中文摘要：

利用静态箱和气相色谱仪法获取水稻田甲烷排放通量数据,选取大气温度、土壤5 cm深温度、土壤pH、土壤Eh、土壤含水量和地表生物量作为影响因子.应用建立在结构风险最小化优化上的支持向量回归（ε-SVR）模型,采用留一法交叉检核网格搜索法（LOOCV）优化ε-SVR预测模型的参数,采用k折交叉检验的方法依据平均相对误差（MRE）和均方根误差（RMSE）对模型的精度进行验证,并与BP人工神经网络（BP-ANN）模型比较,评价ε-SVR预测模型的准确性.结果表明,通过LOOCV选择最优的惩罚因子C和损失系数ε,并由此构建的ε-SVR预测模型预测值和实测值具有很好的一致性,通过11折交叉验证后,测试样本的平均MRE为44%,平均RMSE为16.21 mg·（m2·h）-1.通过与BP-ANN模型比较,预测值和实际值相关系数达0.863,各项指标均优于BP-ANN预测模型,说明ε-SVR模型能够适用于水稻田甲烷排放通量的预测.

英文摘要：

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（ε-SVR） model was built on the optimization of structural risk minimization,and the parameters of the ε-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 ε-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 ε-SVR model with the parameters C and ε 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·（m2·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 ε-SVR model could be applied to the prediction of methane emission of paddy fields.