基于熵权的集对分析法在水资源可持续利用评价中的应用——以塔里木河三源流地区为例

以塔里木河三源流地区为研究对象,结合研究区2004-2007年统计年鉴资料,从社会经济、水资源和生态环境三方面构建水资源可持续利用评价指标体系,采用集对分析和熵权法,对研究区水资源可持续利用进行评价.结果表明:塔里木河三源流地区水资源开发利用已基本达到饱和,水资源可持续利用呈现弱不可持续状态,具体表现为和田地区(Ⅲ)阿克苏地区(Ⅲ)喀什地区(Ⅲ).基于评价结果提出了一些促进三源流地区水资源可持续利用的措施.     

Fuzzy set approaches to classification of rock masses

Rock mass classification is analogous to multi-feature pattern recognition problem. The objective is to assign a rock mass to one of the pre-defined classes using a given set of criteria. This process involves a number of subjective uncertainties stemming from: (a) qualitative (linguistic) criteria; (b) sharp class boundaries; (c) fixed rating (or weight) scales; and (d) variable input reliability. Fuzzy set theory enables a soft approach to account for these uncertainties by allowing the expert to participate in this process in several ways. Hence, this study was designed to investigate the earlier fuzzy rock mass classification attempts and to devise improved methodologies to utilize the theory more accurately and efficiently. As in the earlier studies, the Rock Mass Rating (RMR) system was adopted as a reference conventional classification system because of its simple linear aggregation.

The proposed classification approach is based on the concept of partial fuzzy sets representing the variable importance or recognition power of each criterion in the universal domain of rock mass quality. The method enables one to evaluate rock mass quality using any set of criteria, and it is easy to implement. To reduce uncertainties due to project- and lithology-dependent variations, partial membership functions were formulated considering shallow (<200 m) tunneling in granitic rock masses. This facilitated a detailed expression of the variations in the classification power of each criterion along the corresponding universal domains. The binary relationship tables generated using these functions were processed not to derive a single class but rather to plot criterion contribution trends (stacked area graphs) and belief surface contours, which proved to be very satisfactory in difficult decision situations. Four input scenarios were selected to demonstrate the efficiency of the proposed approach in different situations and with reference to the earlier approaches.     

计算样本相似性在油气预测中的应用

介绍了计算样本相似性方法在油气预测中的应用的基本原理和技术。提出了利用参数的显著性进行参数优化和加权的方法。该方法简单、实用，并在塔北某区的应用取得了满意结果。由此证明了该方法的有效性及在综合信息预测工作中进行参数优化和加权工作的重要性。     

A GEOGRAPHICAL ANALYSIS OF IMAGE INTERPRETATION METHODS

Image interpretation methods, procedures for relating image pattern to ground conditions, are essential to our use of remote sensing imagery. These methods can be analyzed in respect to the role of ancillary information in the image interpretation process. In general, those procedures that are comparatively independent of ancillary information can be applied in varied geographic settings. Because almost all interpretation procedures depend to some extent upon ancillary information, a detailed and integrated knowledge of the cultural and physical landscape is a prerequisite for both manual and automated interpretation procedures.     

基于随机事件集的风暴潮重现期计算的合理性研究

为了研究随机事件集实际应用于计算南通如东岸段风暴增水的合理性问题,基于ADCIRC模型模拟影响南通如东岸段基于随机事件集的风暴增水,然后利用P-Ⅲ型曲线对年增水极值进行拟合,得到各典型重现期下的增水值,并与由28年历史资料拟合得到的各重现期下增水值进行对比。结果表明,在历史资料长度所及的28年以及以下重现期,基于随机事件集模拟得到的重现期增水值与基于历史实测资料的比较符合,说明随机事件集的结果在低重现期情况下结果良好。对于高重现期增水值,基于随机事件集的拟合结果显著大于基于历史资料的拟合结果。由于历史资料时间太短,不足以发生非常极端的风暴潮事件,故对高重现期的拟合结果难以保证可信度,所以不能排除随机事件集拟合结果的合理性。随机事件集在南通如东岸段对低重现期增水估计准确,同时也能较充分地估计高重现期增水,在目前缺乏百年千年时间尺度的实测资料的情况下,不失为一种良好的风暴增水重现期计算工具。在全球变暖情况下,超强台风出现几率大大增加,典型重现期增水值也会相应提高,为了预防风暴灾害的侵袭,需要加深加固海堤、江堤等海岸工程。     

基于粗规则对象空间信息表的最小规则集生成

提出了粗规则对象空间信息表的概念、相关概念和性质，并给出实现粗规则最小化算法。基于此，编写了相应的计算机程序，并对一个地理信息系统实例规则进行了研究。实例表明运行可靠、结果正确，可应用于智能决策信息系统的知识库优化。     

基于MPI并行算法的电阻率法多种装置数据的三维联合反演

由于地表电阻率法受到浅地表局部异常体的干扰,反演精度受到影响,井中装置数据资料参与反演虽然可以减小浅地表局部异常体的干扰,但是由于钻井位置的局限性,数据量得不到保障,也会导致反演精度降低.为此,本文开发了一套结合地表、地-井、井-地和井-井装置数据的三维联合反演算法.首先,利用有限差分法实现正演模拟,采用非线性共轭梯度法（NLCG）恢复电阻率结构;其次,调用Message Passing Interface（MPI）函数库加速正演模拟和灵敏度矩阵运算,当开辟12个进程时,反演程序获得最大加速比4.51;最后,通过合成数据和实测数据算例证明该反演算法的有效性,也证实了多种装置组合数据体反演结果明显优于单一地表装置数据体反演结果.

     

基于FCM算法的粗糙集理论在边坡稳定性影响因素敏感性分析中的应用

结合粗糙集理论与模糊C-均值（FCM）算法,提出一种边坡稳定性影响因素敏感性分析新方法。将边坡稳定性影响因素敏感性分析问题转化为粗糙集理论中的属性重要性评价问题,采用FCM算法离散连续属性数据,给出敏感性分析的具体算法。以圆弧型破坏边坡为例,对影响边坡稳定性的单因素与多因素敏感性进行分析,证明了该方法的可行性和有效性。     

The role of global dietary transitions for safeguarding biodiversity

Diets lower in meat could reduce agricultural expansion and intensification thereby reducing biodiversity impacts. However, land use requirements, associated with alternate diets, in biodiverse regions across different taxa are not fully understood. We use a spatially explicit global food and land system model to address this gap. We quantify land-use change in locations important for biodiversity across taxa and find diets low in animal products reduce agricultural expansion and intensity in regions with high biodiversity. Reducing ruminant meat consumption alone however was not sufficient to reduce fertiliser and irrigation application in biodiverse locations. The results differed according to taxa, emphasising that land-use change effects on biodiversity will be taxon specific. The links shown between global meat consumption and agricultural expansion and intensification in the biodiverse regions of the world indicates the potential to help safeguard biodiverse natural ecosystems through dietary change.     

基于Monte Carlo-BP神经网络TBM掘进速度预测

预测隧道工程中TBM掘进速度,主要有完全经验的、半理论半经验的模型和人工智能等方法,所用参数均为确定性的,未考虑参数存在的随机性,故导致预测结果的不准确性。基于此,提出了Monte Carlo-BP神经网络TBM掘进速度预测模型,着重考虑了一些重要输入参数的随机性, 其中输入参数重要性的大小通过粗糙集进行计算排序。采用Monte Carlo产生随机数时,由于参量的样本数据的有限,分布函数均采用阶梯形经验分布函数。如果采用的数据是来自不同类型的 TBM,则应当考虑机器性能参数,并重新对参数重要性进行排序。实例计算表明,Monte Carlo-BP神经网络模型预测结果和实测值总体趋势和均值比较一致。