Modelling of some soil physical quality indicators using hybrid algorithm principal component analysis - artificial neural network

Desert Pub Date : 2019-06-01 DOI:10.22059/JDESERT.2019.72447
F. AmiriMijan, H. Shirani, I. Esfandiarpour, A. Besalatpour, H. Shekofteh
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引用次数: 1

Abstract

One of the important issues in the analysis of soils is to evaluate their features. In estimation of the hardly available properties, it seems the using of Data mining is appropriate. Therefore, the modelling of some soil quality indicators, using some of the early features of soil which have been proved by some researchers, have been considered. For this purpose, 140 disturbed and 140 undisturbed soil samples were collected from Jiroft, southern Kerman, Iran. Some physical and chemical properties of soil, for example, sand, silt and clay percentage, organic matter (OM), calcium carbonate (CaCO3), electrical conductivity at saturation (ECe), porosity (F), and bulk density (BD) were measured using standard methods. Some soil physical property indicators, including plant available water (PAW), relative field capacity (RFC), air capacity (AC) and saturated hydraulic conductivity (Ks) were also calculated. Using the hybrid algorithm of principle component analysis-artificial neural network (PCA-ANN), the calculated indicators were predicted by the easily available properties. The results showed that PCA-ANN had an acceptable accuracy in the modelling of soilphysical quality. The coefficient of determination (R2) of training and testing data for PAW, RFC and AC were 0.82 and 0.81, 0.90 and 0.79, 0.99 and 0.99, respectively. The optimization of Ks did not have the desired results. In other words, the R2 values of the training and testing data for this indicator were equal to 0.25 and 0.13, respectively.
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主成分分析-人工神经网络混合算法在土壤物理质量指标建模中的应用
土壤分析中的一个重要问题是评估其特征。在估计几乎不可用的属性时,使用数据挖掘似乎是合适的。因此,已经考虑了利用一些研究人员已经证明的土壤早期特征对一些土壤质量指标进行建模。为此,从伊朗克尔曼南部的Jiroft采集了140个扰动土壤样本和140个未扰动土壤样本。使用标准方法测量了土壤的一些物理和化学性质,例如沙子、淤泥和粘土百分比、有机质(OM)、碳酸钙(CaCO3)、饱和电导率(ECe)、孔隙率(F)和堆积密度(BD)。还计算了一些土壤物理性质指标,包括植物有效水(PAW)、相对田间容量(RFC)、空气容量(AC)和饱和导水率(Ks)。利用主成分分析-人工神经网络(PCA-NN)的混合算法,利用易得性对计算指标进行预测。结果表明,PCA-NN在土壤物理质量建模中具有可接受的精度。PAW、RFC和AC的训练和测试数据的决定系数(R2)分别为0.82和0.81、0.90和0.79、0.99和0.99。Ks的优化没有得到所需的结果。换句话说,该指标的训练和测试数据的R2值分别等于0.25和0.13。
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