地下洞室群现场作业风险分析与向量模型

为了厘清地下洞室群施工现场作业风险因素间的关联效应,利用AMOS软件构建施工风险影响因素结构方程模型,对已建模型进行验证性因子分析,利用计算得出的M.I值对已构建的模型进行修正。根据所得模型计算出的路径系数和方差,对地下洞室群施工风险因素关联效应进行分析。结果表明:规章制度建设及执行、围岩稳定性、进场设备优良率、工人技术等级、工人逆反心理以及消极作业分别为各风险层次中影响最大的风险因素,且各风险层次间的关联效应表现出耦合关系;现场作业风险因素可分为现场主体因素、现场客体因素和现场控制因素3大类,它们之间成向量关系,以此为基础建立向量模型,可清晰直观地展示3大因素之间的主次关系。     

石家庄近地层风场辐合与重污染关系研究

石家庄多发生以风场辐合为特征的局地污染事件,客观判识辐合区和辐合过程强弱对重污染预报指示意义重大.为研究石家庄风场辐合特征及其对局地污染的贡献,更好地为区域重污染天气预报预警提供客观指标参考,在引入固定高度层内矢量通风系数物理量的基础上,结合箱体模型提出一种定量计算近地层风场辐合强度系数的方法,并利用ERA-Interim再分析资料计算了京津冀地区矢量通风系数以及辐合强度系数,对2016年9月—2019年3月期间10次典型风场辐合污染过程进行分析,探讨了风场辐合形成的原因和对污染贡献的关系.结果表明:①受地形影响,石家庄易出现近地层风向、风速辐合,均可归结为区域内输入通风量大于输出通风量,西部太行山地形的阻挡加重了其辐合程度.②风场辐合使空气中的水汽和污染物汇聚,为二次反应提供高湿条件,这种正反馈作用促发PM_2.5爆发性增长,期间ρ（PM_2.5）平均每小时可上升25 μg/m3.③石家庄近地层辐合强度系数在26以上时对污染贡献作用明显,辐合区污染强度比周边偏高1~2个等级,ρ（PM_2.5）比区域背景值偏高85%~200%.研究显示,风场辐合对重污染贡献明显,是形成局部污染物浓度偏高、PM_2.5爆发性增长的重要气象指标.      

我国流域生态补偿研究进展

分析了流域生态补偿的概念和实施流域生态补偿应遵循的六大原则,从界定流域生态补偿主客体、甄选流域生态补偿的功能载体和确定补偿标准三方面详细阐述了如何实施流域生态补偿机制,并分析了该领域在我国现阶段的研究特点,明确提出今后的主要研究方向:流域生态补偿的量化研究、探寻流域生态补偿的法律依据和构建流域生态补偿管理体系。     

Soil Moisture Data Assimilation Using Support Vector Machines and Ensemble Kalman Filter1

Abstract: A hybrid data assimilation (DA) methodology that combines two state‐of‐the‐art techniques, support vector machines (SVMs) and ensemble Kalman filter (EnKF), is applied for soil moisture DA in this work. The SVM methodology provides a statistically sound and robust approach to solving the inverse problem, and thus to building statistical models. EnKF is an extension of the Kalman Filter (KF), a well‐known tool in prediction updating. In the present research, ground measurements were used to build a SVM‐type soil moisture predictor. Subsequent observations and their statistics were assimilated to update predictions from the SVM model by coupling it with EnKF. In this way, both model predictions and ground data, as well as their statistics, are fused thus minimizing the prediction error and making the predictions and observations statistically consistent. The results are shown for two approaches; one in which update is done at every time step and the other which assumes that data is only available at alternate time steps (in window of 10 time steps) and hence update is performed at those occasions. The SVM‐EnKF coupling is shown to improve soil moisture forecasts in an example using data from the Soil Climate Analysis Network site at Ames, Iowa.     

基于可缩放矢量图形(SVG)的应急救援网络地理信息系统(WebGIS)构建

地理信息系统(GIS)平台采购成本高、信息发布缺乏有效通用方法,使利用其进行二次开发的模式难以符合一些应急救援信息系统建设实际需要。万维网联盟制定的可缩放矢量图形(SVG)为解决这些问题提供了契机。在评价SVG作为下一代网络矢量图形开放标准的优势基础上,对如何以SVG为基础构建应急救援网络地理信息系统(WebGIS)进行了深入分析;介绍了用SVG设计应急救援专题电子地图的特色与实现方法,然后针对该专题对基于SVG的WebGIS架构进行了剖析与优化设计,并引入了重大危险源元数据库与应急救援方法库两个概念,最后给出一个重大危险源应急救援信息系统的框架。以该技术建立的应急救援WebGIS无平台依赖性,网络发布与交互便捷,开发具有伸缩性,适合不同层次应用需求。     

pUCD-recA重组发光菌构建及对遗传毒性污染物响应作用

基因重组发光菌在水质毒性的评价中具有重要的作用,本研究从分析污染物毒性损伤的机制出发,构建新型pUCD-recA基因重组发光菌. 用PCR法从大肠杆菌W3110中扩增recA基因,将其与pGEM-T easy载体连接后测序.测序正确的recA片段及pUCD615载体均用BamHⅠ、EcoRⅠ双酶切,连接后电转化导入宿主菌JM109.挑取克隆,提取质粒用PCR鉴定,阳性克隆再进行测序.将构建成功的pUCD-recA载体转化入大肠杆菌RFM443,加入相应的遗传毒性污染物,观察发光响应作用.结果表明,recA基因PCR扩增出的片段为293　bp,测序结果与GenBank中的recA序列进行BLAST比对,同源性为99％,表明扩增序列正确.与pUCD615载体连接后的测序结果表明,recA基因已正确地插入到pUCD615的多克隆位点,方向和读码框正确,重组发光菌载体构建成功.将构建好的重组载体转化入RFM443宿主菌,加入遗传毒性污染物观察响应效果.丝裂霉素C（MMC）对pUCDrecA重组发光菌诱导效果最好,0.01 mg/L即可有很好的响应曲线;N′-甲基N′-硝基亚硝基胍（MNNG）则在50～100 mg/L时可发挥最佳响应作用.     

时间序列相似匹配算法在数字海洋风暴潮辅助决策系统中的应用

在现有风暴潮灾害研究基础上,结合风暴潮数据库特点,提出基于斜率分段过滤的相似匹配算法,定义了风暴潮辅助决策、风暴潮序列以及应用于风暴潮序列的相似匹配度量模型。由于海洋数据具有海量等特性,本文先用基于斜率特征向量分段算法进行过滤,再用风暴潮相似度量模型提取相似序列,依据提取的序列在风暴潮数据库中的记录信息,进行风暴潮辅助决策,为风暴潮灾害乃至整个海洋数据的研究提供了较好的技术支持。     

基于扇区动态复杂性因素的航空管制员工作负荷计算

为了更加准确地计算和预测航空管制员的工作负荷,利用雷达管制模拟试验获取的数据,分别采用线性回归、神经网络的非线性回归和基于神经网络的支持向量机方法,建立了基于扇区复杂性因素的管制员工作负荷实时计算模型。结果表明,这3种模型的绝对误差平均值分别为0.969、1.049、0.240;相对误差平均值分别为16.667%、17.979%、6.229%;均方根误差分别为0.186、0.206、0.114。另外,若采用5%作为基准精度,基于神经网络的支持向量机模型可以将相对误差控制在-0.5%~0.5%,表现出较强的误差控制能力。研究表明,可以采用扇区动态复杂性因素来计算管制员的工作负荷,相比线性回归、神经网络的非线性回归方法,基于神经网络的支持向量机方法对管制员工作负荷的计算有更高的精度。     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R²), Nash–Sutcliff efficiency (NSE), relative efficiency criteria (d_rel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Development of HGAPSO-SVR corrosion prediction approach for offshore oil and gas pipelines

Managing the oil and gas pipelines against corrosion is one of the major challenges of the oil and gas sector because of the complexities associated with the initiation, stabilization, and growth of the corrosion defects. The present research attempts to develop a model for predicting the maximum depth of pitting corrosion in oil and gas pipelines using SVM algorithm. In order to improve the SVM performance, Hybrid PSO and GA was utilized. Monte Carlo simulation was used to determine the time lapse for the pit depth growth. In order to implement the above modeling approaches and to prove their efficiency and accuracy against a large database, a total of 340 data samples for corrosion depth and rate are retrieved from the Iranian Oilfields. The performance of the new algorithm shows that it has higher stability and accuracy. In addition, the forecasting results of the new algorithm are compared with the 11 intelligent optimization algorithms, it shows that the novel hybrid algorithm has higher accuracy, better generalization ability, and stronger robustness. The coefficient of determination (R²) value in the testing phase for SVM-HGAPSO was estimated by 0.99. Proposed hybrid model and Monte-Carlo simulations pitting corrosion based on Poisson square wave process have been used to predict the time evolution of the mean value of the pit depth distribution for different categories of maximum pitting rates (low, moderate, high and sever). The models was validated with 4 field data for each of the pitting corrosion categories and the results agreed well. The pipelines under severe pitting corrosion rate were, more conservatively predicted by HGAPSO-SVR than those under low, moderate and high pitting corrosion rates. The results obtained demonstrate the potentials of this technique for the integrity management of corroded aged pipelines.