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Ranking desertification indicators using TOPSIS algorithm

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  • A. Sepehr
  • C. Zucca

Abstract

Desertification is the result of natural and anthropogenic processes, leading to degradation or loss of the land’s productivity and complexity. To assess the desertification status, integrated set of indicators must be identified. Indicators must provide synthetic information on threshold levels, status and evolution of relevant physical, chemical, biological and anthropogenic processes. Multi-criteria decision-making (MCDM) is a collection of methodologies to compare, select, or rank multiple alternatives that involve incommensurate attributes. Technique for order preference by similarity to ideal solution (TOPSIS) method is a multiple criteria method to identify solutions from a finite set. TOPSIS is an algorithm for determining the most preferable choices among the possible indicators that can be developed. The aim of this paper is to introduce TOPSIS as a decision-making method for the selection and integration of desertification indicators. The simulation case study presented here is related to the selection of the best set of indicators to monitor land degradation by remote sensing in three different countries (Brazil, Mozambique and Portugal), within the framework defined by the DesertWatch Extension project. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • A. Sepehr & C. Zucca, 2012. "Ranking desertification indicators using TOPSIS algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(3), pages 1137-1153, July.
    • Handle: RePEc:spr:nathaz:v:62:y:2012:i:3:p:1137-1153
    DOI: 10.1007/s11069-012-0139-z
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    References listed on IDEAS

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    1. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
    2. Olson, David L., 2001. "Comparison of three multicriteria methods to predict known outcomes," European Journal of Operational Research, Elsevier, vol. 130(3), pages 576-587, May.
    3. David Niemeijer & Rudolf Groot, 2008. "Framing environmental indicators: moving from causal chains to causal networks," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 10(1), pages 89-106, February.
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    1. Zhengyin Zhou & Xiaoling Wang & Ruirui Sun & Xuefei Ao & Xiaopei Sun & Mingrui Song, 2014. "Study of the comprehensive risk analysis of dam-break flooding based on the numerical simulation of flood routing. Part II: Model application and results," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 675-700, June.
    2. Hualin Xie & Yanwei Zhang & Zhilong Wu & Tiangui Lv, 2020. "A Bibliometric Analysis on Land Degradation: Current Status, Development, and Future Directions," Land, MDPI, vol. 9(1), pages 1-37, January.

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