Spatially explicit sensitivity and uncertainty analysis for multicriteria-based vulnerability assessment

This research analyses the application of spatially explicit sensitivity and uncertainty analysis for GIS (Geographic Information System) multicriteria decision analysis (MCDA) within a multi-dimensional vulnerability assessment regarding flooding in the Salzach river catchment in Austria. The research methodology is based on a spatially explicit sensitivity and uncertainty analysis of GIS-CDA for an assessment of the social, economic, and environmental dimensions of vulnerability. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimise the associated uncertainty within each dimension of the vulnerability assessment. The methodology proposed for achieving this objective is composed of four main steps. The first step is computing criteria weights using the analytic hierarchy process (AHP). In the second step, Monte Carlo simulation is applied to calculate the uncertainties associated with AHP weights. In the third step, the global sensitivity analysis (GSA) is employed in the form of a model-independent method of output variance decomposition, in which the variability of the different vulnerability assessments is apportioned to every criterion weight, generating one first-order (S) and one total effect (ST) sensitivity index map per criterion weight. Finally, in the fourth step, an ordered weighted averaging method is applied to model the final vulnerability maps. The results of this research demonstrate the robustness of spatially explicit GSA for minimising the uncertainty associated with GIS-MCDA models. Based on these results, we conclude that applying the variance-based GSA enables assessment of the importance of each input factor for the results of the GIS-MCDA method, both spatially and statistically, thus allowing us to introduce and recommend GIS-based GSA as a useful methodology for minimising the uncertainty of GIS-MCDA.     

Building Consensus in Environmental Impact Assessment Through Multicriteria Modeling and Sensitivity Analysis

Multicriteria decision analysis (MCDA) increasingly is being applied in environmental impact assessment (EIA). In this article, two MCDA techniques, stochastic analytic hierarchy process and compromise programming, are combined to ascertain the environmental impacts of and to rank two alternative sites for Mexico City’s new airport. Extensive sensitivity analyses were performed to determine the probability of changes in rank ordering given uncertainty in the hierarchy structure, decision criteria weights, and decision criteria performances. Results demonstrate that sensitivity analysis is fundamental for attaining consensus among members of interdisciplinary teams and for settling debates in controversial projects. It was concluded that sensitivity analysis is critical for achieving a transparent and technically defensible MCDA implementation in controversial EIA.     

A distance-based uncertainty analysis approach to multi-criteria decision analysis for water resource decision making

The choice among alternative water supply sources is generally based on the fundamental objective of maximising the ratio of benefits to costs. There is, however, a need to consider sustainability, the environment and social implications in regional water resources planning, in addition to economics. In order to achieve this, multi-criteria decision analysis (MCDA) techniques can be used. Various sources of uncertainty exist in the application of MCDA methods, including the selection of the MCDA method, elicitation of criteria weights and assignment of criteria performance values. The focus of this paper is on the uncertainty in the criteria weights. Sensitivity analysis can be used to analyse the effects of uncertainties associated with the criteria weights. Two existing sensitivity methods are described in this paper and a new distance-based approach is proposed which overcomes limitations of these methods. The benefits of the proposed approach are the concurrent alteration of the criteria weights, the applicability of the method to a range of MCDA techniques and the identification of the most critical criteria weights. The existing and proposed methods are applied to three case studies and the results indicate that simultaneous consideration of the uncertainty in the criteria weights should be an integral part of the decision making process.     

Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network

The purpose of this article is Artificial Neural Network (ANN) modeling using ecological and associated factors with forest degradation to predict the degradation of ecosystem, thereby enabling us to assess the environmental impacts of forest projects as an Environmental Decision Support System (EDSS). Results of the Multi-Layer Feed-Forward Network (MLFN), trained for Optimized Forest Degradation Model (OFDM), indicate that the performance of OFDM is more than other degradation models. Changes in forest management activities with higher value in sensitivity analysis help forest managers to decrease OFDM entity and environment impacts. The system is an intelligent EDSS, which allows the decision-maker to model criteria in forest degradation in order to reach and employ the optimal allocation plan. Considering results, multi criteria decision analysis (MCDA) approaches based on ANN, is an encouraging and robust method for solving MCDA problems.     

Multi-criteria risk management with the use of <Emphasis Type="Italic">DecernsMCDA</Emphasis>: methods and case studies

Emerging challenges of risk management, environmental protection, and land-use planning requires integration of stakeholder values and expert judgment. The process of decision making in situation of high uncertainty can be assisted through the use of decision support systems (DSSs). Such DSSs are often based on tools for spatial data representation (GIS) and environmental models that are integrated using multi-criteria decision analysis (MCDA). This paper presents DecernsMCDA implementing all major types of multi-criteria methods and tools (AHP, MAUT, Outranking) under the same user interface. In addition to providing ability for testing model uncertainty associated with selection of specific MCDA algorithms, DecernsMCDA implements new algorithms for parameter uncertainty analysis based on probabilistic approaches and fuzzy sets. The paper illustrates application of DecernsMCDA for selecting remedial alternative at radiologically contaminated sites.     

Uncertainty modelling in multi-criteria analysis of water safety measures

Water utilities must assess risks and make decisions on safety measures in order to obtain a safe and sustainable drinking water supply. The World Health Organization emphasises preparation of water safety plans, in which risk ranking by means of risk matrices with discretised probability and consequence scales is commonly used. Risk ranking enables prioritisation of risks, but there is currently no common and structured way of performing uncertainty analysis and using risk ranking for evaluating and comparing water safety measures. To enable a proper prioritisation of safety measures and an efficient use of available resources for risk reduction, two alternative models linking risk ranking and multi-criteria decision analysis (MCDA) are presented and evaluated. The two models specifically enable uncertainty modelling in MCDA, and they differ in terms of how uncertainties in risk levels are considered. The need of formal handling of risk and uncertainty in MCDA is emphasised in the literature, and the suggested models provide innovations that are not dependent on the application domain. In the case study application presented here, possible safety measures are evaluated based on the benefit of estimated risk reduction, the cost of implementation and the probability of not achieving an acceptable risk level. Additional criteria such as environmental impact and consumer trust may also be included when applying the models. The case study shows how safety measures can be ranked based on preference scores or cost-effectiveness and how measures not reducing the risk enough can be identified and disqualified. Furthermore, the probability of each safety measure being ranked highest can be calculated. The two models provide a stepwise procedure for prioritising safety measures and enable a formalised handling of uncertainties in input data and results.     

Collaborative environmental planning in river management: an application of multicriteria decision analysis in the White River Watershed in Vermont

Multicriteria decision analysis (MCDA) provides a well-established family of decision tools to aid stakeholder groups in arriving at collective decisions. MCDA can also function as a framework for the social learning process, serving as an educational aid in decision problems characterized by a high level of public participation. In this paper, the framework and results of a structured decision process using the outranking MCDA methodology preference ranking organization method of enrichment evaluation (PROMETHEE) are presented. PROMETHEE is used to frame multi-stakeholder discussions of river management alternatives for the Upper White River of Central Vermont, in the northeastern United States. Stakeholders met over 10 months to create a shared vision of an ideal river and its services to communities, develop a list of criteria by which to evaluate river management alternatives, and elicit preferences to rank and compare individual and group preferences. The MCDA procedure helped to frame a group process that made stakeholder preferences explicit and substantive discussions about long-term river management possible.     

Trends and applications of multi-criteria decision analysis: use in government agencies

Government agencies are responsible for making complex, high-stake decisions, which require them to balance political, technical, and economic considerations. Pressure from stakeholders and administrative requirements necessitate a traceable and transparent method for decision making. Multi-criteria decision analysis (MCDA) methods are available to decision makers to facilitate systematic treatment of the information and factors necessary to make informed and effective decisions in complex circumstances. A survey of gray and academic literature was conducted to gauge the level of application and awareness of MCDA methods by US government agencies and determine if the tools’ benefits are being realized. Results show an increase in awareness and consideration of MCDA from 2000 to the present, and that agencies are especially considering and using tools to engage with stakeholders. Government agencies would benefit from extending the application of MCDA to strategic planning and congressional engagement, as well as by standardizing MCDA use to better enable inter-agency collaboration and communication.     

Rough Set Rule Induction for Suitability Assessment

The data that characterize an environmental system are a fundamental part of an environmental decision-support system. However, obtaining complete and consistent data sets for regional studies can be difficult. Data sets are often available only for small study areas within the region, whereas the data themselves contain uncertainty because of system complexity, differences in methodology, or data collection errors. This paper presents rough-set rule induction as one way to deal with data uncertainty while creating predictive if–then rules that generalize data values to the entire region. The approach is illustrated by determining the crop suitability of 14 crops for the agricultural soils of the Willamette River Basin, Oregon, USA. To implement this method, environmental and crop yield data were spatially related to individual soil units, forming the examples needed for the rule induction process. Next, four learning algorithms were defined by using different subsets of environmental attributes. ROSETTA, a software system for rough set analysis, was then used to generate rules using each algorithm. Cross-validation analysis showed that all crops had at least one algorithm with an accuracy rate greater than 68%. After selecting a preferred algorithm, the induced classifier was used to predict the crop suitability of each crop for the unclassified soils. The results suggest that rough set rule induction is a useful method for data generalization and suitability analysis.     

Model‐Based Nitrogen and Phosphorus (Nutrient) Criteria for Large Temperate Rivers: 1. Model Development and Application

An initial inquiry into model‐based numeric nitrogen and phosphorus (nutrient) criteria for large rivers is presented. Field data collection and associated modeling were conducted on a segment of the lower Yellowstone River in the northwestern United States to assess the feasibility of deriving numeric nutrient criteria using mechanistic water‐quality models. The steady‐state one‐dimensional model QUAL2K and a transect‐based companion model AT2K were calibrated and confirmed against low‐flow conditions at a time when river loadings, water column chemistry, and diurnal indicators were approximately steady state. Predictive simulation was then implemented via nutrient perturbation to evaluate the steady‐state and diurnal response of the river to incremental nutrient additions. In this first part of a two‐part series, we detail our modeling approach, model selection, calibration and confirmation, sensitivity analysis, model outcomes, and associated uncertainty. In the second part (Suplee et al., 2015) we describe the criteria development process using the tools described herein. Both articles provide a fundamental understanding of the process required to develop site‐specific numeric nutrient criteria using models in applied regulatory settings.     

Trends and applications of multi-criteria decision analysis in environmental sciences: literature review

Approximately 3000 papers concerning multi-criteria decision analysis (MCDA) in the environmental field were identified through a series of queries in the Web of Science database and classified by MCDA method and environmental application using text mining in R. Stemming and stop word removal techniques were used to remove irrelevant text from the literature. Trends in MCDA methods (AHP/ANP, TOPSIS, outranking, MAUT/MAVT) associated with specific environmental applications (water, air, energy, natural resources, and waste management) or interventions/tools applications (stakeholders, strategies, sustainability, and GIS) were identified. The results show a linear growth in the share of MCDA papers in environmental science across all application areas. Furthermore, the results show that AHP/ANP and MAUT/MAVT are the most frequently mentioned MCDA methods in the literature. For environmental applications, the results showed that natural resource and waste management keywords were, respectively, the most and least commonly discussed applications within the MCDA papers. For intervention/tool applications, we found that keywords associated with ‘strategy’ and ‘GIS’ applications are, respectively, the most and least commonly discussed keywords within the MCDA papers. The authors found that MCDA method keywords were evenly distributed across the environmental and intervention/tool applications, indicating a lack of preference in the environmental field for use of specific MCDA methods. This paper demonstrates that text mining is an applicable tool to assess specific textual trends and patterns when analyzing larger bodies of MCDA literature.     

An uncertainty based multi-criteria ranking system for open pit mining cut-off grade strategy selection

Cut-off grade strategy (COGS) is a concept that directly influences the financial, technical, economical, and environmental issues in relation to the exploitation of a mineral resource. Despite the simple definition of cut-off grade, the COGS problem is one of the complex and complicated problems in the mine planning process. From the optimization point of view, the COGS with an objective of maximizing the present value of future cash flows is a non-linear and a non-convex problem that even in its deterministic form can be solved using approximate optimization methods. This optimization problem will also be more complex and complicated under uncertainty conditions. This paper proposes an uncertainty based multi-criteria ranking system to investigate the problem of COGS selection considering metal price and geological uncertainties. The proposed system aims at selection of the best COGS among technically feasible alternative COGSs under uncertainty circumstances. Our developed system is based on integrating metal price and geological uncertainties as well as operating flexibility to close the mine early. We incorporate this operating flexibility into the proposed system using a Monte Carlo based real options (RO) valuation model. For this purpose, in addition to the expected value, other risk criteria are considered to rank the alternatives. These risk criteria include abilities of strategies in producing extra profits, minimizing losses, and achieving the predefined goals of the production. In this study, the technically possible COGSs are generated using the Lane comprehensive algorithm. To demonstrate the effectiveness of the proposed system, we utilize data of an Iranian gold mine. Results show that the proposed system outperforms conventional methods in the sense that it shows significantly lower average mis-ranking than the other methods and also selects a strategy with a higher value. The sensitivity analysis of the proposed system relative to the gold price shows that the system is highly dependent on the parameters of the stochastic process used to model the evolution of the metal price. Therefore, special consideration should be given in estimating stochastic process parameters.     

A new method for site suitability analysis: The analytic hierarchy process

A critical shortcoming of methods that are reliant upon the judgment of experts to determine site suitability is noted. The article introduces a new method, the analytic hierarchy process (AHP) with which error in judging the relative importance of factors in site suitability analysis can be both detected and corrected. The proposed approach is illustrated with an example to show how the AHP frames the site evaluation problem and can aid in decision making involving multiple criteria, factor diversity, and conditions of uncertainty. The article concludes by suggesting the potential application of the AHP in public choice decisions involving complex, controversial, and conflictual site selection processes.     

Uncertainty Analysis In Dissolved Oxygen Modeling in Streams

Uncertainty analysis in surface water quality modeling is an important issue. This paper presents a method based on the first-order reliability method (FORM) to assess the exceedance probability of a target dissolved oxygen concentration in a stream, using a Streeter–Phelps prototype model. Basic uncertainty in the input parameters is considered by representing them as random variables with prescribed probability distributions. Results obtained from FORM analysis compared well with those of the Monte Carlo simulation method. The analysis also presents the stochastic sensitivity of the probabilistic outcome in the form of uncertainty importance factors, and shows how they change with changing simulation time. Furthermore, a parametric sensitivity analysis was conducted to show the effect of selection of different probability distribution functions for the three most important parameters on the design point, exceedance probability, and importance factors. Note: This version was published online in June 2005 with the cover date of August 2004.     

Integrating social preference in GIS-aided planning for forestry and conservation activities: a case study from rural SE Asia

Land-use planning using geographic information systems (GIS) commonly emphasizes biophysical spatial data; however planning can be improved by integrating spatial sets of socioeconomic data into the GIS. As an example, we compared a traditional GIS-aided forestry planning protocol that considered only biophysical suitability, with an integrated GIS-aided approach that incorporated both biophysical and socioeconomic suitability. The analyses were conducted for the planning of plantation investments in the Kyaukpadaung Township in the dry zone of central Myanmar. The traditional approach used three biophysical layers for suitability: land use, slope, and accessibility. In contrast, the integrated GIS approach included biophysical suitability data, perceptions and preferences of local villagers towards forestry (social suitability), and quantitative socioeconomic data. The results indicated that the integrated approach provided two principal benefits over the traditional method. First, the integrated method resulted in a more precise idea of suitable sites for plantation investment that could benefit more rural people and also lead to greater investment efficiency. Second, incorporating social preference into the GIS takes into account the crucial element of social capital (viz., social preference), which should lead to higher levels of community acceptance of plantation projects because those plantations would be established on socially suitable land. A second GIS exercise showed how conservation investment decisions could be informed using the integrated method. The results of this study support the idea that GIS-aided planning activities can be enhanced through the incorporation of social data into the analysis. When applicable, spatial data collection efforts for GIS-based planning exercises should incorporate spatial socioeconomic data.     

Measuring sustainable development using a multi-criteria model: A case study

This paper shows how Multi-criteria Decision Analysis (MCDA) can help in a complex process such as the assessment of the level of sustainability of a certain area. The paper presents the results of a study in which a model for measuring sustainability was implemented to better aid public policy decisions regarding sustainability. In order to assess sustainability in specific areas, a methodological approach based on multi-criteria analysis has been developed. The aim is to rank areas in order to understand the specific technical and/or financial support that they need to develop sustainable growth.The case study presented is an assessment of the level of sustainability in different areas of an Italian Region using the MCDA approach. Our results show that MCDA is a proper approach for sustainability assessment. The results are easy to understand and the evaluation path is clear and transparent. This is what decision makers need for having support to their decisions. The multi-criteria model for evaluation has been developed respecting the sustainable development economic theory, so that final results can have a clear meaning in terms of sustainability.     

Symptom patterns, and perceptual and cognitive styles in subjects with multiple chemical sensitivity (MCS)

Multiple chemical sensitivity (MCS) is defined as a syndrome with multiple medically unexplained symptoms attributed to low levels of chemically unrelated substances in the environment. The etiology of this syndrome is still unclear. As MCS may be conceptualized as an atypical type of somatoform disorder, the purpose of the study was to examine whether MCS subjects show symptom patterns, personality traits, and perceptual and cognitive styles as typically found in somatoform patients. Nonsensitive controls (n=36) were compared to subjects with moderate (n=35) and high (n=35) MCS intensity with self-report psychological questionnaires and structured interviews for depression and somatoform disorders. The high MCS group scored significantly higher than the other two groups on self-report scales for somatoform symptoms and depression. Moreover, high MCS was strongly associated with the diagnosis of somatoform disorder, and weaker but still significantly with depression. In a stepwise multiple regression analysis, cognitions about environmental threat, trait anxiety, focus on autonomic sensations, and general environmental sensitivity predicted MCS symptoms in the total sample, explaining 53% of the variance. These results support the hypothesis that trait negativity and mechanisms of symptom perception and symptom amplification contribute to the enhanced symptom reports of MCS individuals.     

Conservation and Management for Multiple Species: Integrating Field Research and Modeling into Management Decisions

Multiple-species reserves aim at supporting viable populations of selected species. Population viability analysis (PVA) is a group of methods for predicting such measures as extinction risk based on species-specific data. These methods include models that simulate the dynamics of a population or a metapopulation. A PVA model for the California gnatcatcher in Orange County was developed with landscape (GIS) data on the habitat characteristics and requirements and demographic data on population dynamics of the species. The potential applications of this model include sensitivity analysis that provides guidance for planning fieldwork, designing reserves, evaluating management options, and assessing human impact. The method can be extended to multiple species by combining habitat suitability maps for selected species with weights based on the threat faced by each species, and the contribution of habitat patches to the persistence of each species. These applications and extensions, together with the ability of the model to combine habitat and demographic data, make PVA a powerful tool for the design, conservation, and management of multiple species reserves.     

Fuzzy assessment of land suitability for scientific research reserves

Evaluating the characteristics of a set of sites as potential scientific research reserves is an example of land suitability assessment. Suitability in this case is based upon multiple criteria, many of which can be linguistically imprecise and often incompatible. Fuzzy logic is a useful method for characterizing imprecise suitability criteria and for combining criteria into an overall suitability rating. The Ecosystem Management Decision Support software combined a fuzzy logic knowledge base we developed to represent the assessment problem with a GIS database providing site-specific data for the assessment. Assessment of sites as a potential natural reserve for the new University of California campus at Merced demonstrates the benefits of fuzzy suitability assessment. The study was conducted in three stages of successively smaller assessment regions with increasingly fine spatial resolution and specificity of criteria. Several sites were identified that best satisfy the suitability criteria for a reserve to represent vernal pool habitat.     

Impact of Watershed Subdivision and Soil Data Resolution on SWAT Model Calibration and Parameter Uncertainty1

Abstract: Impact of watershed subdivision and soil data resolution on Soil Water Assessment Tool (SWAT) model calibration and parameter uncertainty is investigated by creating 24 different watershed model configurations for two study areas in northern Indiana. SWAT autocalibration tool is used to calibrate 14 parameters for simulating seven years of daily streamflow records. Calibrated parameter sets are found to be different for all 24 watershed configurations, however in terms of calibrated model output, their effect is minimal. In some cases, autocalibration is followed by manual calibration to correct for low flows, which were underestimated during autocalibration. In addition to normal validation using four years of streamflow data for each calibrated parameter set, cross‐validation (using a calibrated parameter set from one model configuration to validate observations using another configuration) is performed to investigate the effect of different model configurations on streamflow prediction. Results show that streamflow output during cross‐validation is not affected, thus highlighting the non‐unique nature of calibrated parameters in hydrologic modeling. Finally, parameter uncertainty is investigated by extracting good parameter sets during the autocalibration process. Parameter uncertainty analysis suggests that significant parameters show very narrow range of uncertainty across different watershed configurations compared with nonsignificant parameters. Results from recalibration of some configurations using only six significant parameters were comparable to that from calibration using 14 parameters, suggesting that including fewer significant parameters could reduce the uncertainty arising from model parameters, and also expedite the calibration process.