Abstract

ABSTRACTGeographic information system (GIS)-based multi-criteria decision analysis (MCDA) is commonly used to solve a range of complex spatial problems. We have incorporated an analytical network process (ANP) as a central part of MCDA and used this approach for land subsidence susceptibility mapping (LSSM). We used an ANP approach in combination with the concepts of global sensitivity and uncertainty analyses, in order to minimize any associated errors. This approach was tested for a LSSM application in north-western Iran, using a methodology that consisted of three distinct phases. An ANP approach was used in the first phase to derive criteria weightings by analysing relevant criteria, which included topographic, hydrologic, climatologic, geological, and anthropological indicators. The second phase involved evaluating the uncertainty and sensitivity of areas susceptible to subsidence as a function of the derived weightings, using Monte Carlo simulations. The third phase then used a land subsidence inventory database to validate the results and to measure any improvement in accuracy following sensitivity analysis. Results indicated a 6% improvement in the accuracy of the ANP method as a result of using an integrated global sensitivity analysis.

Highlights

  • Geographic information system (GIS)-based multi-criteria decision analysis (MCDA) is a powerful modelling methodology in the spatial sciences

  • Building on the results of this earlier work we have aimed to address the uncertainties associated with particular components of GIS-MCDA, and to define a unified approach to uncertainty and sensitivity analyses by means of a global sensitivity analysis (GSA)

  • In the first analysis approach, the weightings calculated using the analytic network process (ANP) technique were applied to produce a land subsidence susceptibility map. This approach was based on the common GISMCDA method for generating base maps, in order to be able to compare them with the results of the second approach

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Summary

Introduction

Geographic information system (GIS)-based multi-criteria decision analysis (MCDA) is a powerful modelling methodology in the spatial sciences. The GIS-MCDA method combines data derived from a number of geographical factors into a single index of evaluation (Chen et al 2010a). The analytic network process (ANP) is one of the most common GIS-MCDA techniques and has been widely used to derive criteria weightings in modelling tasks (Saaty 1996a). This method is a variation of the analytic hierarchy process (AHP) that was developed in the context of network approaches in order to minimize the errors involved in a traditional AHP (Chen and Yang 2011).

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