BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

采油厂HSE监督检查管理系统创建实践研究

本文根据采油厂生产过程风险特点,以生产单元为对象,通过建立全员参与的HSE监督检查体系,实现对采油厂HSE监督检查标准的统一规范,利用开发的HSE监督检查信息集成平台,创建完成采油厂HSE监督检查管理系统。在大庆油田第五采油厂的应用实践显示,其能为采油厂带来巨大的风险管理效益,是全面落实直线与属地HSE责任的有效载体。对于采油厂有效实施HSE监督检查具有重大意义,是HSE管理制度的一种创新。     

Connecting the Dots: Public Engagement with Environmental Data

Air pollution and other environmental hazards are often imperceptible and need to be made publicly visible. The paper argues for the importance of visualizations in drawing public attention to imperceptible hazards and in providing the public with access to empirical data describing the risks. It also argues for critical inquiry into hazards’ selective visibility as it is produced by visualizations. The impact of visualizations and their selective visibility are considered through the example of a public art project called Particle Falls installed in 2014 in Pittsburgh, a city with a long history of both ignoring air pollution and working to ameliorate this problem. I examine the impact and selective visibility of Particle Falls by considering the underlying production of data, as well as context and support systems for this visualization, and by comparing it with other visualizations of local air quality.     

Quantifying population declines based on presence‐only records for red‐list assessments

Accurate trend estimates are necessary for understanding which species are declining and which are most in need of conservation action. Imperfect species detection may result in unreliable trend estimates because this may lead to the overestimation of declines. Because many management decisions are based on population trend estimates, such biases could have severe consequences for conservation policy. We used an occupancy‐modeling framework to estimate detectability and calculate nationwide population trends for 14 Swiss amphibian species both accounting for and ignoring imperfect detection. Through the application of International Union for Conservation of Nature Red List criteria to the different trend estimates, we assessed whether ignoring imperfect detection could affect conservation policy. Imperfect detection occurred for all species and detection varied substantially among species, which led to the overestimation of population declines when detectability was ignored. Consequently, accounting for imperfect detection lowered the red‐list risk category for 5 of the 14 species assessed. We demonstrate that failing to consider species detectability can have serious consequences for species management and that occupancy modeling provides a flexible framework to account for observation bias and improve assessments of conservation status. A problem inherent to most historical records is that they contain presence‐only data from which only relative declines can be estimated. A move toward the routine recording of nonobservation and absence data is essential if conservation practitioners are to move beyond this toward accurate population trend estimation.     

The Role of Disaggregation of Asset Values in Flood Loss Estimation: A Comparison of Different Modeling Approaches at the Mulde River,Germany

In loss estimation there is a spatial mismatch of hazard data that are commonly modeled on an explicit raster level and exposure data that are often available only for aggregated administrative units. Usually disaggregation methods that use ancillary information to distribute lumped exposure data in a finer spatial resolution help to bridge this gap. However, the actual influence of different mapping techniques and ancillary data on the final loss estimation has not been analyzed yet. In this paper three methods are applied to disaggregate residential building assets using two kinds of land use/land cover (LULC) data. The resulting disaggregated assets are validated and compared using census data of the residential building number on the community and constituency level. In addition, the disaggregated assets are taken to estimate residential building losses due to the flood in August 2002 in 21 municipalities on the River Mulde in Saxony, Germany. Losses are calculated with the help of four loss models. In general, disaggregation helps to decrease the error variance within the loss estimation. It must, however, be stated that the application of sophisticated disaggregation methods does not lead to significant improvements compared to the straightforward binary method. Therefore more effort should instead be put into the provision of high-resolution LULC data. Finally, the remaining uncertainties in loss estimation are high and demand further improvements in all modeling aspects.     

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.     

省级生态保护红线划定实践研究 ——以青岛市为例

在省级及以上禁止开发区、重点生态功能保护区等进行调查分析的基础上,以青岛市为实践区域,与多类规划衔接,划定青岛市省级生态保护红线.青岛市最终划定59处省级生态保护红线区,面积合计1120.65 km2,占国土面积的9.9%.划定的生态保护红线符合确立"三生"空间和落实"多规合一"的要求,有利于改善生态系统服务功能,保障人居环境安全,增强经济社会可持续发展能力,对于青岛市市级生态保护红线的划定和生态安全格局的构建具有非常重要的意义.     

CEMS比对监测数据偏差研究

简述CEMS比对监测技术背景,总结实际监测工作中导致数据偏差的各方面因素,分析问题产生原因并提出解决措施和建议。     

基于遥感监测的黄海绿潮漂移路径及分布面积特征分析

本文利用2008～2012年的多源卫星遥感序列图像,动态监测各年绿潮发生的漂移路径、影响海域面积,得到各年绿潮漂移趋势、分布面积和最大分布面积出现时间。根据监测结果,划分出影响绿潮漂移路径的关键区域,并分析了2008、2009和2010年关键区域内各年绿潮漂移方向和面积变化情况。     

基于DEA的上海市非中心城区工业生态效率研究

运用数据包络分析（DataEnvelopmentanalysis,DEA）的方法,从环境污染和资源消耗两个方面,建立了工业生态效率评价的指标体系,评价了2008年上海市非中心城区各区县的工业生态效率。结果表明,松江区处于上海市工业生态效率的前沿面,9个区县中有2／3区县的工业生态效率优于上海市平均水平。其中,崇明、嘉定和青浦处于规模报酬递增阶段,奉贤、闵行、浦东、金山、宝山则处于规模报酬递减阶段。最后,基于C2R模型对各区县的投入产出情况进行了优化调整,认为削减SO2的排放量和减少能源的消耗应是各区县提高工业生态效率的关键。