Uncertainty analysis in a GIS-based multi-criteria analysis tool for river catchment management

Chen, H.; Wood, Michael D.; Linstead, Conor; Maltby, Edward

doi:10.1016/j.envsoft.2010.09.005

Cited by 114 publications

(69 citation statements)

References 31 publications

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“…To combine the three flood hazard components, a relative importance or weight should be assigned to each component. Due to its importance, stakeholders and experts should be engaged into this step of MCDM problems [50,51]. Specially, in a SFM framework, stakeholder participation is a core element [25,52,53].…”

Section: Sustainability-based Flood Hazard Mappingmentioning

confidence: 99%

Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed

2017

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An integrated framework is presented for sustainability-based flood hazard mapping of the Swannanoa River watershed in the state of North Carolina, U.S. The framework uses a hydrologic model for rainfall-runoff transformation, a two-dimensional unsteady hydraulic model flood simulation and a GIS-based multi-criteria decision-making technique for flood hazard mapping. Economic, social, and environmental flood hazards are taken into account. The importance of each hazard is quantified through a survey to the experts. Utilizing the proposed framework, sustainability-based flood hazard mapping is performed for the 100-year design event. As a result, the overall flood hazard is provided in each geographic location. The sensitivity of the overall hazard with respect to the weights of the three hazard components were also investigated. While the conventional flood management approach is to assess the environmental impacts of mitigation measures after a set of feasible options are selected, the presented framework incorporates the environmental impacts into the analysis concurrently with the economic and social influences. Thereby, it provides a more sustainable perspective of flood management and can greatly help the decision makers to make better-informed decisions by clearly understanding the impacts of flooding on economy, society and environment.

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Section: Sustainability-based Flood Hazard Mappingmentioning

confidence: 99%

Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed

2017

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“…The capability of Multicriteria Decision Analysis (MCDA), when integrated with GIS, makes GIS-based MCDA one of the most useful and robust methods for spatial decision making (CHEN et al 2011). MCDA procedures utilizing geographical data consider the user's preferences, manipulate the data, and combine preferences with the data according to specified decision rules (FEIZIZADEH & BLASCHKE 2014).…”

Section: Sensitivity and Uncertainty Analysis Approach For Gis-mcda Bmentioning

confidence: 99%

“…One specific approach to uncertainty analysis applicable to MCDA is to gain a sense of error or uncertainty in the predictions, given the uncertainty in the criterion weights (BENKE & PELIZARO 2010). The uncertainty of weights lies in the subjective expert or stakeholder judgement of the relative importance of different attributes, given the range of their impacts (CHEN et al 2011). FEIZIZADEH et al (2014a implemented the GIS-MCDA based AHP-Monte Carlo Simulation (MCS) as an effort to deal with subjectivity in criterion weights contributing to potential uncertainty of model outcomes.…”

Section: Implementation Of Ahp-monte Carlo Simulationmentioning

confidence: 99%

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Sensitivity and Uncertainty Analysis Approach for GIS-MCDA Based Economic Vulnerability Assessment

Feizizadeh¹,

Kienberger²,

Kamran³

2015

giforum

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This research aims to employ a novel methodology for modelling uncertainty in the GIS environment. The spatially explicit sensitivity and uncertainty analysis was applied on Multicriteria Decision Analysis (MCDA) for an economic vulnerability assessment within the Salzach Basin. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimize the associated uncertainty within an economic vulnerability assessment. In order to achieve this objective, the following methodology, composed four steps, was applied: (1) computation of criteria weights using Analytic Hierarchy Process (AHP); (2) Monte Carlo Simulation was applied for computing the uncertainties associated with AHP weights; (3) the Global Sensitivity Analysis (GSA) was employed in the form of the model-independent method of output variance decomposition, in which the variability of vulnerability maps is apportioned to every criterion weight, generating one first-order (S) and one total-effect (ST) sensitivity index map per criterion weight; and (4) an Ordered Weighted Averaging method was applied for producing vulnerability maps. The results of this research demonstrated the robustness of spatially explicit GSA for minimizing the uncertainty associated with GIS-MCDA models. According to the achieved results, we conclude that applying the variance based GSA leads to a spatial and statistical assessment of the importance of each input factor on the outcome of the GIS-MCDA method, which allows us to introduce and recommend GIS based GSA as a useful methodology for minimizing uncertainty of GIS-MCDA.

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“…Moreover, given the growing complexity and uncertainty in many decision situations of flood risk management, personal experience and single models are not enough to provide decision support for managers. Therefore, comprehensive flood risk management has widely been used in flood insurance, flood plains management, disaster evacuation, disaster warning, disaster evaluation, flood influence evaluation, and improving the public's flood risk awareness and understanding of flood disasters (Zeng et al, 2007;Ray, 2007;EscuderBueno et al, 2012;Chen et al, 2011;Schinke et al, 2012;Li et al, 2012;Evers et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Efficient GIS-based model-driven method for flood risk management and its application in central China

Liu

Zhou

Song³

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. In recent years, an important development in flood management has been the focal shift from flood protection towards flood risk management. This change greatly promoted the progress of flood control research in a multidisciplinary way. Moreover, given the growing complexity and uncertainty in many decision situations of flood risk management, traditional methods, e.g., tight-coupling integration of one or more quantitative models, are not enough to provide decision support for managers. Within this context, this paper presents a beneficial methodological framework to enhance the effectiveness of decision support systems, through the dynamic adaptation of support regarding the needs of the decision-maker. In addition, we illustrate a loose-coupling technical prototype for integrating heterogeneous elements, such as multi-source data, multidisciplinary models, GIS tools and existing systems. The main innovation is the application of model-driven concepts, which put the system in a state of continuous iterative optimization. We define the new system as a model-driven decision support system (MDSS ). Two characteristics that differentiate the MDSS are as follows: (1) it is made accessible to non-technical specialists; and (2) it has a higher level of adaptability and compatibility. Furthermore, the MDSS was employed to manage the flood risk in the Jingjiang flood diversion area, located in central China near the Yangtze River. Compared with traditional solutions, we believe that this model-driven method is efficient, adaptable and flexible, and thus has bright prospects of application for comprehensive flood risk management.

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Uncertainty analysis in a GIS-based multi-criteria analysis tool for river catchment management

Cited by 114 publications

References 31 publications

Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed

Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed

Sensitivity and Uncertainty Analysis Approach for GIS-MCDA Based Economic Vulnerability Assessment

Efficient GIS-based model-driven method for flood risk management and its application in central China

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