Feizizadeh Bakhtiar, Blaschke Thomas
Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, Austria; Center for Remote Sensing and GIS, University of Tabriz, Tabriz, Iran.
Department of Geoinformatics - Z_GIS, University of Salzburg , Salzburg , Austria.
Int J Geogr Inf Sci. 2014 Mar 4;28(3):610-638. doi: 10.1080/13658816.2013.869821. Epub 2014 Jan 20.
GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster-Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster-Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results.
基于地理信息系统（GIS）的多准则决策分析（MCDA）方法越来越多地应用于滑坡易发性制图。然而，与MCDA技术相关的不确定性可能会显著影响结果。这有时可能导致不准确的结果和不良后果。本文介绍了一种新的基于GIS的MCDA方法。我们通过不确定性分析说明了在决策过程中应用不同MCDA方法的后果。结合蒙特卡罗模拟（MCS）和Dempster-Shafer理论，对三种GIS-MCDA方法进行了分析，用于伊朗乌尔米耶湖盆地的滑坡易发性制图（LSM），该地区极易发生滑坡灾害。该方法包括三个阶段。首先，对LSM标准进行排序，并基于MCS进行敏感性分析以模拟误差传播。所得权重通过概率密度函数表示。因此，在第二阶段，使用三种MCDA方法，即层次分析法（AHP）、加权线性组合（WLC）和有序加权平均（OWA）来生成滑坡易发性地图。在第三阶段，进行精度评估并测量不同结果的不确定性。我们基于（1）Dempster-Shafer理论和（2）使用已知滑坡清单及其基于IRS-ID卫星图像的基于对象的图像分析的各自覆盖范围对结果进行验证，比较了三种MCDA方法的精度。本研究结果表明，通过GIS和MCDA模型的集成，可以确定选择合适的LSM方法的策略。此外，我们的研究结果表明，MCDA和MCS的集成可以显著提高结果的准确性。在LSM中，AHP方法表现最佳，而OWA在可靠性评估中表现更好。WLC操作产生的结果较差。
