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首页> 外文期刊>Catena: An Interdisciplinary Journal of Soil Science Hydrology-Geomorphology Focusing on Geoecology and Landscape Evolution >Estimating Proctor parameters in agricultural soils in the Ardabil plain of Iran using support vector machines, artificial neural networks and regression methods
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Estimating Proctor parameters in agricultural soils in the Ardabil plain of Iran using support vector machines, artificial neural networks and regression methods

机译：使用支持向量机，人工神经网络和回归方法估算农业土壤中农业土壤的标题参数

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摘要

Maximum bulk density (BDmax) and critical water content (theta(c)) (i.e., Proctor parameters) are valuable parameters to evaluate soil compactness and optimum moisture of workability for tillage. There are two novelties in the present study: First, no study has been conducted so far to estimate the Proctor parameters from CaCO3, saturated and field water contents in agricultural lands using state-of-the-art methods. Second, no study has been done to compare the estimation accuracy of linear (LR) and nonlinear (NLR) regression, support vector machine (SVM), and artificial neural networks (ANNs) methods in estimating Proctor parameters in agricultural soils. In total, 105 soil samples were taken from agricultural lands of Ardabil plain, northwest of Iran. Pedotransfer functions (PTFs) were constructed using SVM, ANNs, LR and NLR methods to estimate BDmax and theta(c) from readily available soil properties including organic carbon (OC), CaCO3, particle size distribution (PSD), bulk (BD), and particle (D-p) density, total porosity (n), penetration resistance (PR), and saturated (theta(s)) and field (theta(f)) water contents. The results of the LR, NLR, ANNs, and SVM estimations showed that theta(s), theta(f), OC, and D-p were the most suitable estimators in estimating BDmax and theta(c). The values of root mean square error (RMSE) criterion in the best LR, NLR, ANNs, and SVM PTFs were obtained 2.3, 3.29, 2.19 and 3.09 g g(-1) for theta(c) and 0.05, 0.07, 0.05 and 0.07 g cm(-3) for BDmax in the testing data set, respectively. Overall, Proctor parameters of agricultural soils could be accurately estimated by the ANNs compared with the LR, NLR and SVM.
机译：最大堆积密度（BDMAX）和临界含水量（Theta（C））（即，Proctor参数）是评估土壤紧凑性和耕作可行性的最佳水分的有价值的参数。本研究中有两项新奇是：首先，迄今未进行研究，以利用最先进的方法估计农业土地中的CaCo3，饱和和现场水含量的标准器参数。其次，已经没有进行研究以比较线性（LR）和非线性（NLR）回归，支持向量机（SVM）和人工神经网络（ANNS）方法的估计准确度在估计农业土壤中的Proctor参数中的方法。总共有105种土壤样品从伊朗西北部的阿达比尔平原农业用地取出。使用SVM，ANNS，LR和NLR方法构建PEDOT转器功能（PTFS），以估算BDMAX和THETA（C），从包括有机碳（OC），CACO3，粒度分布（PSD），散装（BD），散装（BD），和颗粒（DP）密度，总孔隙率（N），渗透性（PR）和饱和（θ）和邻静脉（THETA（F））水含量。 LR，NLR，ANN和SVM估计的结果表明，θ，θ（f），oc和d-p是估计BDMAX和THETA（C）中最合适的估计。获得最佳LR，NLR，ANNS和SVM PTF中的根均方误差（RMSE）标准的值2.3,3.29,21.19和3.09 gg（-1），为0.05,0.07,0.05和0.07 G cm（-3）分别在测试数据集中的BDMAX。总体而言，与LR，NLR和SVM相比，ANN可以精确地估计农业土壤的标准器参数。

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