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首页> 外文期刊>Journal of plant nutrition and soil science >Using support vector machines to predict cation exchange capacity of different soil horizons in Qingdao City, China
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Using support vector machines to predict cation exchange capacity of different soil horizons in Qingdao City, China

机译：使用支持向量机预测青岛市不同土壤层位的阳离子交换能力

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摘要

Agricultural, environmental and ecological modeling requires soil cation exchange capacity (CEC) that is difficult to measure. Pedotransfer functions (PTFs) are thus routinely applied to predict CEC from easily measured physicochemical properties (e.g., texture, soil organic matter, pH). This study developed the support vector machines (SVM)-based PTFs to predict soil CEC based on 208 soil samples collected from A and B horizons in Qingdao City, Shandong Province, China. The database was randomly split into calibration and validation datasets in proportions of 3:1 using the bootstrap method. The optimal SVM parameters were searched by applying the genetic algorithm (GA). The performance of SVM models was compared to those of multiple stepwise regression (MSR) and artificial neural network (ANN) models. Results show that the accuracy of CEC predicted by SVM improves considerably over those predicted by MSR and ANN. The performance of SVM for B horizon (R~2 = 0.85) is slightly better than that for A horizon (R~2 = 0.81). The SVM is a powerful approach in the simulation of nonlinear relationship between CEC and physicochemical properties of widely distributed samples from different soil horizons. Sensitivity analysis was also conducted to explore the influence of each input parameter on the CEC predictions by SVM. The clay content is the most sensitive parameter, followed by soil organic matter and pH, while sand content has the weakest influence. This suggests that clay is the most important predictor for predicting CEC of both soil horizons.
机译：农业，环境和生态模型需要难以测量的土壤阳离子交换能力（CEC）。因此，通常使用Pedotransfer函数（PTF）从容易测量的理化特性（例如质地，土壤有机质，pH）中预测CEC。这项研究开发了基于支持向量机（PTM）的PTF，可基于从中国山东省青岛市A和B层收集的208个土壤样本预测土壤CEC。使用引导方法，将数据库按3：1的比例随机分为校准和验证数据集。通过应用遗传算法（GA）搜索最佳SVM参数。将SVM模型的性能与多元逐步回归（MSR）和人工神经网络（ANN）模型的性能进行了比较。结果表明，SVM预测的CEC的准确性比MSR和ANN预测的有显着提高。 SVM在B层（R〜2 = 0.85）的性能略好于A层（R〜2 = 0.81）。 SVM是模拟CEC与来自不同土壤层的广泛分布的样品的理化特性之间的非线性关系的有力方法。还进行了敏感性分析，以探索每个输入参数对SVM对CEC预测的影响。黏土含量是最敏感的参数，其次是土壤有机质和pH，而沙含量的影响最弱。这表明粘土是预测两种土壤层的CEC的最重要的预测因子。

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