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

Liangjun Wu, Lihui Yang, Yabin Li, Jian Shi, Xiaochen Zhu () and Yan Zeng
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Liangjun Wu: School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Lihui Yang: Fujian Provincial Climate Center, Fuzhou 350025, China
Yabin Li: Heilongjiang Provincial Climate Center, Harbin 150030, China
Jian Shi: Zhuji Meteorological Bureau, Zhuji 311800, China
Xiaochen Zhu: School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yan Zeng: Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China

Agriculture, 2024, vol. 14, issue 7, 1-17

Abstract: 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.

Keywords: Torreya; habitat suitability zoning; machine learning model (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: 2024
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