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首页> 外文期刊>International Journal of Environmental Science and Technology >Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction
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Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction

机译：支持向量机与人工神经网络模型在土壤阳离子交换量预测中的比较分析

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摘要

The aim of this study was to compare the performance of support vector machine and artificial neural network techniques to predict the soil cation exchange capacity of an agricultural research station in terms of soil characteristics (clay, silt, sand, gypsum, organic matter). The data consist of 380 soil samples collected from different horizons of 80 soil profiles located in the Khoja (Khajeh) region of Azerbaijani provinces, Iran. The support vector machine and artificial neural network models predict the cation exchange capacity from the above soil characteristics of the samples. The models' results are compared using three criteria, i.e., root-mean-square errors, Nash-Sutcliffe and the correlation coefficient. A comparison of support vector machine results with artificial neural network method indicates that artificial neural network is better than the support vector machine method in prediction of the cation exchange capacity.
机译：这项研究的目的是比较支持向量机和人工神经网络技术的性能，以根据土壤特性（粘土，淤泥，沙子，石膏，有机质）预测农业研究站的土壤阳离子交换能力。数据包括从位于伊朗阿塞拜疆省的霍贾（Khajeh）地区的80个土壤剖面的不同层位采集的380个土壤样品。支持向量机和人工神经网络模型根据上述样品的土壤特征预测阳离子交换能力。使用三个标准（即均方根误差，Nash-Sutcliffe和相关系数）比较模型的结果。支持向量机结果与人工神经网络方法的比较表明，在预测阳离子交换容量方面，人工神经网络优于支持向量机方法。

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