文献类型：期刊文献

英文题名：Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques

作者：Wu, Chunyan[1,2] Chen, Yongfu[2] Hong, Xiaojiang[3] Liu, Zelin[4] Peng, Changhui[4]

第一作者：吴春燕

通信作者：Chen, YF[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, State Key Lab Tree Genet & Breeding, Key Lab Tree Breeding & Cultivat State Forestry A, Beijing 100091, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Hainan Bawangling Natl Nat Reserve, Changjiang 572722, Hainan, Peoples R China;[4]Univ Quebec Montreal, Inst Environm Sci, Dept Biol Sci, Montreal, PQ, Canada

年份：2020

卷号：7

期号：1

外文期刊名：FOREST ECOSYSTEMS

收录：;Scopus(收录号：2-s2.0-85084200952);WOS:【SCI-EXPANDED(收录号:WOS:000532392700002)】；

基金：The authors of this study would like to thank the Hainan Bawangling Natural Nature Reserve of Hainan Province who provided the test sites and experimental materials, and the work was financially supported by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (CAFBB2017ZB004). CW would also like to thank the China Scholarship Council (CSC) for offering a scholarship at the University of Quebec at Montreal (UQAM). CP acknowledges the funding provided by the National Science and Engineering Research Council of Canada (NSERC) Discover Grant.

语种：英文

外文关键词：Support vector machine; KNNSVM; Generalized regression neural network; Nutrient grade; Rare and endangered tree species

摘要：Background The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the natural regeneration of Dacrydium pectinatum communities in China, designing advanced and accurate estimation methods is necessary. Methods This study uses machine learning techniques created a series of comprehensive and novel models from which to evaluate soil nutrient content. Soil nutrient evaluation methods were built by using six support vector machines and four artificial neural networks. Results The generalized regression neural network model was the best artificial neural network evaluation model with the smallest root mean square error (5.1), mean error (- 0.85), and mean square prediction error (29). The accuracy rate of the combined k-nearest neighbors (k-NN) local support vector machines model (i.e. k-nearest neighbors -support vector machine (KNNSVM)) for soil nutrient evaluation was high, comparing to the other five partial support vector machines models investigated. The area under curve value of generalized regression neural network (0.6572) was the highest, and the cross-validation result showed that the generalized regression neural network reached 92.5%. Conclusions Both the KNNSVM and generalized regression neural network models can be effectively used to evaluate soil nutrient content and quality grades in conjunction with appropriate model variables. Developing a new feasible evaluation method to assess soil nutrient quality for Dacrydium pectinatum, results from this study can be used as a reference for the adaptive management of rare and endangered tree species. This study, however, found some uncertainties in data acquisition and model simulations, which will be investigated in upcoming studies.