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Assessment of agricultural drought loss using a skewed grey cloud ordered clustering model

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  • Dongxing Zhang

    (North China University of Water Resources and Electric Power)

  • Dang Luo

    (North China University of Water Resources and Electric Power
    North China University of Water Resources and Electric Power)

Abstract

A skewed grey cloud ordered clustering model is proposed to overcome the problem that the existing grey clustering assessment results are inconsistent with objective reality due to the greyness, fuzziness, and randomness of information in the process of agricultural drought loss assessment. The skewed grey cloud model is built by split-combination possibility function, which is based on the normal grey cloud model. The grey cloud constraint interval is solved using the outer envelope curve and expectation curve of the skewed grey cloud, and the classification weight of the index is derived using the Gini coefficient. Then, the grey cloud constraint interval and the weight coefficient of comprehensive decision measure are combined to establish the ordered clustering criterion of skewed grey cloud. The degree of agricultural drought loss in Henan Province is examined from 2006 to 2019, and the assessment results are compared to those of the grey clustering model, normal grey cloud clustering model and comprehensive index method. The results reveal that the skewed grey cloud model’s evaluation results are more in line with the actual scenario of agricultural drought in Henan Province, showing the model’s applicability and usefulness.

Suggested Citation

  • Dongxing Zhang & Dang Luo, 2022. "Assessment of agricultural drought loss using a skewed grey cloud ordered clustering model," 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. 114(3), pages 2787-2810, December.
    • Handle: RePEc:spr:nathaz:v:114:y:2022:i:3:d:10.1007_s11069-022-05491-9
    DOI: 10.1007/s11069-022-05491-9
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    References listed on IDEAS

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    2. Xiangyu Li & Tianjie Lei & Jing Qin & Jiabao Wang & Weiwei Wang & Baoyin Liu & Dongpan Chen & Guansheng Qian & Li Zhang & Jingxuan Lu, 2023. "The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation," Land, MDPI, vol. 12(2), pages 1-14, January.

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