灾害发生时影响居民心理承受能力的社会心理因素分析

目的：探讨影响个体面对自然灾害时心理承受能力的社会心理因素．方法，采用问卷调查的方式，运用因素分析法和相关分析法对1000余名居民进行测评．结果。①个体面对灾害时的心理承受能力的影响因素为。灾害意识、灾时行为、灾害认知、社会支持，②通过相关分析，灾时行为对心理承受能力的影响最大。     

地震的社会经济影响

在查阅前人研究成果的基础上,分析了地震造成社会灾害严重、影响地区经济发展的现象,认为地震灾害对一个地区所造成的影响程度与该地区的承灾力有关,并影响地区经济的可持续发展.     

关于大型社会经营活动安全问题的探讨

笔者列举了相关事例,指出了大型社会经营活动的主要特点;从制度、操作、技术研究、教育4方面分析了目前大型社会经营活动存在的现实突出问题;重点从公共场所安全、应急、疏散、人群、活动容量、旅游安全、安全管理、工作实践这几个方面综述了大型社会经营活动安全相关研究的进展并指出不足之处;在上述研讨的基础上结合风险评价理论等,提出了一个较为完善的保障大型社会经营活动安全的构想,并详细论述其内容、方法及技术路线。该构想对开展大型社会经营活动的事故预防、风险管理、应急处理和控制具有重要参考价值。     

提高洪水智能预报中洪峰预报精度方法的研究

针对防洪减灾的实际需要,对如何提高智能网络对洪峰的预报精度问题进行了深入系统的研究,提出了峰值放大修正系数和遗传算法优化网络初始权重相结合的改进算法,历史资料的检验结果表明了这些改进策略的有效性和可靠性.     

Significant developments in sustainable development law and governance: A proposal

This article develops a practical proposal for progress on sustainable development law. It examines the prospects for an international sustainable development law to provide a framework for more effective, coherent governance. Sustainable development law is briefly defined and an analytical framework is provided. Different degrees of integration between economic, social and environmental law are described. Certain principles of international law related to sustainable development are also highlighted. It is argued that these principles may serve to guide law‐makers and jurists where social, economic and environmental law and policy conflict or overlap. Continuing, underlying questions of sustainable development governance are addressed and its global frameworks analysed. The article also focuses on the 2002 World Summit on Sustainable Development, held in Johannesburg in August‐September 2002, and its specific mandate for the United Nations Commission on Sustainable Development (UNCSD) to take related legal developments into account. The article advances a proposal: that governments, economic, social and environmental intergovernmental organizations and other actors establish a ‘network of inquiry’ with members from relevant groups, including legal and academic organizations, and other expert groups, in order to follow, research, analyse and debate legal developments in a balanced way.     

AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.     

中国水灾社会脆弱性评估方法的改进与应用--以长沙地区为例

中国是洪水灾害发生频繁的国家之一,研究水灾脆弱性对中国的灾害风险管理有重要的意义.但是,合理评估脆弱性尤其是社会脆弱性却面临着极大的挑战.论文对评估社会脆弱性指数的研究现状进行了分析,指出了传统评估方法存在的问题,并尝试改进Hoovering评估模式.选择湘江流域的长沙地区为研究区,应用改进模型对研究区进行了社会脆弱性指数的评估.结果表明,长沙地区社会脆弱性从1980年至2000年基本处于下降趋势,但是在2002年和2003年增长迅速.2003年,长沙5区4县中社会脆弱性指数最大的是开福区,其次是长沙县,而宁乡县的社会脆弱性指数最低.     

Information Network Topologies for Enhanced Local Adaptive Management

We examined the principal effects of different information network topologies for local adaptive management of natural resources. We used computerized agents with adaptive decision algorithms with the following three fundamental constraints: (1) Complete understanding of the processes maintaining the natural resource can never be achieved, (2) agents can only learn by experimentation and information sharing, and (3) memory is limited. The agents were given the task to manage a system that had two states: one that provided high utility returns (desired) and one that provided low returns (undesired). In addition, the threshold between the states was close to the optimal return of the desired state. We found that networks of low to moderate link densities significantly increased the resilience of the utility returns. Networks of high link densities contributed to highly synchronized behavior among the agents, which caused occasional large-scale ecological crises between periods of stable and high utility returns. A constructed network involving a small set of experimenting agents was capable of combining high utility returns with high resilience, conforming to theories underlying the concept of adaptive comanagement. We conclude that (1) the ability to manage for resilience (i.e., to stay clear of the threshold leading to the undesired state as well as the ability to re-enter the desired state following a collapse) resides in the network structure and (2) in a coupled social–ecological system, the systemwide state transition occurs not because the ecological system flips into the undesired state, but because managers lose their capacity to reorganize back to the desired state. An erratum to this article can be found at .     

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.     

Dynamic Bayesian networks for reliability evaluation of subsea wellhead connector during service life based on Monte Carlo method

The subsea wellhead connector is a critical connection component between subsea Christmas tree and subsea wellhead for preventing the leakage of oil and gas in the subsea production system. Excited by cyclical loadings due to environmental forces and the other support forces, the subsea wellhead connector is prone to the failure, which could lead to the loss of subsea tree or wellhead integrity and even catastrophic accidents. With the Monte Carlo simulation method, this paper presents a reliability analysis approach based on dynamic Bayesian Networks, aiming to assess the failure probability of the subsea wellhead connector during service life. Take the driving ring component of the subsea wellhead connector as an example to demonstrate the reasonability of the proposed model. The generation data is processed by the transform between the numerical value and the state variable. Based on the stress-strength interference theory, the structure reliability of the driving ring with 96.26% is achieved by the proposed model with the consideration the aging of the material strength and the most influential factors are figured out. Meanwhile, the corresponding control measures are proposed effectively reduce the failure risk of the subsea wellhead connector during service life.