Application of fuzzy sets and cognitive maps to incorporate social science scenarios in integrated assessment modelsA case study of urbanization in Ujung Pandang, Indonesia

Decision–support systems in the field of integrated water management could benefit considerably from social science knowledge, as many environmental changes are human-induced. Unfortunately the adequate incorporation of qualitative social science concepts in a quantitative modeling framework is not straightforward. The applicability of fuzzy set theory and fuzzy cognitive maps for the integration of qualitative scenarios in a decision–support system was examined for the urbanization of the coastal city of Ujung Pandang, Indonesia. The results indicate that both techniques are useful tools for the design of integrated models based on a combination of concepts from the natural and social sciences. This revised version was published online in July 2006 with corrections to the Cover Date.     

Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area

Istanbul is the most populated city of Turkey with a population of around 10.58 M (2000) living on around 5,750 km². In 1980, the population was only 4.7 M and then it has been more than doubled in only two decades. The population has been increasing as a result of mass immigration. An urbanization process continues and it causes serious increases in urban areas while decreasing the amount of green areas. This rapid, uncontrolled, and illegal urbanization accompanied by insufficient infrastructure has caused degradation of forest and barren lands in the metropolitan area, especially through the last two decades. The watershed basins inside the metropolitan area and the transportation network have accelerated the land-cover changes, which have negative impacts on water quality of the basins. Monitoring urban growth and land cover change will enable better management of this complex urban area by the Greater Istanbul Metropolitan Municipality (GIMM). A temporal assessment of land-cover changes of Istanbul has been documented in this study. The study mainly focuses on the acquisition and analysis of Landsat TM and Landsat GeoCover LC satellite images reflecting the significant land-cover changes between the years of 1990 and 2005. Raster data were converted to vector data and used in Geographic Information Systems (GIS). A database was created for Istanbul metropolitan area to plan, manage, and utilize statistical attribute data covering population, water, forest, industry, and topographic position. Consequently an overlay analysis was carried out and land use/cover changes through years have been detected for the case study area. The capability of Landsat images in determining the alterations in the macro form of the city are also discussed.     

Using Multi-Scale Sampling and Spatial Cross-Correlation to Investigate Patterns of Plant Species Richness

Land managers need better techniques to assess exoticplant invasions. We used the cross-correlationstatistic, I _YZ, to test for the presence ofspatial cross-correlation between pair-wisecombinations of soil characteristics, topographicvariables, plant species richness, and cover ofvascular plants in a 754 ha study site in RockyMountain National Park, Colorado, U.S.A. Using 25 largeplots (1000 m²) in five vegetation types, 8 of 12variables showed significant spatial cross-correlationwith at least one other variable, while 6 of 12variables showed significant spatial auto-correlation. Elevation and slope showed significant spatialcross-correlation with all variables except percentcover of native and exotic species. Percent cover ofnative species had significant spatialcross-correlations with soil variables, but not withexotic species. This was probably because of thepatchy distributions of vegetation types in the studyarea. At a finer resolution, using data from ten1 m² subplots within each of the 1000 m² plots, allvariables showed significant spatial auto- andcross-correlation. Large-plot sampling was moreaffected by topographic factors than speciesdistribution patterns, while with finer resolutionsampling, the opposite was true. However, thestatistically and biologically significant spatialcorrelation of native and exotic species could only bedetected with finer resolution sampling. We foundexotic plant species invading areas with high nativeplant richness and cover, and in fertile soils high innitrogen, silt, and clay. Spatial auto- andcross-correlation statistics, along with theintegration of remotely sensed data and geographicinformation systems, are powerful new tools forevaluating the patterns and distribution of native andexotic plant species in relation to landscape structure.     

Finding threatened forest areas in the central volcanic mountain range conservation area in Costa Rica

The degree at which tropical forests are exposed to human pressure is spatially dependent. Population density, proximity to roads, terrain slope, logging activities and land distribution projects are well known factors inducing deforestation and forest degradation in Latin America. Using expert knowledge to weight these threat factors and a Geographical Information System for spatial modeling, a multi-criteria analysis procedure is presented, that allows stratifying a study region in categories of deforestation threat. The procedure was implemented in the Central Volcanic Mountain Range Conservation Area (CVMRCA) in Costa Rica with the purpose of finding areas with a combination of physical and socioeconomic characteristics that is particularly predisposing to a high probability of deforestation. To validate the map, the CVMRCA was stratified in categories of deforestation risk, and the result was superposed to historical deforestation data of the period 1986–1996. The good correlation between risk category and historical deforestation (r = 0.91, p < 0.001) indicates that the map can be used as a decision support tool for defining priority areas for conservation action.     

Optimal Expansion Strategy for a Sewer System under Uncertainty

Because of fast urban sprawl, land use competition, and the gap in available funds and needed funds, municipal decision makers and planners are looking for more cost-effective and sustainable ways to improve their sewer infrastructure systems. The dominant approaches have turned to planning the sanitary sewer systems within a regional context, while the decentralized and on-site/cluster wastewater systems have not overcome the application barriers. But regionalization policy confers uncertainties and risks upon cities while planning for future events. Following the philosophy of smart growth, this paper presents several optimal expansion schemes for a fast-growing city in the US/Mexico borderlands—the city of Pharr in Texas under uncertainty. The waste stream generated in Pharr is divided into three distinct sewer sheds within the city limit, including south region, central region, and north region. The options available include routing the wastewater to a neighboring municipality (i.e., McAllen) for treatment and reuse, expanding the existing wastewater treatment plant (WWTP) in the south sewer shed, and constructing a new WWTP in the north sewer shed. Traditional deterministic least-cost optimization applied in the first stage can provide a cost-effective and technology-based decision without respect to associated uncertainties system wide. As the model is primarily driven by the fees charged for wastewater transfer, sensitivity analysis was emphasized by the inclusion of varying flat-rate fees for adjustable transfer schemes before contracting process that may support the assessment of fiscal benefits to all parties involved. Yet uncertainties might arise from wastewater generation, wastewater reuse, and cost increase in constructing and operating the new wastewater treatment plant simultaneously. When dealing with multiple sources of uncertainty, the grey mixed integer programming (GIP) model, formulated in the second stage, can further allow all sources of uncertainties to propagate throughout the optimization context, simultaneously leading to determine a wealth of optimal decisions within a reasonable range. Both models ran for three 5-year periods beginning in 2005 and ending in 2020. The dynamic outputs of this analysis reflect the systematic concerns about integrative uncertainties within this decision analysis, which enable decision makers and stakeholders to make all-inclusive decisions for sanitary sewer system expansion in an economically growing region.     

Integrity of Tailings Infrastructure and Information Management: A GIS-Based Conceptual Model

Managing the integrity of tailings infrastructure is an important aspect of handling mine tailings. Unfortunately, little research is directed towards its efficient modeling. This paper presents salient aspects of tailings management. It proposes an integrated geographic information systems (GIS)-based conceptual model for efficient handling of these aspects. The paper also discusses the various integrated modules and comments on the systematic implementation of the model to achieve desired results. The modules include: structural integrity management (SIM), release impact assessment (RIA), and hazardous risk assessment and control (HRAC).     

航班起飞过程的风险识别与控制

飞机起飞阶段是事故多发阶段,为了进行风险识别与控制,设计了定性判别方法和定量计算仿真算法,并以具体飞机坠毁事故为例分析了安全裕度.分别计算了飞机冲出跑道的速度、距离和逃生时间.中断起飞的速度与时间,是火灾发生后的判断关键,也构成了飞机起飞事故的裕度.结果表明,中断起飞的安全裕度比坠毁的大,且关于中断起飞的规定有矛盾之处;用于逃生的90 s规则高于中断起飞的决断速度的限制规定.该分析方法为处理危急时刻的两难决策问题提供了理论指导.     

冲压机械伤害风险评价信息系统研究

研究冲压机械伤害风险评价信息系统,无论对企业内部事故预防还是对职工健康安全,还是节省国家因事故造成的经济损失,都十分必要。应用计算机编程语言和数据库软件,对冲压作业进行综合管理评价、设备设施的固有危险性评价及劳动卫生与作业环境评价,进而对冲压作业风险进行评价分级,建立动态的冲压机械伤害风险评价数据库管理信息系统。该系统可保存所有评价的项目资料,供应用和参考,实现了对冲压机械伤害风险评价的数据库动态管理,使风险评价分析工作组织化、条理化、系统化。     

发展和完善我国航空安全自愿报告系统的研究

鉴于当前我国航空安全自愿报告系统运行时间短、效果不佳的现状,主要从航空安全自愿报告系统运行、系统平台功能设计及信息处理分析3方面,对国内外大量航空安全自愿报告系统进行研究。指出目前我国航空安全自愿报告系统在系统设计、系统运行及后期的信息处理、信息发布利用等环节存在的问题和制约因素。就协调中立运行机构与系统3大原则之间的冲突,加强系统平台的人性化设计,提高信息分析处理效果,完善信息发布利用机制、提高信息利用度等关键问题给出了新的思路和对策。为我国航空自愿报告系统的完善和发展,提出了切实的改进建议和措施。     

基于OSHMS的煤矿模糊综合安全评价管理模型的建立

针对目前我国煤矿企业安全管理现状,以一种全新的思路结合职业安全健康管理体系(OSHMS)的系统化体系结构及其先进之处,以预防为主,持续改进为动力,建立了基于生产过程的绩效测量、人员职责分配、运行控制和风险管理为核心的4个主要因素的安全评价管理模型;并以此为指导建立安全评价指标体系框架,运用模糊综合安全评价的方法对指标体系进行合成;最后,结合矿井生产过程中的供电系统,对淮南潘三矿现场进行实例应用,证明其可行性。该管理模型对煤矿企业安全管理有推广和借鉴意义,可望为煤矿企业通过安全管理实现科技兴安提供一种更可靠,更有效的安全管理方法。