基于粗糙集和支持向量机的标准农田地力等级评价

标准农田是耕地的精华,是确保国家粮食安全的关键。科学评价标准农田地力等级对标准农田培肥和土壤改良有着重要意义。将粗糙集(Rough Set,RS)理论和支持向量机(Support Vector Machine,SVM)相结合,提出了基于RS和SVM的标准农田地力等级评价方法,同时,利用遗传算法的并行搜索结构和模拟退火的概率突跳特性,提出了GASA优化SVM参数算法。该方法首先在确定标准农田地力等级评价指标的基础上,利用地力调查样本数据及传统的指数和法评价结果构建RS决策表,应用RS穷尽算法对决策表进行约简,剔除冗余的评价指标,然后用约简后的评价指标作为SVM的输入,运用GASA优化SVM参数算法对SVM进行训练,建立标准农田地力等级的RS-SVM评价模型。应用该方法对温州市鹿城区标准农田地力等级进行评价,与未用RS约简的SVM模型和BP神经网络模型评价结果进行对比,SVM模型和BP神经网络模型的输入指标数均为15个,其评价正确率分别为100%和90%;RS-SVM模型的输入指标数为14个,其评价正确率分别为100%,结果表明,该方法通过RS约简评价指标后,SVM评价精度并没有降低,但降低了SVM输入向量维数和计算复杂度,提高了训练效率;SVM 用于标准农田地力等级评价,具有比BP神经网络更高的评价精度,可有效用于标准农田地力等级评价,为耕地地力评价提供了新方法。     

Sustainability assessment of entire forest value chains: Integrating stakeholder perspectives and indicators in decision support tools

The optimization of value chains is an important process to promote sustainable development, since value chains are closely linked to the satisfaction of human needs and combine different driving forces for environmental change. This article presents a methodological approach for the participatory development of value-chain wide sustainability indicator sets and their integration into a decision support tool in the specific case study of the chain “construction and refurbishment with wood”. There are numerous indicator sets for sustainable development of forests and sustainable forestry available at different levels, ranging from local, regional and national to global scale assessments. Some efforts were also made to integrate later production stages of forest value chains (such as wood processing) in the assessment scope (e.g. for chain-of-custody certification). However, no indicator set has so far been available covering environmental, social and economic aspects for the entire value chain of building with timber. This gap was closed through applied sustainability research in the project “Holzwende 2020: Sustainable future markets for wood in the building sector”.     

HOW CLIMATE UNCERTAINTY SHOULD BE INCLUDED IN GREAT LAKES MANAGEMENT: MODELING WORKSHOP RESULTS1

ABSTRACT: In two workshops, we evaluated decision analysis methods for comparing Lake Erie levels management alternatives under climate change uncertainty. In particular, we wanted to see how acceptable and effective those methods could be in a public planning setting. The methods evaluated included simulation modeling, scenario analysis, decision trees and structured group discussions. We evaluated the methods by interviewing the workshop participants before and after the workshops. The participants, who were experienced Great Lakes water resources managers, concluded that simulation modeling is user-friendly enough to enable scenario analysis even in workshop settings for large public planning studies. They felt that simulation modeling can improve not only understanding of the system, but also of the options for managing it. Scenario analysis revealed that the decision for the case study, Lake Erie water level regulation, could be altered by the likelihood of climate change. The participants also recommended that structured group discussions be used in public planning settings to elicit ideas and opinions. On the other hand, the participants were less optimistic about decision trees because they felt that the public might view subjective probabilities as difficult to understand and subject to manipulation.     

基于遥感影像的木兰溪水质参数反演

水质监测对水环境评价及污染预防至关重要,但地面监测成本高、监测面积有限等,难以满足实时、大范围监测的要求。为了更好地解决该问题,基于遥感影像的空中监测技术越来越得到研究人员的青睐。以木兰溪为研究区,利用和地面监测数据同步的Landsat-8卫星遥感影像数据,对木兰溪的典型水质参数总磷、总氮、溶解氧、高锰酸盐指数的反演问题进行研究。首先,根据Landsat-8的水体敏感波段,分别选取总磷、总氮、溶解氧、高锰酸盐指数的反演特征波段组合为(b1-b2)/(b2+b3),(b1-b2)/(b3-b4),b2/(b1+b4),b1/b2;其次,利用反演特征波段组合分别构建总磷、总氮、溶解氧、高锰酸盐指数浓度的SVR(Support Vector Regression)反演模型,通过IPSO算法对SVR模型的参数进行优选;然后,将IPSO-SVR反演模型和统计回归反演模型、广义回归神经网络(GRNN)反演模型在验证集上进行评估,以平均绝对误差和均方根误差作为评价指标进行对比分析,结果表明IPSO-SVR反演模型的平均绝对误差和均方根误差最小,说明IPSO-SVR反演模型具有较高的精度和较好的实用性...     

针对大型振动试验辅助支撑系统的结构设计与技术改进

对振动台辅助支撑系统进行了结构改进,增加了空气弹簧和导向轴。并且针对偏心问题,采用多个气源对不同位置分布的空气弹簧施加不同的气压进一步增强振动台的抗倾覆能力。建立了一套可以应用于轨道交通等大型设备的具有较大承载能力(14 t),较大抗倾覆能力的振动台辅助支撑系统。     

Hurricane Katrina‐linked environmental injustice: race,class, and place differentials in attitudes

Claims of environmental injustice, human neglect, and racism dominated the popular and academic literature after Hurricane Katrina struck the United States in August 2005. A systematic analysis of environmental injustice from the perspective of the survivors remains scanty or nonexistent. This paper presents, therefore, a systematic empirical analysis of the key determinants of Katrina‐induced environmental injustice attitudes among survivors in severely affected parishes (counties) in Louisiana and Mississippi three years into the recovery process. Statistical models based on a random sample of survivors were estimated, with the results revealing significant predictors such as age, children in household under 18, education, homeownership, and race. The results further indicate that African‐Americans were more likely to perceive environmental injustice following Katrina than their white counterparts. Indeed, the investigation reveals that there are substantial racial gaps in measures of environmental injustice. The theoretical, methodological, and applied policy implications of these findings are discussed.     

Weather,climate, politics,or God? Determinants of American public opinions toward global warming

2012 American National Election Study data supplemented with monthly temperature data from the United States Historical Climatology Network are used to examine how religion, politics, and weather and climate affect views of global warming. Evangelical fundamentalism is the most consistent factor among all religion-related variables in determining perceptions of global warming, those more oriented toward evangelical fundamentalism being less likely than others to believe in the existence, anthropogenic cause, and adverse impact of global warming. Controlling for the effects of traditional political predisposition variables, including party identification and political ideology, support for the Tea Party plays a dominant role in determining views of global warming; support for the Tea Party leading to higher likelihood of disbelief in the existence, anthropogenic cause, and negative impact of global warming. Warming winters coupled with cooling springs of the past decade are positively related with belief in the existence of global warming.     

Modeling Summer Month Hydrological Drought Probabilities in the United States Using Antecedent Flow Conditions

Climate change raises concern that risks of hydrological drought may be increasing. We estimate hydrological drought probabilities for rivers and streams in the United States (U.S.) using maximum likelihood logistic regression (MLLR). Streamflow data from winter months are used to estimate the chance of hydrological drought during summer months. Daily streamflow data collected from 9,144 stream gages from January 1, 1884 through January 9, 2014 provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February, estimating outcomes 5‐11 months ahead of their occurrence. Few drought prediction methods exploit temporal links among streamflows. We find MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. MLLR models with strong correct classification rates were produced for streams throughout the U.S. One ad hoc test of correct prediction rates of September 2013 hydrological droughts exceeded 90% correct classification. Some of the best‐performing models coincide with areas of high concern including the West, the Midwest, Texas, the Southeast, and the Mid‐Atlantic. Using hydrological drought MLLR probability estimates in a water management context can inform understanding of drought streamflow conditions, provide warning of future drought conditions, and aid water management decision making.     

An Open Source GIS‐Based Decision Support System for Watershed Evaluation of Best Management Practices

Economic costs, water quantity/quality benefits, and cost effectiveness of agricultural best management practices (BMPs) at a watershed scale are increasingly examined using integrated economic‐hydrologic models. However, these models are typically complex and not user‐friendly for examining the effects of various BMP scenarios. In this study, an open source geographic information system (GIS)‐based decision support system (DSS), named the watershed evaluation of BMPs (WEBs), was developed for creating BMP scenarios and simulating economic costs and water quantity/quality benefits at farm field, subbasin, and watershed scales. This DSS or WEBs interface integrated a farm economic model, the Soil and Water Assessment Tool (SWAT), and an optimization model within Whitebox Geospatial Analysis Tools (GAT), an open source GIS software. The DSS was applied to the 14.3‐km² Gully Creek watershed, a coastal watershed in southern Ontario, Canada that drains directly into Lake Huron. BMPs that were evaluated included conservation tillage, nutrient management, cover crop, and water and sediment control basins. In addition to assessing economic costs, water quantity/quality benefits, and cost effectiveness of BMPs, the DSS can be also used to examine prioritized BMP types/locations and corresponding economic and water quantity/quality tradeoffs in the study watershed based on environmental targets or budget constraints. Further developments of the DSS including interface transfer to other watersheds are also discussed. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

An enhanced sustainable approach for design and manufacturing with an application to fuselages for mono-seat civil sailplanes

Abstract

A number of tools and software systems have been developed in order to reduce the time for the design and manufacturing of industrial products. Nowadays, virtual modelling by Computer Aided Design (CAD) systems and process simulation by the application of Finite Element Method (FEM) software is a standard routine in order to compress product design activities and to achieve the optimal solution before producing any real components. These developments provided not only a reduction in the time to market but a higher complexity of the project and an improved quality of the industrial products. With the aim to further improve the sustainability of this activity, the implementation of a tool that adds automatic operations into the design activity is demonstrated. This software can interact with any CAD and helps designers when the object dimension and shape can be affected by limits and constraints deriving from different features and targets, such as the spatial collocation of the component and the interaction with other limiting items, for examples the material and the manufacturing issues. The concept was developed into the software SFIDA (Sailplane Fuselage Integrated Design Application) that is a first attempt to automatically manage multiple relations and optimise their combination.