Natural Hazards

, Volume 93, Issue 1, pp 339–347 | Cite as

Two precautions of entropy-weighting model in drought-risk assessment

  • Fanghui Yi
  • Chen Li
  • Yan Feng
Original Paper


Two disadvantages of the entropy-weighting model (EWM) in drought-risk assessment are presented through two typical examples in this paper. (1) For distortion in the normalization process, entropy defined by EWM cannot represent the indicator’s dipartite degree correctly when too many zero values exist in the observation data. (2) Given that EWM neglects the indicator’s practical significance in drought-risk assessment, the indicator’s dipartite degree cannot correctly represent its importance when observation data are concentrated in the worst category. These two problems lead to unjustified drought-risk assessment results. Therefore, the features of observation data should be checked before weighting. If the indicator’s observation values are concentrated in the worst domain or numerous zero values exist, then EWM should be applied cautiously.


Entropy-weighting method Drought-risk assessment Observation data checking 



This work is supported by the Natural Science Foundation of Water Resource Department of Hunan Government (No. 201524507).

Supplementary material

11069_2018_3303_MOESM1_ESM.doc (41 kb)
Supplementary material 1 (DOC 41 kb)


  1. Chang J, Li Y, Wang Y et al (2016) Copula-based drought risk assessment combined with an integrated index in the Wei River Basin, China. J Hydrol 540:824–834CrossRefGoogle Scholar
  2. Huang Z (2014) Evaluating intelligent residential communities using multi-strategic weighting method in china. Energy Build 69(69):144–153CrossRefGoogle Scholar
  3. Huang S, Chang J, Leng G et al (2015) Integrated index for drought assessment based on variable fuzzy set theory: a case study in the yellow river basin, China. J Hydrol 527:608–618CrossRefGoogle Scholar
  4. Liu L, Zhou J, An X et al (2010) Using fuzzy theory and information entropy for water quality assessment in three gorges region, china. Expert Syst Appl 37(3):2517–2521CrossRefGoogle Scholar
  5. Peng Y, Lai Y, Li X, Zhang X (2015) An alternative model for measuring the sustainability of urban regeneration: the way forward. J Clean Prod 109:76–83CrossRefGoogle Scholar
  6. Waseem M, Ajmal M, Kim TW (2015) Development of a new composite drought index for multivariate drought assessment. J Hydrol 527:30–37CrossRefGoogle Scholar
  7. Xu X (2004) A note on the subjective and objective integrated approach to determine attribute weights. Eur J Oper Res 156(2):530–532CrossRefGoogle Scholar
  8. Zhou Y, Xing X, Fang K et al (2013) Environmental efficiency analysis of power industry in China based on an entropy SBM model. Energy Policy 57(7):68–75CrossRefGoogle Scholar
  9. Zou ZH, Yun Y, Sun JN (2006) Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. J Environ Sci 18(5):1020–1023CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Water Resources and Hydropower EngineeringWuhan UniversityWuhanChina
  2. 2.School of Civil Engineering and ArchitectureNanchang UniversityNanchangChina
  3. 3.Key Laboratory of Poyang Lake Environment and Resource Utilization (Nanchang University)Ministry of EducationNanchangChina

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