WuChenHongEtAl2020

Référence

Wu, C., Chen, Y., Hong, X., Liu, Z., Peng, C. (2020) Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques. Forest Ecosystems, 7(1). (Scopus )

Résumé

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. © 2020, The Author(s).

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@ARTICLE { WuChenHongEtAl2020,
    AUTHOR = { Wu, C. and Chen, Y. and Hong, X. and Liu, Z. and Peng, C. },
    JOURNAL = { Forest Ecosystems },
    TITLE = { Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques },
    YEAR = { 2020 },
    NOTE = { cited By 0 },
    NUMBER = { 1 },
    VOLUME = { 7 },
    ABSTRACT = { 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. © 2020, The Author(s). },
    AFFILIATION = { State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China; Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China; Hainan Bawangling National Natural Reserve, Changjiang, Hainan 572722, China; Department of Biological Science, Institute of Environment Sciences, University of Quebec at Montreal, Montreal, QC, Canada },
    ART_NUMBER = { 30 },
    AUTHOR_KEYWORDS = { Generalized regression neural network; KNNSVM; Nutrient grade; Rare and endangered tree species; Support vector machine },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1186/s40663-020-00232-5 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084200952&doi=10.1186%2fs40663-020-00232-5&partnerID=40&md5=23206ae0e64b3a27fc0cb38d05c1eb9c },
}

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