IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v37y2023i2d10.1007_s11269-022-03391-y.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03391-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-022-03391-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
    2. Dai, Meng & Huang, Shengzhi & Huang, Qiang & Leng, Guoyong & Guo, Yi & Wang, Lu & Fang, Wei & Li, Pei & Zheng, Xudong, 2020. "Assessing agricultural drought risk and its dynamic evolution characteristics," Agricultural Water Management, Elsevier, vol. 231(C).
    3. Abdol Rassoul Zarei & Mohammad Mehdi Moghimi & Elham Koohi, 2021. "Sensitivity Assessment to the Occurrence of Different Types of Droughts Using GIS and AHP Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3593-3615, September.
    4. Junfei Chen & Menghua Deng & Lu Xia & Huimin Wang, 2017. "Risk Assessment of Drought, Based on IDM-VFS in the Nanpan River Basin, Yunnan Province, China," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
    5. Moumita Palchaudhuri & Sujata Biswas, 2016. "Application of AHP with GIS in drought risk assessment for Puruliya district, India," 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. 84(3), pages 1905-1920, December.
    6. 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.
    7. Huafeng Xu & Kexin Xu & Yingjie Yang, 2021. "Risk assessment model of agricultural drought disaster based on grey matter-element analysis theory," 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. 107(3), pages 2693-2707, July.
    8. Hui An & Wenjing Yang & Jin Huang & Ai Huang & Zhongchi Wan & Min An, 2020. "Identify and Assess Hydropower Project’s Multidimensional Social Impacts with Rough Set and Projection Pursuit Model," Complexity, Hindawi, vol. 2020, pages 1-16, November.
    9. Xiaohong Yu & Haiyan Xu & Shengqiao Wang & Lei Xie, 2021. "Vulnerability Assessment and Spatiotemporal Differentiation of Provinces Tourism Economic System Based on the Projection Pursuit Clustering Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-12, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wentong Yang & Liyuan Zhang & Chunlei Liang, 2023. "Agricultural drought disaster risk assessment in Shandong Province, China," 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. 118(2), pages 1515-1534, September.
    2. Gruber, Lutz F. & West, Mike, 2017. "Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models," Econometrics and Statistics, Elsevier, vol. 3(C), pages 3-22.
    3. Salois, Matthew & Moss, Charles, 2010. "An Information Approach to the Dynamics in Farm Income: Implications for Farmland Markets," MPRA Paper 26850, University Library of Munich, Germany.
    4. Piotr Fiszeder & Witold Orzeszko, 2012. "Nonparametric Verification of GARCH-Class Models for Selected Polish Exchange Rates and Stock Indices," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(5), pages 430-449, November.
    5. Zhang, Yitong & Hao, Zengchao & Zhang, Yu, 2023. "Agricultural risk assessment of compound dry and hot events in China," Agricultural Water Management, Elsevier, vol. 277(C).
    6. Dionisio, Andreia & Menezes, Rui & Mendes, Diana & Vidigal Da Silva, Jacinto, 2007. "Nonlinear Dynamics Within Macroeconomic Factors And Stock Market In Portugal, 1993-2003," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 7(2), pages 57-70.
    7. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    8. Omidreza Mikaili & Majid Rahimzadegan, 2022. "Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province, Iran)," 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. 111(3), pages 2511-2529, April.
    9. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    10. Luca Bagnato & Valerio Potì & Maria Zoia, 2015. "The role of orthogonal polynomials in adjusting hyperpolic secant and logistic distributions to analyse financial asset returns," Statistical Papers, Springer, vol. 56(4), pages 1205-1234, November.
    11. Sanjib Mondal & Pritam Ghosh & Pratima Rohatgi, 2023. "Village‐level livelihood security: A case study on a wasteland‐dominated forest fringe region of rural India," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(5), pages 1019-1036, June.
    12. Kang, Yanfei & Hyndman, Rob J. & Smith-Miles, Kate, 2017. "Visualising forecasting algorithm performance using time series instance spaces," International Journal of Forecasting, Elsevier, vol. 33(2), pages 345-358.
    13. Tuan Minh Cao & Sang Hyeon Lee & Ji Yong Lee, 2023. "The Impact of Natural Disasters and Pest Infestations on Technical Efficiency in Rice Production: A Study in Vietnam," Sustainability, MDPI, vol. 15(15), pages 1-16, July.
    14. Kwang-il Choe & Joshua Krausz & Kiseok Nam, 2011. "Technical trading rules for nonlinear dynamics of stock returns: evidence from the G-7 stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 36(3), pages 323-353, April.
    15. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    16. Tao He & Wenya Zhang & Hanwen Zhang & Jinliang Sheng, 2023. "Estimation of Manure Emissions Issued from Different Chinese Livestock Species: Potential of Future Production," Agriculture, MDPI, vol. 13(11), pages 1-17, November.
    17. Samantaray, Alok Kumar & Ramadas, Meenu & Panda, Rabindra Kumar, 2022. "Changes in drought characteristics based on rainfall pattern drought index and the CMIP6 multi-model ensemble," Agricultural Water Management, Elsevier, vol. 266(C).
    18. Junfei Chen & Juan Ji & Huimin Wang & Menghua Deng & Cong Yu, 2020. "Risk Assessment of Urban Rainstorm Disaster Based on Multi-Layer Weighted Principal Component Analysis: A Case Study of Nanjing, China," IJERPH, MDPI, vol. 17(15), pages 1-19, July.
    19. Wei Pei & Cuizhu Tian & Qiang Fu & Yongtai Ren & Tianxiao Li, 2022. "Risk analysis and influencing factors of drought and flood disasters in China," 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. 110(3), pages 1599-1620, February.
    20. Casarin, Roberto & Costola, Michele, 2019. "Structural changes in large economic datasets: A nonparametric homogeneity test," Economics Letters, Elsevier, vol. 176(C), pages 55-59.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:37:y:2023:i:2:d:10.1007_s11269-022-03391-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.