Poplar’s Waterlogging Resistance Modeling and Evaluating: Exploring and Perfecting the Feasibility of Machine Learning Methods in Plant Science

Frontiers in Plant Science

Kebing Du¹  Xuelin Xie²  Jingfang Shen²  Xinye Zhang³ 

[1] College of Horticulture and Forestry Sciences, Hubei Engineering Technology Research Center for Forestry Information, Huazhong Agricultural University, Wuhan, China;College of Sciences, Huazhong Agricultural University, Wuhan, China;Hubei Academy of Forestry, Wuhan, China;

关键词: flood disaster;    prediction of waterlogging tolerance;    machine learning;    feature selection;    model establishment and evaluation;   

DOI  :  10.3389/fpls.2022.821365

来源: DOAJ

【 摘 要 】

Floods, as one of the most common disasters in the natural environment, have caused huge losses to human life and property. Predicting the flood resistance of poplar can effectively help researchers select seedlings scientifically and resist floods precisely. Using machine learning algorithms, models of poplar’s waterlogging tolerance were established and evaluated. First of all, the evaluation indexes of poplar’s waterlogging tolerance were analyzed and determined. Then, significance testing, correlation analysis, and three feature selection algorithms (Hierarchical clustering, Lasso, and Stepwise regression) were used to screen photosynthesis, chlorophyll fluorescence, and environmental parameters. Based on this, four machine learning methods, BP neural network regression (BPR), extreme learning machine regression (ELMR), support vector regression (SVR), and random forest regression (RFR) were used to predict the flood resistance of poplar. The results show that random forest regression (RFR) and support vector regression (SVR) have high precision. On the test set, the coefficient of determination (R2) is 0.8351 and 0.6864, the root mean square error (RMSE) is 0.2016 and 0.2780, and the mean absolute error (MAE) is 0.1782 and 0.2031, respectively. Therefore, random forest regression (RFR) and support vector regression (SVR) can be given priority to predict poplar flood resistance.

【 授权许可】

Unknown