预测和控制深基坑变形的抗隆起稳定系数法

本文讨论了在软土基坑工程设计施工中所涉及到的抗隆起稳定系数的合理计算方法；通过对大量上海深基坑工程的计算分析并结合现场实测数据论证了利用抗隆起稳定系数进行基坑变形预测和控制的合理性，并提出了适用稳定系数法控制基坑变形的设计计算预测公式：δh/H～Ks关系式。     

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。     

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.     

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     

土地利用动态管理系统研发中的若干问题研究

土地利用管理是土地管理的核心内容之一。土地利用动态变化加剧了土地管理的压力，建设具有辅助决策功能的土地利用动态信息系统是大势所趋。本文在阐述建设此系统的重要意义基础上，讨论了系统研发中几个关键问题，即“动态”管理的实现、空间数据和属性数据集成管理和决策的初步实现等。本文提出的解决方法，对于类似土地信息系统的建设具有普遍的指导意义。     

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.     

Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area".     

Use of time series analysis for the identification of tidal effect on groundwater in the coastal area of Kimje, Korea

The purpose of this study is to apply time series analysis to investigate whether the groundwater quality in the coastal area is affected by the tide. Continuous and regular in situ monitoring data of electrical conductivity (EC) and groundwater level, and tidal level data measured by the National Oceanographic Research Institute were used for the time series analysis. Through the time series analysis, it is known that EC and groundwater level conspicuously fluctuate with two periodicities (15.4 and 0.52-day), which is very similar to those of the tide. Also the behaviors of their fluctuations vary in accordance with the tidal period. These indicate that the groundwater quality has been mainly controlled by the tidal level, and the strength of tidal effect on the groundwater quality is different according to the tidal period.     

Data assimilation (4D-VAR) to forecast flood in shallow-waters with sediment erosion

In this paper, the four-dimensional variational data assimilation technique (4D-VAR) is presented as a tool to forecast floods. Our study is limited to purely hydrological flows and supposes that the weather, here a big rain, has been already forecasted by meteorological services. The technique consists in minimizing, in the sense of Lagrange, the cost function: a measure of the difference between calculated data and available observations, here the water level. This is done under constraints that are the equations of the physical model. In our case, we modified the shallow-water equations to include a simplified sediment transport model. The steepest descent algorithm is then used to find the minimum. This is made possible because we can compute analytically the gradient of the cost function by using the adjoint equations of the model. As an application of the 4D-VAR technique, the overflowing of the Chicoutimi River at the Chute-Garneau dam, during the 1996 flood, is investigated. It is found that the 4D-VAR method reduces the error in the water height forecast even when the erosion model is not activated. In terms of Lyapunov exponents, we estimate the predictability horizon of such an event to be about half-an-hour after a big rain. However, this limit of predictability can be increased by using more observations or by using a finer computational grid.