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.

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基于GIS的多标准滑坡敏感性绘图的不确定性和敏感性分析方法

基于 GIS 的多标准决策分析 (MCDA) 方法越来越多地用于滑坡敏感性绘图。然而，与 MCDA 技术相关的不确定性可能会显着影响结果。这有时可能会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 理论的三种 GIS-MCDA 方法分析了伊朗乌尔米亚湖盆地的滑坡敏感性绘图 (LSM)，该盆地极易受到滑坡灾害的影响。该方法包括三个阶段。第一的，对 LSM 标准进行排序，并实施敏感性分析以模拟基于 MCS 的错误传播。结果权重通过概率密度函数表示。相应地，在第二阶段，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），用于生成滑坡敏感性图。第三阶段，进行精度评估，测量不同结果的不确定性。我们基于 (1) Dempster-Shafer 理论和 (2) 基于 IRS-ID 卫星的基于对象的图像分析使用已知滑坡清单及其各自覆盖范围对结果的验证，比较了三种 MCDA 方法的准确性图片。这项研究的结果表明，通过 GIS 和 MCDA 模型的集成，可以确定为 LSM 选择合适方法的策略。此外，我们的研究结果表明，MCDA 和 MCS 的集成可以显着提高结果的准确性。在 LSM 中，AHP 方法表现最好，而 OWA 在可靠性评估中表现更好。WLC 操作的结果很差。