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Study on Agricultural Drought Risk Assessment Based on Information Entropy and a Cluster Projection Pursuit Model

Author

Listed:
  • Wei Pei

    (Northeast Agricultural University)

  • Lei Hao

    (Northeast Agricultural University)

  • Qiang Fu

    (Northeast Agricultural University
    Northeast Agricultural University
    Northeast Agricultural University)

  • Yongtai Ren

    (Northeast Agricultural University)

  • Tianxiao Li

    (Northeast Agricultural University)

Abstract

The projection pursuit model is an important tool for processing high-dimensional nonnormal and nonlinear data, and it has a wide range of applications. In this paper, clustering analysis and information entropy theory are simultaneously introduced into the projection pursuit model. The K-means clustering method is used to cluster high-dimensional data, and information entropy is used to measure the overall dispersion and local aggregation of projection data. A projection pursuit model based on clustering and information entropy is proposed. The new model has both the classification advantages of clustering analysis and the evaluation advantages of the projection pursuit model. In the case test, different cases are tested by using the Shapiro-Wilk test, Kolmogorov-Smirnov test, Epps-Pulley test, etc. The results show that in most cases, the new model is better than the original model, and the advantage is clearer when the data dimension is higher. Finally, the new model is applied to the agricultural drought risk assessment of Qiqihar, a major grain-producing area in China that is prone to drought. The regional agricultural drought risk shows a downward trend over time. Spatially, the risk of the central and southern regions is low, while the risk of the northern and western regions is high. Regional ability to resist disasters is the main reason for the spatial and temporal differences. This paper extends projection pursuit model theory, analyzes the characteristics of the spatiotemporal variation in regional agricultural drought risk, and provides a reference for identifying the hidden dangers of drought disasters and disaster reduction.

Suggested Citation

  • Wei Pei & Lei Hao & Qiang Fu & Yongtai Ren & Tianxiao Li, 2023. "Study on Agricultural Drought Risk Assessment Based on Information Entropy and a Cluster Projection Pursuit Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 619-638, January.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:2:d:10.1007_s11269-022-03391-y
    DOI: 10.1007/s11269-022-03391-y
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    References listed on IDEAS

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    1. Dong Liu & Chunlei Liu & Qiang Fu & Tianxiao Li & Muhammad Imran Khan & Song Cui & Muhammad Abrar Faiz, 2018. "Projection Pursuit Evaluation Model of Regional Surface Water Environment Based on Improved Chicken Swarm Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1325-1342, March.
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