| 基于机器学习的农田土壤阳离子交换量空间分布预测 |
| 摘要点击 1082 全文点击 118 投稿时间：2024-03-20 修订日期：2024-05-10 |
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| 中文关键词 农田 阳离子交换量（CEC） 多元线性回归 机器学习 算法优化 |
| 英文关键词 farmland cation exchange capacity（CEC） multiple linear regression machine learning algorithm optimization |
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| 中文摘要 |
| 阳离子交换量（CEC）反映了土壤对交换性阳离子的固持能力,是评价农田土壤肥力和环境质量的重要指标. 室内滴定法测定土壤CEC成本高且流程繁琐. 为此,利用宁夏各地农田内采集的565个0~20 cm耕层土壤样本,测定土壤pH、有机碳和机械组成等参数,采用多元线性回归法和机器学习法构建基于田块尺度的土壤CEC估测模型,用于快速准确地获取土壤CEC值. 结果表明：①宁夏农田土壤CEC平均值为9.39 cmol·kg-1,变异系数为40.74%,属于高变异,空间分布特征总体表现为黄河流域（宁夏段）周边及宁南山区较高,中部干旱带和中东部地区较低；②选用总数据集所构建的模型土壤参数中,土壤有机碳、黏粒含量、pH和砂粒含量是影响宁夏农田土壤CEC的重要因素,其相关系数分别为0.55、0.72、-0.41和-0.44；③多元线性回归建模结果显示,按市区对总数据集划分,构建市区范围内的多元线性型回归模型更有利于农田土壤CEC预测；④相较于多元线性回归法,机器学习法在总数据集预测上效果更好,以多元线性回归模型为参照,反向传播神经网络、卷积神经网络、粒子群算法优化的反向传播神经网络、粒子群算法优化的卷积神经网络、灰狼算法优化的反向传播神经网络和灰狼算法优化的卷积神经网络模型预测精度（R2）分别提高了13.59%、30.78%、18.91%、35.47%、20.94%和38.91%. ⑤模型验证结果表明,灰狼算法优化的卷积神经网络模型,验证集R2为0.91,RMSE为1.07 cmol·kg-1,NRMSE为11.77%,模型接近极稳定水平,模型综合表现最佳. 综上所述,灰狼算法优化的卷积神经网络模型预测精度高,外推能力强,是农田尺度预测土壤CEC的较优模型. 此结果可为宁夏农田土壤CEC预测提供了新的思路和解决方案. |
| 英文摘要 |
| Cation exchange capacity （CEC） reflects the ability of soil to sequester exchangeable cations and is an important indicator of the fertility and environmental quality of agricultural soils. The indoor titration method for determining soil cation exchange is expensive and cumbersome. To this end, 565 soil samples from the 0-20 cm plough layer were collected from farmland in Ningxia, and the parameters of soil pH, organic carbon, and mechanical composition were determined. A field-scale soil cation exchange （CEC） estimation model was constructed using multiple linear regression and machine learning methods to obtain soil CEC values rapidly and accurately. The results showed that： ① The mean CEC value of farmland soils in Ningxia was 9.39 cmol·kg-1, with a coefficient of variation of 40.74%. This indicated a high degree of variability, with the spatial distribution of the CEC values generally showing higher values in the periphery of the Yellow River Basin （Ningxia section） and the southern mountainous areas of Ningxia and lower values in the central arid zone and the east-central region. ② The soil parameters selected for modeling the total dataset were as follows： Soil organic carbon, clay content, pH, and sand content were the important factors influencing the CEC of farmland soil in Ningxia, with correlation coefficients of 0.55, 0.72, -0.41, and -0.44, respectively. ③ The results of multiple linear regression modeling showed that dividing the total dataset according to the urban area and constructing a multiple linear-type regression model within the urban area was more conducive to the prediction of the CEC of farmland soils. ④ Compared with the multiple linear regression method, the machine learning method was more effective in the prediction of the total dataset. Further, using the multiple linear regression model as a reference, the prediction accuracy （R2） of the back propagation neural network, convolutional neural network, back propagation neural network optimized by the particle swarm algorithm, convolutional neural network optimized by the particle swarm algorithm, back propagation neural network optimized by the grey wolf algorithm, and convolutional neural network model optimized by the grey wolf algorithm were improved by 13.59%, 30.78%, 18.91%, 35.47%, 20.94%, and 38.91%, respectively. ⑤ The validation results showed that the validation set of the convolutional neural network model optimized by the grey wolf algorithm had an R2 of 0.91, an RMSE of 1.07 cmol·kg-1, and an NRMSE of 11.77%, and the model was close to the very stable level with the best overall performance. In conclusion, the convolutional neural network model optimized by the grey wolf algorithm has high prediction accuracy and strong extrapolation ability, which is a better model for predicting soil CEC at the farmland scale. This result provides a novel idea and solution for the prediction of soil CEC in farmland in Ningxia and the whole country. |
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