土壤湿度地面观测数据处理方法研究
发布时间：2018-09-07 06:50
【摘要】：土壤湿度是一类重要的地表环境变量,获取高质量的区域土壤湿度数据对于科学研究和生产实践具有重要意义。传统的土壤湿度数据是基于地面站点监测,其优势是精度高、时间连续性好,但是空间连续性较差。为了获取高精度、空间连续的土壤湿度数据,本文以黑河中游盈科/大满灌区域为实验区,利用地面观测得到的多个空间点的土壤湿度时间序列数据,对该区域的土壤湿度进行了空间分析,并对土壤湿度的空间插值方法和时间序列数据预测方法进行了研究。主要工作如下:1、对土壤湿度进行空间异质性分析土壤湿度的空间异质性分析主要包括统计分析、变异分析以及自相关分析三个方面。为了解本实验区域的空间异质特性,为接下来的实验作为对比与参考,本文分别通过数值统计分析了解数据布局以及变异程度;以模型拟合度和残差大小为检验指标,选取合适的变异模型,利用变异分析剖析区域化变量的空间结构特征;计算土壤湿度的全局自相关Moran’s I系数,计算结果通过Z值检验,检验具有空间位置的土壤湿度观测点是否显著地与其相邻观测点的观测值相关联。2、基于叠加理论,建立了考虑时空特征的土壤湿度插值方法依据信号叠加基本理论,信号可以分解为互不重合的部分,经过处理后仍可以重构得到原数据。以叠加理论作为理论基础,利用小波分解方法,对土壤湿度时序数据进行分解,得到低频部分和残差部分。分别利用高精度曲面建模(HASM)和变异函数对两部分进行插值。将插值结果进行拟合,得到了精度较高的插值结果。3、建立了一种基于改进BP神经网络的土壤湿度时序预测方法针对普通BP神经网络收敛速度慢,易陷入局部最优的问题,提出动量因子和自适应学习率的BP神经网络改进方法,并且利用粒子群算法优化BP神经网络的初始阈值和权值。针对标准粒子群算法(PSO)中惯性权重线性递减、学习因子取常数而导致的PSO收敛速度慢、易错过全局最优解等问题,将迭代次数和适应度值相结合改进惯性权重和学习因子,有效提高算法找到全局最优解的速度。
[Abstract]:Soil moisture is a kind of important surface environmental variable. It is very important to obtain high quality regional soil moisture data for scientific research and production practice. The traditional soil moisture data is based on ground site monitoring, which has the advantages of high precision, good time continuity, but poor spatial continuity. In order to obtain soil moisture data with high accuracy and spatial continuity, the soil moisture time series data of several spatial points obtained from ground observation are used in this paper. The experimental area is Yingke / Daoman irrigation area in the middle reaches of Heihe River. The spatial analysis of soil moisture in this area was carried out, and the spatial interpolation method and time series data prediction method of soil moisture were studied. The main work is as follows: 1. The spatial heterogeneity analysis of soil moisture includes three aspects: statistical analysis, variation analysis and autocorrelation analysis. In order to understand the spatial heterogeneity of the experimental area, for the following experiments as a comparison and reference, this paper respectively through the numerical statistical analysis to understand the data layout and variability; model fitting and residual size as the test index, Selecting the appropriate variation model, using variation analysis to analyze the spatial structure characteristics of regionalized variables, calculating the global autocorrelation Moran's I coefficient of soil moisture, and calculating the results through Z test. To test whether the soil moisture observation point with spatial position is significantly related to the observed value of its adjacent observation points. Based on the superposition theory, a soil moisture interpolation method considering spatial and temporal characteristics is established according to the basic theory of signal superposition. The signal can be decomposed into non-coincidence parts, and the original data can be reconstructed after processing. Based on the superposition theory and wavelet decomposition method, the soil moisture time series data are decomposed to obtain the low frequency part and the residual part. (HASM) and variable function are used to interpolate the two parts respectively. By fitting the interpolation results, the interpolation results with high precision are obtained. A soil moisture time series prediction method based on improved BP neural network is established to solve the problem that the convergence rate of ordinary BP neural networks is slow and is prone to fall into local optimum. An improved method of BP neural network based on momentum factor and adaptive learning rate is proposed, and the initial threshold and weight of BP neural network are optimized by particle swarm optimization (PSO). Aiming at the problem of linear decline of inertia weight in standard particle swarm optimization (PSO), slow convergence speed of PSO caused by learning factor taking constant, and easy missing global optimal solution, the iterative times and fitness value are combined to improve inertia weight and learning factor. The speed of finding the global optimal solution is improved effectively.
【学位授予单位】：山东农业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：S152.71
本文编号：2227511
[Abstract]:Soil moisture is a kind of important surface environmental variable. It is very important to obtain high quality regional soil moisture data for scientific research and production practice. The traditional soil moisture data is based on ground site monitoring, which has the advantages of high precision, good time continuity, but poor spatial continuity. In order to obtain soil moisture data with high accuracy and spatial continuity, the soil moisture time series data of several spatial points obtained from ground observation are used in this paper. The experimental area is Yingke / Daoman irrigation area in the middle reaches of Heihe River. The spatial analysis of soil moisture in this area was carried out, and the spatial interpolation method and time series data prediction method of soil moisture were studied. The main work is as follows: 1. The spatial heterogeneity analysis of soil moisture includes three aspects: statistical analysis, variation analysis and autocorrelation analysis. In order to understand the spatial heterogeneity of the experimental area, for the following experiments as a comparison and reference, this paper respectively through the numerical statistical analysis to understand the data layout and variability; model fitting and residual size as the test index, Selecting the appropriate variation model, using variation analysis to analyze the spatial structure characteristics of regionalized variables, calculating the global autocorrelation Moran's I coefficient of soil moisture, and calculating the results through Z test. To test whether the soil moisture observation point with spatial position is significantly related to the observed value of its adjacent observation points. Based on the superposition theory, a soil moisture interpolation method considering spatial and temporal characteristics is established according to the basic theory of signal superposition. The signal can be decomposed into non-coincidence parts, and the original data can be reconstructed after processing. Based on the superposition theory and wavelet decomposition method, the soil moisture time series data are decomposed to obtain the low frequency part and the residual part. (HASM) and variable function are used to interpolate the two parts respectively. By fitting the interpolation results, the interpolation results with high precision are obtained. A soil moisture time series prediction method based on improved BP neural network is established to solve the problem that the convergence rate of ordinary BP neural networks is slow and is prone to fall into local optimum. An improved method of BP neural network based on momentum factor and adaptive learning rate is proposed, and the initial threshold and weight of BP neural network are optimized by particle swarm optimization (PSO). Aiming at the problem of linear decline of inertia weight in standard particle swarm optimization (PSO), slow convergence speed of PSO caused by learning factor taking constant, and easy missing global optimal solution, the iterative times and fitness value are combined to improve inertia weight and learning factor. The speed of finding the global optimal solution is improved effectively.
【学位授予单位】：山东农业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：S152.71
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