Evaluation of the Habitat Suitability for Zhuji Torreya Based on Machine Learning Algorithms

被引:0
|
作者
Wu, Liangjun [1 ,2 ]
Yang, Lihui [3 ]
Li, Yabin [4 ]
Shi, Jian [5 ]
Zhu, Xiaochen [1 ]
Zeng, Yan [6 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Ecol & Appl Meteorol, Nanjing 210044, Peoples R China
[2] Nanning Meteorol Bur, Nanning 530029, Peoples R China
[3] Fujian Prov Climate Ctr, Fuzhou 350025, Peoples R China
[4] Heilongjiang Prov Climate Ctr, Harbin 150030, Peoples R China
[5] Zhuji Meteorol Bur, Zhuji 311800, Peoples R China
[6] Nanjing Joint Inst Atmospher Sci, China Meteorol Adm, Key Lab Transportat Meteorol, Nanjing 210041, Peoples R China
来源
AGRICULTURE-BASEL | 2024年 / 14卷 / 07期
关键词
Torreya; habitat suitability zoning; machine learning model; CROP CLASSIFICATION; FEATURE-SELECTION;
D O I
10.3390/agriculture14071077
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Torreya, with its dual roles in both food and medicine, has faced multiple challenges in its cultivation in Zhuji city due to frequent global climate disasters in recent years. Therefore, conducting a study on suitable zoning for Torreya habitats based on climatic, topographic, and soil factors is highly important. In this study, we utilized the latitude and longitude coordinates of Torreya distribution points and ecological factor raster data. We thoroughly analyzed the ecological environmental characteristics of the climate, topography, and soil at Torreya distribution points via both physical modeling and machine learning methods. Zhuji city was classified into suitable, moderately suitable, and unsuitable zones to determine regions conducive to Torreya growth. The results indicate that suitable zones for Torreya cultivation in Zhuji city are distributed mainly in mountainous and hilly areas, while unsuitable zones are found predominantly in central basins and northern river plain networks. Moderately suitable zones are located in transitional areas between suitable and unsuitable zones. Compared to climatic factors, soil and topographic factors more significantly restrict Torreya cultivation. Machine learning algorithms can also achieve suitability zoning with a more concise and efficient classification process. In this study, the random forest (RF) algorithm demonstrated greater predictive accuracy than the support vector machine (SVM) and naive Bayes (NB) algorithms, achieving the best classification results.
引用
收藏
页数:17
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