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Disaster Risk Regionalization and Prediction of Corn Thrips Combined with Cloud Model: A Case Study of Shandong Province, China

Author

Listed:
  • Yanan Zuo

    (College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China)

  • Fengxiang Jin

    (College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China)

  • Min Ji

    (College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China)

  • Zhenjin Li

    (College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China)

  • Jiutao Yang

    (Shandong Agricultural Technology Extension Center, Jinan 250000, China)

Abstract

Corn thrips do serious harm to the yield and quality of corn. In this paper, the Shandong Province of China was taken as the study area. Based on the data of the occurrence of corn thrips in Shandong Province, a risk regionalization model was established by using eight indicators under four categories of hazard, sensitivity, vulnerability and the disaster prevention and mitigation capacity of diseases and pests on a monthly time scale. Firstly, the cloud model was introduced to determine the weight of each indicator, and then the risk regionalization of the corn thrips disaster in Shandong Province was carried out using the weighted percentage method, the weighted comprehensive evaluation method and the natural disaster risk index method. Finally, combined with the collected data, the disaster prediction of corn thrip occurrence degree was realized based on multiple linear regression, genetic algorithm optimized back-propagation neural network and genetic algorithm optimized support vector machine methods. The results show that: (1) the risk of Corn thrips disaster is mainly concentrated in the central and western parts of Shandong Province. Heze City is a high-risk area. Liaocheng City, Dezhou City, Jinan City and Weifang City are relatively high-risk areas. (2) By comparing the prediction accuracy of the three models, it was determined that the genetic algorithm optimized support vector machine model has the best effect, with an average accuracy of 79.984%, which is 7.013% and 22.745% higher than that of the multiple linear regression and genetic algorithm optimized back-propagation neural network methods, respectively. The results of this study can provide a scientific basis for fine prevention of corn thrips in Shandong Province.

Suggested Citation

  • Yanan Zuo & Fengxiang Jin & Min Ji & Zhenjin Li & Jiutao Yang, 2023. "Disaster Risk Regionalization and Prediction of Corn Thrips Combined with Cloud Model: A Case Study of Shandong Province, China," Land, MDPI, vol. 12(3), pages 1-20, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:3:p:709-:d:1101552
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    References listed on IDEAS

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    1. Qiqi Chen & Junbiao Zhang & Lu Zhang, 2015. "Risk Assessment, Partition and Economic Loss Estimation of Rice Production in China," Sustainability, MDPI, vol. 7(1), pages 1-21, January.
    2. Zhiguo Shao & Li Zhang & Chuanfeng Han & Lingpeng Meng, 2022. "Measurement and Prediction of Urban Land Traffic Accessibility and Economic Contact Based on GIS: A Case Study of Land Transportation in Shandong Province, China," IJERPH, MDPI, vol. 19(22), pages 1-15, November.
    3. Bhuvaneswari Madasamy & Paramasivan Balasubramaniam & Ritaban Dutta, 2020. "Microclimate-Based Pest and Disease Management through a Forewarning System for Sustainable Cotton Production," Agriculture, MDPI, vol. 10(12), pages 1-12, December.
    4. Naveen P Singh & Bhawna Anand & Surendra Singh & S K Srivastava & Ch Srinivasa Rao & K V Rao & S K Bal, 2021. "Correction to: Synergies and trade-offs for climate-resilient agriculture in India: an agro-climatic zone assessment," Climatic Change, Springer, vol. 165(3), pages 1-1, April.
    5. Naveen P Singh & Bhawna Anand & Surendra Singh & S K Srivastava & Ch Srinivasa Rao & K V Rao & S K Bal, 2021. "Synergies and trade-offs for climate-resilient agriculture in India: an agro-climatic zone assessment," Climatic Change, Springer, vol. 164(1), pages 1-26, January.
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