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首页> 外文期刊>International journal of soil science >Determination the Factors Explaining Variability of Physical Soil Organic Carbon Fractions using Artificial Neural Network
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Determination the Factors Explaining Variability of Physical Soil Organic Carbon Fractions using Artificial Neural Network

机译：利用人工神经网络确定解释土壤物理有机碳组分变异性的因素

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Limited information is available about the use of intelligent system such as Artificial Neural Networks (ANN) to determine the most affecting factors on variability of soil organic carbon fractions (SOC) in the landscape scale. Therefore, this study was conducted to estimate SOC fractions by topographic attributes, selected soil properties and Normalized Vegetation Index (NDVI) data using ANN models. A total of 108 samples from surface soils (0-10 cm depth) were collected and various physical soil organic fractions were determined. The developed ANN models could explain 78-91% of the total variability in SOC fractions in the site studied. Sensitivity analysis using ANN models developed showed that NDVI as indication of vegetation cover was the most important factor for explaining variability of SOC fractions at the site. Furthermore, soil properties such as clay, silt and calcium carbonate and some topographic attributes which indirectly affect the total SOC content, also significantly influence the variability of SOC fractions. In overall, the results showed that the ANN models provide reliable prediction of SOC fractions by considering the NDVI, soil properties and terrain attributes.
机译：关于使用诸如人工神经网络（ANN）之类的智能系统来确定景观尺度上影响土壤有机碳组分（SOC）变化的最大影响因素的信息有限。因此，本研究旨在使用ANN模型通过地形属性，选定的土壤特性和归一化植被指数（NDVI）数据估算SOC含量。总共从地表土壤（0-10厘米深）收集了108个样品，并确定了各种物理土壤有机组分。所开发的ANN模型可以解释所研究站点中SOC分数的总变异性的78-91％。使用开发的ANN模型进行的敏感性分析表明，NDVI作为植被覆盖的指示是解释该地点SOC分数变化的最重要因素。此外，土壤特性（例如粘土，粉砂和碳酸钙）以及一些间接影响总SOC含量的地形特征，也显着影响SOC组分的变异性。总体而言，结果表明，通过考虑NDVI，土壤特性和地形属性，ANN模型可提供SOC分数的可靠预测。

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