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

被引:3
作者
Han, Ying [1 ]
He, Yongjian [1 ]
Liang, Zhuoran [2 ]
Shi, Guoping [1 ]
Zhu, Xiaochen [3 ]
Qiu, Xinfa [3 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China
[2] Hangzhou Meteorol Bur, Hangzhou 310000, Peoples R China
[3] Nanjing Univ Informat Sci & Technol, Sch Appl Meteorol, Nanjing 210044, Peoples R China
来源
AGRICULTURE-BASEL | 2023年 / 13卷 / 02期
基金
中国国家自然科学基金;
关键词
random forest; machine learning; GIS; tea frost hazard; hazard risk assessment; hazard warning;
D O I
10.3390/agriculture13020327
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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.
引用
收藏
页数:14
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