港口石油化工码头及其库区灾害事故应急系统研究

通过对石油化工码头及其库区危险因素的分析,提出编制应急预案重点要考虑的问题是:明确重大危险源性质与布局;灾害事故的类型及规模;灾害事故等级的划分;一旦发生灾害事故,谁来指挥,谁来救灾,拿什么救灾,怎么救灾的应急反应机制和整个应急过程的通讯联络。为使应急预案具有可操作性,提出了预案编制要点。石油化工码头及库区灾害事故应急系统除编制灾害事故应急预案外,还应实施由重大危险源电子地图信息管理,油品(化学品)的理化特性、应急处理方法及防护措施基本信息查询,可燃有毒气体泄漏扩散模拟预测和池火热辐射强度模拟预测等组成的危险源管理地理信息系统和灾害事故灾情快速预测系统。     

Field‐Scale Application of Three Types of Neural Networks to Predict Ground‐Water Levels1

Abstract: In this paper, a field‐scale applicability of three forms of artificial neural network algorithms in forecasting short‐term ground‐water levels at specific control points is presented. These algorithms are the feed‐forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground‐water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground‐water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field‐scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored.     

云南省交通事故数据的分析研究

通过对云南省1981—2003年的交通事故统计数据的分析研究,给出了交通事故死亡人数的预测模型。通过与发达国家类似的交通事故历史数据的对比分析,给出以时间和机动车拥有量为自变量、交通事故死亡人数为因变量的简单预测模型,该模型对2004年的交通事故死亡人数的预测是准确的;同时采用该模型预测了云南省交通事故死亡人数的峰值及其年份。结论指出:基于目前的人、车、路和管理水平及发展趋势,云南省的交通事故死亡人数在2013—2018年之间将达到高峰,高峰时的交通事故死亡人数在5528～7369人之间。     

黑龙江省暴雪天气分析和预报技术

分析表明，造成黑龙江省暴雪的低压天气系统不仅来自西北、偏西和偏南方向，而且还有在东北和日本海新生成的，其中又以西南和南来的居多；高空影响系统主要是低槽，其次是低涡，但降暴雪时多数是低涡；造成暴雪的锋面系统以暖锋和锢囚锋为最多。另外，从造成降水的成因和水平锋生这两个角度出发，给出了7个暴雪落区的温压场模型。850hPa温度、850hPa温度平流和拔海高度决定了降水（雨、雪）的性质。     

MULTITEMPORAL SCALE HYDROGRAPH PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

Abstract: An artificial neural network (ANN) provides a mathematically flexible structure to identify complex nonlinear relationship between inputs and outputs. A multilayer perceptron ANN technique with an error back propagation algorithm was applied to a multitime-scale prediction of the stage of a hydro-logically closed lake, Devils Lake (DL), and discharge of the Red River of the North at Grand Forks station (RR-GF) in North Dakota. The modeling exercise used 1 year (2002), 5 years (1998–2002), and 27 years (1975–2002) of data for the daily, weekly, and monthly predictions, respectively. The hydrometeorological data (precipitations P_(t), P_(t-1), P_(t-2), P_(t-3), antecedent runoff/lake stage R_(t-1) and air temperature T_(t) were partitioned for training and for testing to predict the current hydro-graph at the selected DL and RR-GF stations. Performance of ANN was evaluated using three combinations of daily datasets (Input I = P_(t)), P_(t-l), P_(t-2), P_(t-3), T_(t) and R_(t-l); Input II = Input-l less P_(t) P_(t-l), P_(t-2), P_(t-3); and Input III = Input-II less T_(t)). Comparison of the model output using Input I data with the observed values showed average testing prediction efficiency (E) of 86 percent for DL basin and 46 percent for RR-GF basin, and higher efficiency for the daily than monthly simulations.     

ESTIMATING STREAMFLOW AND ASSOCIATED HYDRAULIC GEOMETRY,THE MID‐ATLANTIC REGION,USA1

ABSTRACT: Methods to estimate streamflow and channel hydraulic geometry were developed for unpaged streams in the Mid‐Atlantic Region. Observed mean annual streamflow and associated hydraulic geometry data from 75 gaging stations in the Appalachian Plateau, the Ridge and Valley, and the Piedmont Physiographic Provinces of the Mid‐Atlantic Region were used to develop a set of power functions that relate streamflow to drainage area and hydraulic geometry to streamflow. For all three physiographic provinces, drainage area explained 95 to 98 percent of the variance in mean annual streamflow. Relationships between mean annual streamflow and water surface width and mean flow depth had coefficients of determination that ranged from R²= 0.55 to R²= 0.91, but the coefficient of determination between mean flow velocity and mean annual streamflow was lower (R²= 0.44 to R²= 0.54). The advantages of using the regional regression models to estimate streamflow over a conceptual model or a water balance model are its ease of application and reduced input data needs. The prediction of the regression equations were tested with data collected as part of the U.S. Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). In addition, equations to transfer streamflow from gaged to ungaged streams are presented.     

压入式局部通风工作面风流分布数值模拟研究

笔者应用三维k -ε紊流模型描述压入式局部通风工作面风流的流动过程 ,并采用控制容积法导出了描述流体流动方程的离散化方程式 ;用计算流体力学的方法求得了三维k -ε紊流模型的数值解。采用SIMPLE(压力耦合方程式的半阴解法 )算法解算流场 ;TDMA(三对角线算法 )和Gauss Seidel法结合通过线顺法求解离散方程。利用弱松弛法防止非线性方程组迭代求解过程中的发散现象 ,获得了掘进工作面空间的速度分布 ;并将模拟解算结果与在具有实际尺寸的拱形巷道模型中测得的实验结果进行了对比。数值计算结果与模型实验测定结果非常一致 ,验证了笔者的数学模型和数值模拟方法的正确性。     

道路交通安全研究方法

介绍了交通安全的研究方法和预测模型 ,并对统计分析、模糊数学分析及灰色理论方法进行了比较。在此基础上 ,介绍了适应我国目前交通安全研究现状的交通事故灰色预测研究方法 ,对交通事故发展趋势进行了预测。论文给出了相应的灰色预测模型及预测方法 ,预测结果与实际情况较吻合     

河流水质预测的因素状态网络模型研究

建立了一种河流水质预测的因素状态人工神经网络模型,其算例的计算结果表明,这一种方法有着极大的研究意义和广阔的应用前景。      

以摩托车为主的道路交通噪声预测模型

将摩托车流单独处理,通过理论推导和实测数据回归建立摩托车为主的道路交通噪声预测模型.模型对居住区的交通噪声(Leq)预测精度在 2.5dBA 以内,道路两侧 Leq预测精度更高,大部分测点误差在 1dBA 以内.