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Risk Assessment and Application of Tea Frost Hazard in Hangzhou City Based on the Random Forest Algorithm

Author

Listed:
  • Ying Han

    (School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Yongjian He

    (School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Zhuoran Liang

    (Hangzhou Meteorological Bureau, Hangzhou 310000, China)

  • Guoping Shi

    (School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Xiaochen Zhu

    (School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Xinfa Qiu

    (School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

Using traditional tea frost hazard risk assessment results as sample data, the four indicators of minimum temperature, altitude, tea planting area, and tea yield were selected to consider the risk of hazard-causing factors, the exposure of hazard-bearing bodies, and the vulnerability of hazard-bearing bodies. The random forest algorithm was used to construct the frost hazard risk assessment model of Hangzhou tea, and hazard risk assessment was carried out on tea with different cold resistances in Hangzhou. The model’s accuracy reached 93% after training, and the interpretation reached more than 0.937. According to the risk assessment results of tea with different cold resistance, the high-risk areas of weak cold resistance tea were the most, followed by medium cold resistance and the least strong cold resistance. Compared with the traditional method, the prediction result of the random forest model has a deviation of only 1.57%. Using the random forest model to replace the artificial setting of the weight factor in the traditional method has the advantages of simple operation, high time efficiency, and high result accuracy. The prediction results have been verified by the existing hazard data. The model conforms to the actual situation and has certain guiding for local agricultural production and early warning of hazards.

Suggested Citation

  • Ying Han & Yongjian He & Zhuoran Liang & Guoping Shi & Xiaochen Zhu & Xinfa Qiu, 2023. "Risk Assessment and Application of Tea Frost Hazard in Hangzhou City Based on the Random Forest Algorithm," Agriculture, MDPI, vol. 13(2), pages 1-14, January.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:327-:d:1050311
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    References listed on IDEAS

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    1. Jie Xu & Suri Guga & Guangzhi Rong & Dao Riao & Xingpeng Liu & Kaiwei Li & Jiquan Zhang, 2021. "Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning," Agriculture, MDPI, vol. 11(7), pages 1-16, June.
    2. Sadeeka Layomi Jayasinghe & Lalit Kumar & Janaki Sandamali, 2019. "Assessment of Potential Land Suitability for Tea ( Camellia sinensis (L.) O. Kuntze) in Sri Lanka Using a GIS-Based Multi-Criteria Approach," Agriculture, MDPI, vol. 9(7), pages 1-25, July.
    3. Abiodun A. Ogundeji, 2022. "Adaptation to Climate Change and Impact on Smallholder Farmers’ Food Security in South Africa," Agriculture, MDPI, vol. 12(5), pages 1-16, April.
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    Cited by:

    1. Liangjun Wu & Lihui Yang & Yabin Li & Jian Shi & Xiaochen Zhu & Yan Zeng, 2024. "Evaluation of the Habitat Suitability for Zhuji Torreya Based on Machine Learning Algorithms," Agriculture, MDPI, vol. 14(7), pages 1-17, July.

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