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Estimation of Low Organic Matter Content in Desert Soil of Arid Area Based on Fractional Order Sprott Chaotic Circuit and Gray Theory

机译:基于分数阶令人意逝的混沌电路及灰色理论估算干旱地区沙漠土壤低有机质含量

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摘要

Weak spectral response and low model accuracy problems occurred in the process of quantitative inversion of low organic matter content of desert soil in arid areas. This study collects soil samples and field spectral data from different human interference regions in Fukang City, Xinjiang, to search the soil hyperspectral response law that based on the fractional Sprott chaotic system, and combined with the gray system theory to estimate organic matter content rapidly and accurately. Simulation shows that for those sampling soil with lower organic matter content, the range of X and Y components of the dynamic error motion curve distribution of the Sprott chaotic system is larger, and the motion curve of the non-integer order 1.9-order dynamic error is the most obvious. Since the chaotic attractors will appear as a linear trend according to different contents of the organic matter, so this thesis establishes a gray prediction model based on the 1.9 fractional order chaotic attractors. The R-2, RPD, and RMSE of the low organic matter content in the region without human interruption are 0.995, 14.86, and 0.17, respectively. The R-2, RPD, and RMSE of low organic matter content in the region with human interruption are 0.992, 11.95, and 0.11, respectively. This study demonstrates that it is feasible to estimate the low organic matter content of desert soils in arid regions via the gray prediction model that based on fractional chaotic attractors. This study provides a novel method for soil spectra signal analysis and estimation of the organic matter content.
机译:干旱地区沙漠土壤低有机质含量的定量转化过程中发生弱光谱响应和低模型精度问题。本研究从新疆福康市的不同人类干涉区收集土壤样本和田间光谱数据,以搜索基于分数令人兴奋的混沌系统的土壤高光谱响应法,并结合灰色系统理论迅速估算有机质含量准确。仿真表明,对于具有较低有机质含量的采样土,Sprott混沌系统的动态误差运动曲线分布的X和Y分量的范围越大,非整数阶数的运动曲线为1.9阶动态误差是最明显的。由于混沌吸引子将根据有机物质的不同内容显示为线性趋势,因此本文基于1.9分数混沌吸引子建立灰度预测模型。没有人类中断的区域中低有机质含量的R-2,RPD和RMSE分别为0.995,14.86和0.17。具有人类中断的区域中的低有机质含量的R-2,RPD和RMSE分别为0.992,11.95和0.11。该研究表明,通过基于分数混沌吸引子的灰色预测模型估计干旱地区的沙漠土壤的低有机质含量是可行的。该研究提供了一种用于土壤光谱信号分析和有机质含量估计的新方法。

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