Identification of Soil Properties Influencing Some Soil Physical Quality Indicators Using Hybrid PSO-ICA-SVR Algorithm in Some Agricultural Land Uses of Kerman Province, Iran
Identification of Soil Properties Influencing Some Soil Physical Quality Indicators Using Hybrid PSO-ICA-SVR Algorithm in Some Agricultural Land Uses of Kerman Province, Iran
复制标题
使用混合 PSO-ICA-SVR 算法识别伊朗克尔曼省部分农业用地中影响部分土壤物理质量指标的土壤特性
DOI：
10.1080/00103624.2019.1648658
复制
发表时间：
2019
影响因子：
1.8
通讯作者：
A. Mohammadi Torkashvand
中科院分区：
文献类型：
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作者：
F. Hojjatnooghi;H. Shirani;E. Pazira;A. Besalatpour;A. Mohammadi Torkashvand
摘要本研究旨在确定伊朗东南部土壤性质对土壤物理质量指标（SPQIs）影响的重要性。为此，在克尔曼省不同地点选择了169个具有不同农业用地的点，并在0-20 cm的深度采集了扰动和未扰动的土壤样本。测定了土壤pH值、土壤质地、田间容量（FC）、永久凋萎点（PWP）、土壤容重（BD）、电导率（ECe）、碳酸钙当量（CCE）、土壤有机质（SOM）等土壤性质，以及土壤物理质量指标（spqi），如田间容量（FC）、平均重量直径（MWD）、空气容量（AC）、相对田间容量（RFC）等。随后，采用粒子群优化-帝国主义竞争算法-支持向量回归（PSO-ICA-SVR）混合算法确定了影响SPQIs的土壤性质，并通过敏感性分析，识别了所选土壤性质对SPQIs影响的重要性。结果表明，OM、BD、粘土含量和CaCO3总体上影响土壤物理质量，并通过混合算法进行了仔细的选择。此外，利用SVR方法选择的特征进行建模并进行敏感性分析后，所选属性中粘土含量和BD对SPQIs的影响最为显著。与MWD指标相关的决定系数最高（R2 = 83.33），与AC和RFC指标相关的误差最低（RMSE = 0.031）。
ABSTRACT This research was conducted in order to determine the importance of the effect of some soil properties on some soil physical quality indicators (SPQIs) in southeast Iran. To this end, 169 points from different locations in Kerman province were selected which had different agricultural land uses, and disturbed and undisturbed soil samples were taken from a depth of 0–20 cm. Soil properties such as soil pH, soil texture, field capacity (FC), permanent wilting point (PWP), soil bulk density (BD), electrical conductivity (ECe), calcium carbonate equivalent (CCE), and soil organic matter (SOM), and soil physical quality indicators (SPQIs) such as field capacity (FC), mean weight diameter (MWD), air capacity (AC), and relative field capacity (RFC) were determined. Subsequently, soil properties affecting SPQIs were ascertained using Particle Swarm Optimization-Imperialist Competitive Algorithm-Support Vector Regression (PSO-ICA-SVR) hybrid algorithm, and after the sensitivity analysis, the importance of each selected property in terms of its impact on SPQIs was recognized. The results indicated OM, BD, clay content, and CaCO3 in general, affect soil physical quality and this selection was carefully made by the hybrid algorithm. Furthermore, after modeling using the features selected by the SVR method and performing the sensitivity analysis, clay content and BD, among the selected properties, had the most significant effects on SPQIs. The highest value of the coefficient of determination was associated with MWD index (R2 = 83.33) and the lowest error was related to AC and RFC indicators (RMSE = 0.031).