title: [Prediction of methane emission of paddy field based on the support vector regression model].
abstract: The methane emission data of paddy fields was obtained by using the static chamber and gas chromatography, and six parameters including atmospheric temperature, soil temperature at 5 cm depth, pH of soil, Eh of soil, soil moisture and ground biomass were selected as the primary influencing factors of methane emission. The support vector regression (epsilon-SVR) model was built on the optimization of structural risk minimization, and the parameters of the epsilon-SVR model were optimized using Leave-one-out Cross Validation (LOOCV). The prediction accuracy of model was evaluated by k-fold cross validation with the mean relative error (MRE) and the root mean square error (RMSE). In addition, the accuracy of the epsilon-SVR model was analyzed by comparison with the Back Propagation-Artificial Neural Network (BP-ANN) model. The results indicated that the predicted value of the epsilon-SVR model with the parameters C and epsilon optimized by LOOCV was in good agreement with the measured value, and the average MRE of test samples was 44% and the average RMSE was 16.21 mg x (m2 x h)(-1) in the process of 11-fold cross validation. Compared with the BP-ANN model, the correlation coefficient was 0.863, and all the indicators were better. It demonstrated that the 8-SVR model could be applied to the prediction of methane emission of paddy fields.
authors.affiliation: College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China. chenqiang@mail.bnu.edu.cn
authors.forename: Qiang
authors.initials: Q
authors.lastname: Chen
authors.forename: Wei-Guo
authors.initials: WG
authors.lastname: Jiang
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.forename: Li-Hua
authors.initials: LH
authors.lastname: Yuan
authors.forename: Wen-Jie
authors.initials: WJ
authors.lastname: Wang
authors.forename: Ying-Zi
authors.initials: YZ
authors.lastname: Pan
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Xiao-Fu
authors.initials: XF
authors.lastname: Liu
authors.forename: Hai-Jiang
authors.initials: HJ
authors.lastname: Liu
mesh_terms: D008697:Methane / Q000737:chemistry*; D016571:Neural Networks, Computer; D012275:Oryza; D012044:Regression Analysis; D012987:Soil / Q000737:chemistry*; D060388:Support Vector Machine; D013696:Temperature
chemical_list: D012987:Soil; D008697:Methane