复杂矿井通风系统角联风路自动识别方法的研究

角联风路的识别与稳定性分析是矿井通风系统稳定性与可靠性分析理论中的核心内容之一。笔者对通风系统角联风路进行了深入的研究;分析了通路法的局限性,简化了角联风路的数学模型,提出了快速、准确、灵活的角联风路自动识别的新方法——节点位置法;编写了角联风路自动识别软件,并应用现场数据进行测试,将其结果与通路法的结果进行了比较分析;证明了节点位置法是切实可行的。     

地震应急的影响因素分析

本文在理论分析、国内外历史震例和救灾案例研究的基础上,同时参照我国的相关政策和法律法规文件,对影响地震应急(包括震前准备和震后应急行动)的因素进行了初步分类,并系统讨论了这些因素影响地震应急的方式、途径和程度.以期为地震应急的理论研究和地震应急的实际工作提供部分补充和指导.     

基于GIS的长江中下游地区洪灾风险分区及评价

国内外近几年的发展表明,在所有可能避免和减轻自然灾害的措施中,最简单有效的方法就是通过在科学研究基础上进行风险区划,将自然灾害管理提高到风险管理的水平.在长江流域数字化地图的基础上,选取不同重复期(20,50,100年),及包括1870年历史洪水和1931,1935,1954,1991,1995,1996,1998,1999和2002年共10次洪水,借助Arcview地理信息系统的空间分析和叠加功能,对长江中下游地区的洪水灾害危险性进行了初步评价.首先参考洪水灾害淹没图和相关历史文献记录资料,构建10次洪水受灾县(市)分布图;其次对这10次洪水受灾县(市)分布图进行叠加,得到长江中下游地区洪涝灾害风险性评价图.分析表明:长江中下游地区洪水风险的分布是有规律的,而且具有明显的地理意义.有4个明显的高危风险区,分别是洞庭湖、鄱阳湖两湖平原的湖滨地区和公安以下的长江中游河段的沿江一带,尤其是荆江河段以及两江相夹地势低洼的江汉平原;沿高危风险区外侧为高风险地区,重点在汉江下游、资、沅、澧水、清江流域、皖沿江地区以及太湖流域的部分地区;沿长江于高危风险和高风险地区两侧分别为风险较小地区;其他地区对于洪水灾害而言则为安全地区.评价结果与长江中下游的实际情况基本吻合.     

北京市地热资源开发的时空变化

简要介绍了北京地热资源特点,分析了北京地热资源开发的时空演变规律.在搜集资料和实地调查的基础上,绘制出了北京城区地热井分布示意图,并针对其开发利用中可能出现的问题提出了对策.     

生态示范区可持续发展灰色评估系统设计应用

介绍了如何在国家级生态示范区的可持续发展评估中运用灰色系统理论构建定量评估系统模型,并给出了关系型指标表、量纲模型、多元灰色预测模型和发展系数模型以及应用实例.根据该评估系统模型所开发的灰色评估系统由数学模型、计算机软件和硬件组成,可自动完成量纲统一、指标预测、相关分析、综合评估和趋势图表的运算和输出.     

经济欠发达山区矿泉水资源的开发利用--以梅州市为例

基于矿泉水与人体健康的关系、在社会经济发展中的作用,以及对梅州市矿泉水资源条件及其开发利用现状的调研,探讨了经济欠发达山区矿泉水资源的开发利用对策和前景.认为应统一资源管理,加强质量监督力度,打造优质品牌,提高市场竞争能力,争取与食品、旅游等部门联营,使矿泉水产品多样化、系列化;结合地方工业建设和梅州旅游市场需求,培育矿泉水消费市场.     

改性沸石用于饮用水除氟的试验研究

试验针对存在的氟污染饮用水问题,将天然沸石用NaOH和Al2（SO4）3溶液改性制成除氟材料.静态试验研究表明：改性沸石除氟吸附反应快,其最佳pH值范围为5～9,而且对氟离子具有较好的离子选择性能.通过动态试验研究发现,降低进水流量和原水浓度可以增大滤层的吸附容量.两种再生方式对比试验表明：用Al2（SO4）3溶液再生效果优于用NaOH和Al2（SO4）3溶液联合再生.     

大气环境影响评价工作分级要求要点

根据大气环境影响评价工作的分级要求,提出了针对性的工作要点,以满足大气环境评价工作的需要.     

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.