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

Ying Han, Yongjian He (), Zhuoran Liang, Guoping Shi, Xiaochen Zhu and Xinfa Qiu
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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

Agriculture, 2023, vol. 13, issue 2, 1-14

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.

Keywords: random forest; machine learning; GIS; tea frost hazard; hazard risk assessment; hazard warning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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