporal scale of population sampling efforts. Analyzing such data sets will require robust and efficient tools that can automatically identify the presence of a species in audio recordings. Like bats and birds, many anuran species produce distinct vocalizations that can be captured by autonomous acoustic recorders and represent excellent candidates for automated recognition. However, in contrast to birds and bats, effective automated acoustic recognition tools for anurans are not yet widely available. An effective automated call-recognition method for anurans must be robust to the challenges of real-world field data and should not require extensive labeled data sets. We devised a vocalization identification tool that classifies anuran vocalizations in audio recordings based on their periodic structure: the repeat interval-based bioacoustic identification tool (RIBBIT). We applied RIBBIT to field recordings to study the boreal chorus frog (Pseudacris maculata) of temperate North American grasslands and the critically endangered variable harlequin frog (Atelopus varius) of tropical Central American rainforests. The tool accurately identified boreal chorus frogs, even when they vocalized in heavily overlapping choruses and identified variable harlequin frog vocalizations at a field site where it had been very rarely encountered in visual surveys. Using a few simple parameters, RIBBIT can detect any vocalization with a periodic structure, including those of many anurans, insects, birds, and mammals. We provide open-source implementations of RIBBIT in Python and R to support its use for other taxa and communities.     

基于熵权-集对分析模型的重大危险源安全等级评价研究

针对国内危险化学品事故频发,但危险源分级监管研究不足的现状,尝试将集对分析理论应用于重大危险源安全等级评价中。以评价指标集合与分级标准集合构成一组集对,利用熵权法计算出各项评价指标的客观权重,再结合同异反联系度函数求出各指标单项联系度及加权平均联系度,以此为依据判定重大危险源的安全等级。研究案例的实施情况表明,熵权-集对分析模型充分考虑了危险源安全因素中的不确定性,并且通过熵权值能够识别出影响系统安全的关键指标;与传统方法相比,该模型具有较高的可行性和易操作性,可以为重大危险源的安全监管和事故预防工作提供理论支持。     

基于IOCS的内蒙古潜在植被NPP空间分布特征研究

以综合顺序分类系统(IOCS)为基础,利用1985-2009年内蒙古及周边常规气象站的观测资料,采用NPP分类指数模型模拟内蒙古各潜在植被类型NPP,分析其空间分布特征及NPP与气候因子之间的关系。研究表明:①内蒙古地区植被NPP在空间分布上表现出一定的经向分布特征,多年均值介于0~366.5 gC·m^-2·a^-1之间,高值区中心分布在大兴安岭东北部;②各潜在植被类型中,碳汇潜力贡献率最大的潜在植被类型为微温微干温带典型草原类(ⅢC),其值为23.6%;最小的为寒温微干山地草原类(ⅡC),接近于0;③内蒙古潜在植被类型NPP表现出明显的干湿地带性和纬度地带性,且随寒温→微温→暖温、微干→微润→湿润→潮湿的变化其NPP值逐渐增大;但暖温极干暖温带荒漠类(ⅣA)和暖温干旱暖温带半荒漠类(ⅣB)并不符合这一规律,主要原因是较高的温度和较少的降水,达不到植被物质生长的需求。     

考虑区域灾情分级的应急物资公平调度优化模型

为解决不同受灾点应急物资公平调度的问题,受分区分级精准防控思想的启发,考虑同级间各灾点应急物资最小未满意度为基准设计公平性衡量指标,以最小化调度时间和最大化应急物资分配公平性为目标,构建一种新的考虑差异化灾情分级的应急物资公平调度优化模型,运用遗传算法求解模型,以新冠疫情重灾区湖北省作为案例仿真模拟.结果 表明:该模型...     

基于GOCI影像和水体光学分类的内陆湖泊叶绿素a浓度遥感估算

叶绿素a作为水质参数之一,常用来作为衡量水体富营养化程度的指示标准.利用从太湖及洞庭湖获取的326个实测数据,基于实测遥感反射率对水体光谱进行光学分类,结果表明所采集的样点可分为3种水体类型.结合GOCI的波段设置,建立了不同类型水体的叶绿素a浓度反演模型.水体类型一可以利用490 nm(3波段)和555 nm(4波段)来反演,水体类型二可利用660 nm(5波段)和443 nm(2波段),水体类型三利用745 nm(7波段)和680 nm(6波段).精度分析表明,分类后的平均相对误差明显下降,类型一为38.91%、类型二为24.19%、类型三为22.90%;类型一均方根误差为4.87μg·L-1、类型二为8.13μg·L-1、类型三为11.66μg·L-1;分类前后的总体平均相对误差由49.78%降低到29.59%,总体均方根误差由14.10μg·L-1降低到9.29μg·L-1,分类后反演精度得到了显著提高.利用2013年5月13日8景GOCI影像反演了太湖的叶绿素a浓度,结果表明,2013年5月13日太湖叶绿素a浓度日变化显著,高值区主要集中在竺山湾、梅梁湾、贡湖湾,低值区主要集中在湖心区以及南部区域,10:00以后太湖西南部沿岸的叶绿素a浓度显著降低.这种先分类后反演的方法对于二类水体的模型反演精度的提高具有重要作用.     

B－P网络用于环境质量分类研究

用全国２５个湖泊的水质指标资料作为训练样本，应用人工神经网络Ｂ－Ｐ算法，分别建立了３参数和４参数的水质分类模型。并用所建立的模型对６个湖泊水质进行分类检验效果。结果表明Ｂ－Ｐ模型应用于环境质量分类是可行的。且由于Ｂ－Ｐ网络有自学习、自适应的能力，因而与其它的环境质量分类法相比具有优越性。     

隧道围岩Vague集可拓安全评估模型

为提高公路隧道围岩分级的准确性,丰富风险评级方法,首先基于可拓物元理论,对实测数据进行无量纲化处理并转化为Vague集.然后通过AHP法和熵权法得到主客观权重,进而构建区间权重并按照隶属度与非隶属度距离最大化原则进行优化.最后建立基于Vague集的可拓安全评估模型,并结合工程项目进行案例分析,结果与实际监测数据一致.     

珠江三角洲秋季典型O3污染的气象条件及贡献量化

利用自组织神经网络分析法,对珠江三角洲2013~2017年秋季海平面气压和全风速场进行大样本客观分型.结果表明,影响O₃的天气类型共有7种,由污染程度高低分为高、中、低3类,对应的平均O₃超标率分别为32.3%, 12.0%和4.2%.对比2017年和2016年秋季O₃污染天气分型下促发O₃污染的气象因子差异,2017年秋季高污染型O₃天气形势出现的天数比2016年增多,且中污染型天气形势出现时,2017年的局地污染气象条件更为不利.采用WRF-CAMx模式通过改变气象场输入来量化气象条件贡献,并用实测变化减去气象变化以推算排放贡献.结果表明,气象条件变化导致O₃浓度上升的贡献率为29.8%,而排放的变化引起O₃浓度下降的贡献率为7.1%.在2017年秋季开展的O₃污染防治专项行动指导下的珠江三角洲O₃前体物控制措施,有效缓解了部分由于不利气象条件而引起的O₃污染浓度上升.不利气象条件是导致2017年秋季O₃浓度升高的重要成因.