纵向弥散作用与渗透介质非均质性定量关系的模拟研究

在地下水污染模拟预报中,弥散参数是很难确定的一个模型参数。因实验室小尺度弥散规律一般不能用于大尺度弥散过程,而野外示踪试验却耗资大、周期长,限制了其实用性。文中利用随机数值模拟手段、基于随机理论的蒙特卡罗方法及序贯高斯模拟技术来生成渗透系数随机场,并研究渗透系数对数场的方差、相关长度以及变异函数类型在不同尺度上对纵向弥散度的影响,进而建立纵向弥散度与随机分布渗透系数场的方差和相关长度的统计定量关系,并与Gelhar理论计算结果进行比较。数值模拟结果表明,经过一定迁移距离后纵向弥散度与随机分布渗透系数对数场的方差和相关长度具有良好的线性统计关系,与Gelhar理论公式表达的关系类型类似。但对于较大的方差,纵向弥散度模拟结果明显大于Gelhar理论计算值,而对于较大相关长度在迁移距离不很大时,纵向弥散度模拟结果明显小于Gelhar理论计算值。本研究可为野外大尺度地下水污染预报模型中水动力弥散参数的确定提供方法借鉴。     

类多变量输出误差系统的耦合多新息辨识方法

辅助模型辨识思想、多新息辨识理论、耦合辨识概念是研究复杂多变量系统辨识的新理念和原理.将它们结合起来研究类多变量输出误差系统的辨识问题,提出了多元辅助模型辨识方法、多元辅助模型多新息辨识方法、变递推间隔多元辅助模型多新息辨识方法.为减小算法的计算量和提高参数估计精度,将系统模型分解为一些子辨识模型,应用辅助模型辨识思想、多新息辨识理论、耦合辨识概念,研究和推导了部分耦合辅助模型辨识方法、部分耦合辅助模型多新息辨识方法.讨论了几个典型辨识算法的计算量,给出了参数估计的计算步骤和计算流程图.     

基于改进麻雀搜索算法优化BP神经网络的PM_(2.5)浓度预测北大核心CSCD

针对传统BP神经网络模型收敛速度慢、易陷入局部极值等问题,本文采用分段线性混沌映射(PWLCM)和萤火虫算法(FA)改进麻雀搜索算法(SSA),并优化BP神经网络模型初始权值和阈值,对西安市PM_(2.5)浓度进行预测。通过比较不同模型预测结果的评价指标,并与性能较优的SSA-BP模型对比,ISSA-BP模型预测结果的RMSE、MAPE、MAE分别下降了3.70、3.73、3.34。试验结果表明,改进后的麻雀搜索算法具有高效的全局最优搜索能力,优化后的ISSA-BP神经网络预测稳定性高,精度优于BP、SSA-BP神经网络模型,可用于预测PM_(2.5)浓度。     

南海海上搜救困难性评价

为保障船舶海上航行安全,结合GIS与模糊层次分析法,从海上搜救的自然环境和人文搜救力量2个方面选取9个因子建立海上搜救困难性评价模型,对南海海上搜救困难性进行评价。结果表明：1）自然环境影响下,南海海域搜救困难性具有“V”字形分布的特点,由东北向西南难度等级逐渐降低。2）在人文搜救力量影响下,南海海域的搜救困难性整体呈“东北－西南条带式”,难度最大的区域主要位于南海东北―西南的连线上,呈不规则环状向连线两侧递减。3）综合两方面因素,南海部分岛屿周围搜救难度偏高,主要受台风等恶劣天气及远离搜救基地的影响,而远海海域搜救难度在整体上为“东北－西南递减式”空间分布格局,最高和较高的区域由研究区东北延伸至西南,然后向西北、东南两侧递减。总体而言,南海研究区中近21.1%的海域搜救难度≥7级,仍需加强对搜救力量的部署与建设。     

基于深度和广度优先算法相结合的闭合环自动搜索方法研究

搜索闭合环、附合路线是测量控制网验前精度评定和粗差探测的重要内容。文中结合数据结构相关知识,在已有的基于深度优先算法搜索闭合环的基础上,提出一种利用深度优先和广度优先算法相结合的自动搜索控制网闭合路线、附合路线的搜索方法,与传统搜索方法相比,搜索速度有很大提高。     

城市消防应急指挥地理信息服务系统开发研究

阐述了GIS(地理信息系统)的概念、特点以及GIS技术应用的范围,分析了城市消防附属设施资源与GIS技术的紧密相关特性。以城市消防部门应用需求为基础,提出了基于GIS技术的城市消防应急指挥地理信息系统结构设计、数据库设计、专题空间地理信息数据设计、功能设计等。最后结合哈尔滨市城市消防应急指挥地理信息系统开发试验,展示了部分项目开发内容。     

虚拟太阳天文台及其发展

伴随着太阳物理研究的深入、观测仪器的发展、观测数据的积累,促使人们去思考这样一个问题:如何使研究者能够方便地检索、分析和使用在不同时间、不同地点、不同波段上观测得到的大量与太阳有关的数据,从而能探索更多悬而未决的科学谜题。这就是虚拟太阳天文台(VSO)项目被提出后得到有关天文台、研究所和大学积极响应并迅速投入运作的原因所在。介绍了虚拟太阳天文台的由来、作用、采用的技术和发展状况等。     

Implementing quality control on a random number stream to improve a stochastic weather generator

For decades, stochastic modellers have used computerized random number generators to produce random numeric sequences fitting a specified statistical distribution. Unfortunately, none of the random number generators we tested satisfactorily produced the target distribution. The result is generated distributions whose mean even diverges from the mean used to generate them, regardless of the length of run. Non‐uniform distributions from short sequences of random numbers are a major problem in stochastic climate generation, because truly uniform distributions are required to produce the intended climate parameter distributions. In order to ensure generation of a representative climate with the stochastic weather generator CLIGEN within a 30‐year run, we tested the climate output resulting from various random number generators. The resulting distributions of climate parameters showed significant departures from the target distributions in all cases. We traced this failure back to the uniform random number generators themselves. This paper proposes a quality control approach to select only those numbers that conform to the expected distribution being retained for subsequent use. The approach is based on goodness‐of‐fit analysis applied to the random numbers generated. Normally distributed deviates are further tested with confidence interval tests on their means and standard deviations. The positive effect of the new approach on the climate characteristics generated and the subsequent deterministic process‐based hydrology and soil erosion modelling are illustrated for four climatologically diverse sites. Copyright © 2007 John Wiley & Sons, Ltd.     

Using stochastic state‐space modelling to specify and reduce hydrograph error

A Bayesian post‐processor is used to generate a representation of the likely hydrograph forecast flow error distribution using raingauge and radar input to a stochastic catchment model and its deterministic equivalent. A hydrograph ensemble is so constructed. Experiments are analysed using the model applied to the River Croal in north‐west England. It is found that for rainfall input to the model having errors less than 3mm h^?1, corresponding to about a 15% error in peak flow, the stochastic model outperforms the deterministic model. The range of hydrographs associated with the different model simulations and the measured hydrographs are compared. The significant improvement possible using a stochastic approach is demonstrated for a specific case study, although the mean hydrograph derived using the stochastic model has an error range associated with it. Copyright © 2007 John Wiley & Sons, Ltd.     

Numerical study of soil heterogeneity effects on contaminant transport in unsaturated soil (model development and validation)

The movement of chemicals through soil to groundwater is a major cause of degradation of water resources. In many cases, serious human and stock health implications are associated with this form of pollution. The study of the effects of different factors involved in transport phenomena can provide valuable information to find the best remediation approaches. Numerical models are increasingly being used for predicting or analyzing solute transport processes in soils and groundwater. This article presents the development of a stochastic finite element model for the simulation of contaminant transport through soils with the main focus being on the incorporation of the effects of soil heterogeneity in the model. The governing equations of contaminant transport are presented. The mathematical framework and the numerical implementation of the model are described. The comparison of the results obtained from the developed stochastic model with those obtained from a deterministic method and some experimental results shows that the stochastic model is capable of predicting the transport of solutes in unsaturated soil with higher accuracy than deterministic one. The importance of the consideration of the effects of soil heterogeneity on contaminant fate is highlighted through a sensitivity analysis regarding the variance of saturated hydraulic conductivity as an index of soil heterogeneity. Copyright © 2011 John Wiley & Sons, Ltd.