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 .     

A multi-objective optimisation approach to water management

The management of river basins is complex especially when decisions about environmental flows are considered in addition to those concerning urban and agricultural water demand. The solution to these complex decision problems requires the use of mathematical techniques that are formulated to take into account conflicting objectives. Many optimization models exist for water management systems but there is a knowledge gap in linking bio-economic objectives with the optimum use of all water resources under conflicting demands. The efficient operation and management of a network of nodes comprising storages, canals, river reaches and irrigation districts under environmental flow constraints is challenging. Minimization of risks associated with agricultural production requires accounting for uncertainty involved with climate, environmental policy and markets. Markets and economic criteria determine what crops farmers would like to grow with subsequent effect on water resources and the environment. Due to conflicts between multiple goal requirements and the competing water demands of different sectors, a multi-criteria decision-making (MCDM) framework was developed to analyze production targets under physical, biological, economic and environmental constraints. This approach is described by analyzing the conflicts that may arise between profitability, variable costs of production and pumping of groundwater for a hypothetical irrigation area.     

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.     

基于24Model的矿工怠工行为 管理模式架构及策略研究

为减少矿工怠工行为,基于24Model研究矿工怠工行为的管理模式架构及策略。首先,基于冰山模型理论分析矿工怠工行为的影响因素;其次,基于24Model和行为修正理论提出集怠工行为识别、行为原因分析、行为管理策略选择和行为修正评估于一体的矿工怠工行为管理模式架构;最后,提出了针对性的管理策略。对于提高煤矿企业矿工的工作绩效具有重要的理论意义和实践价值。     

灰色系统模型在总悬浮物预测中的应用

以１９８６－１９９４年东北某城市总悬浮物统计资料为依据，应用灰色系统理论ＧＭ（１，１）模型对总量浮物数值进行预测分析。     

上海市环境空气质量监测体系规划设计

简述了上海市环境空气质量监测的历史和现状,指出了环境空气质量监测发展需求,构建了上海市空气质量监测系统规划框架;提出应构建上海市环境综合信息和公共数据平台,建设发展地基遥感探空、空基和天基遥感反演技术,建立长三角区域大气复合污染监测体系。     

全面建立机动车排气污染监管体系探讨

对南京市机动车排气污染监督管理工作进行了分析,指出了存在的问题,设计了未来监督管理工作的思路。提出对机动车排气污染监管要构建完整的监管体系,重在对部门的监管;加强车辆使用全过程污染防治监管,建立对各级机动车维修企业尾气治理行为的监管制度;启用严格的排放标准和检测手段,全面控制机动车排气污染。     

浙江省生态环境可持续发展评价指标体系的建立

生态环境的可持续发展评价是生态安全与生态建设中的重要内容。本研究应用主成分分析法，收集浙江省1990～2000年的数据，对评价指标进行初步筛选．同时采用德尔斐法进行专家咨询，得到评价指标的分层排序。最后从初选的54个评价指标中，根据主成分分析法、专家咨询法的指标筛选结果，结合有关国家及地方生态环境建设规划，建立了由40个指标组成的浙江省生态环境可持续发展评价指标体系。     

城市燃气管网的地震紧急处置系统研究

燃气管网的地震破坏有时会导致火灾、爆炸等严重的次生灾害。城市中的燃气管网系统若能具备地震紧急处置功能,在地震来临时及时采取自动关闭燃气供应阀门等紧急处置措施,可以避免或减少此类次生灾害的发生。介绍了普遍用于燃气管网日常调度管理的SCADA系统的组成与工作原理,探讨了在SCADA系统中加入地震紧急处置功能的可行性,并基于ArcGIS地理软件,通过VBA二次开发编制了燃气管网的地震紧急处置模拟程序,为燃气管网地震紧急处置系统的实现提供了一定依据。