基于贝叶斯网络分类的土壤盐渍化遥感监测

贝叶斯网络是一种将贝叶斯概率方法和有向无环图的网络拓扑结构有机结合的概率模型。采用贝叶斯网络分类对具有典型干旱特征的库车县土壤盐渍化情况进行监测，首先应用条件独立性测试原理建立贝叶斯网络结构，把研究区遥感数据进行离散化，然后应用贝叶斯定理作为分类原则，将每个像元分为像元最大概率的类别。研究结果表明该方法分类6种地类的整体分类精度达到96％，为该区盐渍地面积、空间分布等特征监测提供了较好的依据。     

海洋环境中材料腐蚀数据采集处理网络系统的研究

介绍了利用电话公网为传输栽体建立以海洋腐蚀与防护国防科技重点实验室为中心辐射青岛、厦门和三亚3个海水试验站的材料腐蚀数据采集计算机网络平台。研发了具有自主知识产权的多通道电偶腐蚀数据自动采集系统,将该系统与网络平台相结合可实现远程实验数据自动采集。同时为了有效地管理、存储和实现数据的共享,研究建立了海洋环境材料腐蚀与防护数据库,收集和整理了大量的材料腐蚀数据。在分析研究大量腐蚀数据的基础上建立了误差反传（BP）人工神经网络预测模型和灰色GM（1,1）腐蚀预测模型。从而形成一套较完整的数据采集、处理和分析网络系统。     

区域空气监测网络建设策略与实践

根据区域空气质量管理的发展形势,提出建设区域空气自动监测网络的思路和建设内容,探索建立区域空气监测QA/QC技术体系的策略与解决方案。     

2015—2019年汾渭平原臭氧污染状况分析

基于全国空气质量监测网数据,分析了2015—2019年汾渭平原11个城市臭氧(O₃)污染状况。结果表明:2015—2019年,汾渭平原11个城市O₃平均浓度总体呈升高趋势,年平均升高12.2μg/m³,其中,2017—2019年均超过二级标准限值(160μg/m³)。O₃单项污染物的空气质量分指数占空气质量指数的比例逐年升高,O₃超标使汾渭平原2015—2019年各年度空气质量优良天数比例分别减少了1.4、5.4、13.0、11.1、14.4个百分点。O₃浓度呈春夏季(5—9月)高、秋冬季(11—12月)低的特点,其中,5—9月O₃超标天数占全部O₃超标天数的97%以上。各年度O₃日最大8小时平均质量浓度(O₃-8 h)的最大值分别为152、176、224、195、202μg/m³,均出现在5—7月。O₃...     

我国生态环境监测网络建设成效与展望

2015年中央全面深化改革领导小组审议通过《生态环境监测网络建设方案》,推动全国生态环境监测网络建设取得重大成就.对照《生态环境监测网络建设方案》提出的"全面设点,完善生态环境监测网络"方面的任务与要求,对生态环境监测网络建设成效进行客观评估,并对生态环境监测面临的形势与不足进行了深入剖析.针对加强生态环境监测网络建设...     

基于BP神经网络的藻类水华预测模型研究

以宁波大学校内池塘2009年3—10月间30周的监测数据为基础,运用BP人工神经网络方法构建预测模型,探求颤藻生物量与总氮、总磷、透明度等6项环境因子之间的关系,选出最佳预测模型,并对模型进行敏感度分析。结果显示:①BP神经网络模型对颤藻生物量预测值与实测值之间拟合程度良好,相关系数达到了0.984,说明BP神经网络模型可以用于水体中藻类水华的短期预测。②通过对构建的BP神经网络模型进行敏感度分析,阐明了宁波大学校内池塘藻类水华的主要驱动因素,并指出控制水体的pH是宁波大学校内池塘藻类水华防治工作的重点。     

白洋淀养殖区春季沉积物垂向微生物群落特征及驱动因素

结合16S rRNA高通量测序技术,对春季白洋淀养殖区(后塘和西李庄)沉积物垂向细菌群落演变特征及影响因素进行解析.结果表明:春季白洋淀沉积物环境因子(硝态氮、铵态氮、溶解性总磷、紫外光谱指数、三维荧光光谱指数和荧光组分强度)均呈现显著差异(p<0.05),后塘采样点的硝态氮和铵态氮含量明显高于西李庄采样点;高通量测序得到23527个OTU,共分为11个主要门类,其中,变形菌门占比最大,达到15.33%～50.59%;α-多样性显示除辛普森指数和覆盖率外,ACE指数、Chao指数及香农指数呈现显著差异(p<0.05),并且在后塘采样点高于西李庄;主坐标分析表明,后塘与西李庄采样点沉积物细菌群落存在显著差异,与Adonis分析的结果相一致(p<0.01);RDA分析发现,硝态氮和铵态氮是驱动沉积物细菌群落结构演变的主要环境因素;物种间网络分析显示,春季后塘和西李庄都为8个主要的模块,西李庄正向相关的边所占比例高达84.79%,明显高于后塘的62.82%;西李庄有244个关键物种OTU,也高于后塘的59个关键物种OTU;相关性分析得出两个采样点在主要属和模块上都存在差异,...     

Internal corrosion hazard assessment of oil & gas pipelines using Bayesian belief network model

A substantial amount of oil & gas products are transported and distributed via pipelines, which can stretch for thousands of kilometers. In British Columbia (BC), Canada, alone there are over 40,000 km of pipelines currently being operated. Because of the adverse environmental impact, public outrage and significant financial losses, the integrity of the pipelines is essential. More than 37 pipe failures per year occur in BC causing liquid spills and gas releases, damaging both property and environment. BC oil & gas commission (BCOGS) has indicated metal loss due to internal corrosion as one of the primary causes of these failures. Therefore, it is of a paramount importance to timely identify pipelines subjected to severe internal corrosion in order to improve corrosion mitigation and pipeline maintenance strategies, thus minimizing the likelihood of failure. To accomplish this task, this paper presents a Bayesian belief network (BBN)-based probabilistic internal corrosion hazard assessment approach for oil & gas pipelines. A cause-effect BBN model has been developed by considering various information, such as analytical corrosion models, expert knowledge and published literature. Multiple corrosion models and failure pressure models have been incorporated into a single flexible network to estimate corrosion defects and associated probability of failure (PoF). This paper also explores the influence of fluid composition and operating conditions on the corrosion rate and PoF. To demonstrate the application of the BBN model, a case study of the Northeastern BC oil & gas pipeline infrastructure is presented. Based on the pipeline's mechanical characteristics and operating conditions, spatial and probabilistic distributions of corrosion defect and PoF have been obtained and visualized with the aid of the Geographic Information System (GIS). The developed BBN model can identify vulnerable pipeline sections and rank them accordingly to enhance the informed decision-making process.     

A neuro-fuzzy algorithm for assessment of health,safety, environment and ergonomics in a large petrochemical plant

Process plants such as petrochemical units have been continuously trying to improve Health, Safety, Environment and Ergonomics (HSEE) programs. This study proposes an adaptive network-based fuzzy inference system (ANFIS) for assessment of HSEE programs in a petrochemical plant. The proposed neuro-fuzzy approach is applied to a set of operators in the petrochemical unit to show its applicability and superiority. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and accomplishment of HSEE programs is used as output for the algorithm. Moreover, this algorithm is used to rank operators performance with respect to HSEE. Finally, the algorithm identifies efficient operators with respect to HSEE. This is the first study that introduces an intelligence algorithm for assessment and improvement of HSEE program in a petrochemical plant.     

非线性理论在开采沉陷中的应用

简要介绍了近年来把非线性理论应用于矿山开采沉陷领域的一些研究成果,主要包括两大部分：（１）神经网络理论在开采沉陷中的应用;（２）离散单元法在开采沉陷中的应用