人工神经网络在岩体质量分级中的应用

结合四川省金沙江某水电站工程实例，应用BP人工神经网络方法建立3层BP网络模型，选取岩石单轴抗压强度等6个影响因素为输入变量，对坝基复杂岩体进行质量分级。通过机算机Visual C 语言编程实现神经网络模型，进行网络的学习和运算。以神经网络合理结构分析方法选取合理结构，确定合理隐层单元的数量，提高网络测试的精度。对测试结果的分析发现，经过优化的BP网络模型经多次学习后，测试精度提高，结果可靠，取得较好的实际应用效果。     

水质大涡模拟数学模型研究

建立了基于弱可压缩流基础上的水质大涡模拟数学模型及污染物质扩散系数与平均运动的关系。数值计算采用有限体积法和预测校正法,固壁边界条件采用"部分滑移条件"。并应用该模型对重庆嘉陵江、长江两江交汇段的流场,污染物质浓度分布进行了计算,计算结果与实测值比较吻合。研究表明,横向扩散与流动状态有关,当支流流量相对较小时,横向扩散较弱,形成岸边污染带,当支流流量相对较大时,横向扩散较强,污染带向江心移动。     

从全国水环境数据库看中国水环境状况

以全国水环境数据库的监测记录为第一手资料,以各大流域片为单位,从宏观上介绍了各大流域片的水质总体概况,包括各类水质所占比例、污染水质的超标物类型等,基本反映了我国各大流域在20世纪90年代的水质状况以及水环境的时空变化特征.总的来说,我国的水环境状况不容乐观,水质污染比较严重,尤其是我国北方的流域片.     

澜沧江某水电站左岸地下式厂房岩体质量分级

通过大量的现场调查数据与统计工作结果，采用了多种岩体质量分级评价方案，对澜沧江某水电站左岸地下式厂房岩体质量进行了精细的评价，最后将各种结果进行对比分析，得到一个合理、科学的综合岩体质量综合评价分级。并根据评价结果，作出了研究区坝址左岸地下式厂房岩体在不同高程的岩体质量分区图，为下一步的参数选取工作奠定了必要的基础。     

RIVER SYSTEM AND DISTURBANCE FACTORS IN THE LAKE TOBA

Situated at an elevation of 905 m above sea level in the Province of North Sumatra, Lake Toba and its surrounding landscapes are regarded as a natural heritage in a certain extent, as a quoted national treasure. Unfortunately degradation of the land and water resources in the watershed along Lake Toba is taking place at an alarming and totally unacceptable rate. The quality of the lake is partly depended on input the quality of the rivers. When compared to the control area the water quality that influenced by the piggeries are highly polluted. It can be concluded that the Salbe River at the downstream of the piggeries has been polluted and apparently it is a serious problem to the catchment area management. It should be noted that the polluted river would influence the water quality of the Lake Toba. Based on calculation, the permissible BOD5 according to B- river standard is 238 mg/L, it means the river still in B standard but the condition and quality are decreasing continuously. Following the Indonesian health standard the permissible BOD is - 461 mg/L. It means BOD in the river should be reduced 461 mg/L or clean program is needed.     

中巴地球资源一号卫星CCD图像质量评价和交叉定标研究

使用定量分析方法评价了中巴地球资源卫星CCD相机遥感数据质量 ,在此基础上利用LANDSAT 7ETM 对CCD的对应波段进行交叉定标计算 ,得到了一组CCD的参考定标系数。在遥感数据质量评价研究中 ,使用统计分析方法计算得到了CCD相机图像数据的条纹强度 ;应用结构函数方法计算获得了CCD的噪声数据 ;通过功率谱计算 ,对比分析了CCD和LANDSAT 7ETM 对应波段图像空间纹理特征。该文通过交叉定标计算 ,得到了CCD相机前 4个波段的参考定标系数、动态范围和噪声等效反射率等参考指标 ,并对这组定标系数进行了验证 ,从验证结果中发现CCD1,2 ,3和LANDSAT 7ETM 对应波段反射率最大相差 1 98% ,最小0 0 3% ,平均差要低于 1 31% ,而波段 4最大也仅相差 4 4 1% ,平均差 3 0 2 % ,证明计算得到的交叉定标系数具有较高的参考价值     

基于扫描矢量化地图数据生产的数据质量控制

概述了基于扫描矢量化GIS数据的生产过程 ,介绍了扫描矢量化质量控制的策略和方法 ,在地图扫描阶段采用栅格图纠正的方法对地图的变形进行纠正 ,以提高矢量化原图的质量 ,在基于扫描矢量化GIS数据的生产过程中 ,可从算法和软件使用工艺方面提高数据的质量     

The influence of land use change on karst water quality of Shuicheng Basin in Guizhou Province

The influence of land use and land cover on ecological environment is a focus of global change research. The paper chooses an industrial city-Shuicheng in Guizhou Province-as a study area because the karst water quality around the city is deteriorating with land use and land cover change. The natural susceptibility of karst water system is an important factor leading to karst water pollution. But land use and land cover change is also a main factor according to the chemical analysis of karst water quality and land use change. So it is a good way to protect karst water through rational planning and managing of land use and land cover.     

Water Quality Impacts of Climate and Land Use Changes in Southeastern Pennsylvania*

This study investigates potential changes in nitrogen and phosphorus loads under a warmer and wetter climate, urban growth, and combined changes in the Conestoga River Basin and its five subbasins in southeastern Pennsylvania. A GIS‐based hydrochemical model was employed for assessing the sensitivity of the basins to the projected changes in 2030. Under the HadCM2 climate change scenario, mean annual nitrogen and phosphorus loads are expected to increase, with great increases in spring but slight decreases in fall primarily because of changes in monthly precipitation. When climate change and urbanization occur concurrently, mean annual nitrogen loads further increase by 50% in the most urbanizing subbasin. Point source nitrogen control could mitigate negative effects of climate and land use changes, reducing mean annual nitrogen loads to the contemporary baseline level.     

Assessing the Comparability of Ammonium,Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters’ geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA erformed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.