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Waterlogging Resistance Evaluation Index and Photosynthesis Characteristics Selection: Using Machine Learning Methods to Judge Poplar’s Waterlogging Resistance

Author

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  • Xuelin Xie

    (College of Sciences, Huazhong Agricultural University, Wuhan 430070, China)

  • Jingfang Shen

    (College of Sciences, Huazhong Agricultural University, Wuhan 430070, China)

Abstract

Flood disasters are the major natural disaster that affects the growth of agriculture and forestry crops. Due to rapid growth and strong waterlogging resistance characteristics, many studies have explained the waterlogging resistance mechanism of poplar from different perspectives. However, there is no accurate method to define the evaluation index of waterlogging resistance. In addition, there is also a lack of research on predicting the waterlogging resistance of poplars. Based on the changes of poplar biomass and seedling height, the evaluation index of poplar resistance to waterlogging was well determined, and the characteristics of photosynthesis were used to predict the waterlogging resistance of poplars. First, four methods of hierarchical clustering, lasso, stepwise regression and all-subsets regression were used to extract the photosynthesis characteristics. After that, the support vector regression model of poplar resistance to waterlogging was established by using the characteristic parameters of photosynthesis. Finally, the results show that the SVR model based on Stepwise regression and Lasso method has high precision. On the test set, the coefficient of determination ( R 2 ) was 0.8581 and 0.8492, the mean square error (MSE) was 0.0104 and 0.0341, and the mean relative error (MRE) was 9.78% and 9.85%, respectively. Therefore, using the characteristic parameters of photosynthesis to predict the waterlogging resistance of poplars is feasible.

Suggested Citation

  • Xuelin Xie & Jingfang Shen, 2021. "Waterlogging Resistance Evaluation Index and Photosynthesis Characteristics Selection: Using Machine Learning Methods to Judge Poplar’s Waterlogging Resistance," Mathematics, MDPI, vol. 9(13), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:13:p:1542-:d:586749
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    References listed on IDEAS

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    1. Mishra, Sasmita & Padhy, Sudarsan, 2019. "An efficient portfolio construction model using stock price predicted by support vector regression," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    2. Kenong Xu & Xia Xu & Takeshi Fukao & Patrick Canlas & Reycel Maghirang-Rodriguez & Sigrid Heuer & Abdelbagi M. Ismail & Julia Bailey-Serres & Pamela C. Ronald & David J. Mackill, 2006. "Sub1A is an ethylene-response-factor-like gene that confers submergence tolerance to rice," Nature, Nature, vol. 442(7103), pages 705-708, August.
    3. Tian, Lixin & Li, Jing & Bi, Wenshuang & Zuo, Shiyu & Li, Lijie & Li, Wenlong & Sun, Lei, 2019. "Effects of waterlogging stress at different growth stages on the photosynthetic characteristics and grain yield of spring maize (Zea mays L.) Under field conditions," Agricultural Water Management, Elsevier, vol. 218(C), pages 250-258.
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