A comparative analysis of three multi-criteria decision-making methods for land suitability assessment

被引:0
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作者
Farahnaz Rashidi
Shadi Sharifian
机构
[1] Education and Extension Organization (AREEO),Research Institute of Forests and Rangelands, Agricultural Research
[2] University of Payam Noor (PNU),undefined
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关键词
Afforestation; AHP; Fuzzy AHP; Buckley method; Multi-criteria decision-making; Siahpoosh Watershed; TOPSIS;
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摘要
Natural resource management relies on identifying the ecological constraints, assessing land suitability, and considering the socio-economic demands in the region. However, in many developing countries, natural resources are extensively overused in favor of economic growth. This is due to the fact that conservation and natural constraints are not always taken into consideration during the planning phase, especially when the decision-making process is mainly influenced by political or economical views. To avoid these subjective plannings, environmental planners are encouraged to consider quantitative planning approaches that can integrate environmental, social, economic, and political matters through a non-bias procedure. The present study, therefore, examines the application of three multi-criteria decision-making methods (MCDM), namely, analytic hierarchical process (AHP), fuzzy analytic hierarchical process (fuzzy AHP), and technique for order of preference by similarity to ideal solution (TOPSIS), for the assessment of land suitability afforestation. Siahpoosh Watershed, in Iran, is used as a case study to compare three MCDM methods. To achieve this, a set of land suitability criteria (i.e., slope, elevation, aspect, soil texture, soil depth, drainage, erosion, temperature, rainfall, and vegetation type and cover) was defined and weighted using the AHP and fuzzy AHP methods. TOPSIS was then used to prioritize and rank the suitability of different sections of the study area for afforestation. The study demonstrates that the fuzzy AHP method combined with TOPSIS generates more reliable outcomes than the AHP method. The results could be useful for making more informed decisions about afforestation in the region.
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