Combining GIS with fuzzy multicriteria decision-making for landfill siting in a fast-growing urban region

Landfill siting is a difficult, complex, tedious, and protracted process requiring evaluation of many different criteria. This paper presents a fuzzy multicriteria decision analysis alongside with a geospatial analysis for the selection of landfill sites. It employs a two-stage analysis synergistically to form a spatial decision support system (SDSS) for waste management in a fast-growing urban region, south Texas. The first-stage analysis makes use of the thematic maps in Geographical information system (GIS) in conjunction with environmental, biophysical, ecological, and socioeconomic variables leading to support the second-stage analysis using the fuzzy multicriteria decision-making (FMCDM) as a tool. It differs from the conventional methods of integrating GIS with MCDM for landfill selection because the approach follows two sequential steps rather than a full-integrated scheme. The case study was made for the city of Harlingen in south Texas, which is rapidly evolving into a large urban area due to its vantage position near the US-Mexico borderlands. The purpose of GIS was to perform an initial screening process to eliminate unsuitable land followed by utilization of FMCDM method to identify the most suitable site using the information provided by the regional experts with reference to five chosen criteria. Research findings show that the proposed SDSS may aid in recognizing the pros and cons of potential areas for the localization of landfill sites in any study region. Based on initial GIS screening and final FMCDM assessment, "site 1" was selected as the most suitable site for the new landfill in the suburban area of the City of Harlingen. Sensitivity analysis was performed using Monte Carlo simulation where the decision weights associated with all criteria were varied to investigate their relative impacts on the rank ordering of the potential sites in the second stage. Despite variations of the decision weights within a range of 20%, it shows that "site 1" remains its comparative advantage in the final site selection process.     

Systematic regional planning for multiple objective natural resource management

On-ground natural resource management actions such as revegetation and remnant vegetation management can simultaneously affect multiple objectives including land, water and biodiversity resources. Hence, planning for the sustainable management of natural resources requires consideration of these multiple objectives. However, planning the location of management actions in the landscape often treats these objectives individually to reduce the process and spatial complexity inherent in human-modified and natural landscapes. This can be inefficient and potentially counterproductive given the linkages and trade-offs involved. We develop and apply a systematic regional planning approach to identify geographic priorities for on-ground natural resource management actions that most cost-effectively meet multiple natural resource management objectives. Our systematic regional planning approach utilises integer programming within a structured multi-criteria decision analysis framework. Intelligent siting can capitalise on the multiple benefits of on-ground actions and achieve natural resource management objectives more efficiently. The focus of this study is the human-modified landscape of the River Murray, South Australia. However, the methodology and analyses presented here can be adapted to other regions requiring more efficient and integrated planning for the management of natural resources.     

Application of spatial multi-criteria analysis to site selection for a local park: a case study in the Bergamo Province, Italy

This contribution discusses a site selection process for establishing a local park. It was supported by a value-focused approach and spatial multi-criteria evaluation techniques. A first set of spatial criteria was used to design a number of potential sites. Next, a new set of spatial and non-spatial criteria was employed, including the social functions and the financial costs, together with the degree of suitability for the park to evaluate the potential sites and to recommend the most acceptable one. The whole process was facilitated by a new software tool that supports spatial multiple criteria evaluation, or SMCE. The application of this tool, combined with a continual feedback by the public administration, has provided an effective methodology to solve complex decisional problem in land-use and urban planning.     

Monitoring nitrogen accumulation in mosses in central European forests

In order to assess whether nitrogen (N) loads in mosses reflect different land uses, 143 sites in North Rhine-Westphalia, the Weser-Ems Region and the Euro Region Nissa were sampled between 2000 and 2005. The data were analysed statistically with available surface information on land use and forest conditions. N bioaccumulation in mosses in the Weser-Ems Region with high densities of agricultural land use and livestock exceeded the concentrations in the more industrialised Euro Region Nissa. In all three study areas agricultural and livestock spatial densities were found to be positively correlated with N bioaccumulation in mosses. In North Rhine-Westphalia, the N concentrations in mosses was also moderately correlated with N concentrations in leaves and needles of forest trees. The moss method proved useful to assess the spatial patterns of N bioaccumulation due to land use.     

环境信息数据仓库建设及其相关的技术应用

阐述了环境信息数据仓库建设的需求和体系框架,介绍了环境信息数据仓库建设的关键技术和基于数据仓库的决策支持系统,列举了环境信息数据仓库在2000年2001年间对我国西部12个省、自治区和直辖市的大范围生态环境调查课题中的应用价值。     

环境决策支持系统中两类模型方法的整合

该文对环境决策支持系统中情景分析和数学优化两类模型方法的优缺点和适用范围进行了比较分析。实例研究表明,两类模型方法的整合使用可以有效地支持较为复杂的决策过程。      

An inexact two-stage mixed integer linear programming method for solid waste management in the City of Regina

In this study, an interval-parameter two-stage mixed integer linear programming (ITMILP) model is developed for supporting long-term planning of waste management activities in the City of Regina. In the ITMILP, both two-stage stochastic programming and interval linear programming are introduced into a general mixed integer linear programming framework. Uncertainties expressed as not only probability density functions but also discrete intervals can be reflected. The model can help tackle the dynamic, interactive and uncertain characteristics of the solid waste management system in the City, and can address issues concerning plans for cost-effective waste diversion and landfill prolongation. Three scenarios are considered based on different waste management policies. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment or justification of the existing waste flow allocation patterns, the long-term capacity planning of the City's waste management system, and the formulation of local policies and regulations regarding waste generation and management.     

Why not in your backyard? scientific data and nonrational decisions about risk

The siting of hazardous waste facilities constitutes a special case of the many no win environmental decisions we face. They share common features: (a) we must decide something; (b) the decision affects some people more than others; (c) as scientists we are not 100% confident of our research results; (d) elements of the decision remain unquantifiable; and (e) decisions combine both scientific and political elements. In this paper we attempt to illustrate and analyze several examples that combine all of these elements and to suggest methods which would lead toward a scientific valid and politically useful resolution. Using well-known examples such as the public's fear of death from nuclear power, snakebite, and smoking, we attempt to integrate public perception of risks into a decision-making model. Finally, the conclusions deal with the role of policy making, public perception, and science in resolving environmental controversies. We do not, however, solve this perplexing problem.     

Assessing Risk in Operational Decisions Using Great Lakes Probabilistic Water Level Forecasts

/ A method adapted from the National Weather Service's Extended Streamflow Prediction technique is applied retrospectively to three Great Lakes case studies to show how risk assessment using probabilistic monthly water level forecasts could have contributed to the decision-mak-ing process. The first case study examines the 1985 International Joint Commission (IJC) decision to store water in Lake Superior to reduce high levels on the downstream lakes. Probabilistic forecasts are generated for Lake Superior and Lakes Michigan-Huron and used with riparian inundation value functions to assess the relative impacts of the IJC's decision on riparian interests for both lakes. The second case study evaluates the risk of flooding at Milwaukee, Wisconsin, and the need to implement flood-control projects if Lake Michigan levels were to continue to rise above the October 1986 record. The third case study quantifies the risks of impaired municipal water works operation during the 1964-1965 period of extreme low water levels on Lakes Huron, St. Clair, Erie, and Ontario. Further refinements and other potential applications of the probabilistic forecast technique are discussed.KEY WORDS: Great Lakes; Water levels; Forecasting; Risk; Decision making