USING THE GEOSPATIAL MULTI-CRITERIA DECISION ANALYSIS MODEL AND METHODS FOR SOIL DEGRADATION RISK MAPPING
DOI:
https://doi.org/10.20998/2079-0023.2024.01.03Keywords:
multi-criteria decision analysis, Analytic Hierarchy Process, Geographic Information System, spatial modeling, expert assessments, soil degradation riskAbstract
Modern methods of spatial analysis and modeling are increasingly being combined with decision-making methods and fuzzy set theory. The latter are actively integrated into the environment of Geographic Information Systems (GIS), such as well-known ones like ArcGIS or QGIS, in the form of separate tools, plugins, or Python scripts. Decision-making methods allow structuring the problem in geographical space, as well as taking into account the knowledge and judgments of experts and the preferences of the decision-maker in determining the priorities of alternative solutions. This paper provides a description of a geospatial multi-criteria decision analysis model, which allows addressing a wide range of ecological and socio-economic issues. An example of applying this model to map soil degradation risk in Ukraine is presented in the paper. According to the object-spatial approach, the properties of a territory are determined as the result of the action (impact) of a set of objects (processes) belonging to this territory. The territory is represented as a two-dimensional discrete grid, each point of which (local area) is an alternative. The set of local areas of the territory constitutes the set of alternatives. The representation of the territory model as a system of objects and relationships between them allows justifying the choice of a set of criteria (factors) for assessing soil degradation risk. Each criterion is a separate raster layer of the map. To build a hierarchical decision-making structure and calculate the importance coefficients of the criteria, the Analytic Hierarchy Process (AHP) method is used. To account for uncertainty in assessments and judgments of experts at the stages of standardization of alternative attributes by different criteria and aggregation of their assessments, expert membership functions for fuzzy sets and fuzzy quantifiers are applied. The particular feature of the proposed multi-criteria decision analysis model is its low computational complexity and ease of integration into the GIS environment.
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