技术领域Technical Field
本发明涉及环境科学和土壤科学的交叉领域，尤其涉及一种流域土地利用与土壤侵蚀关系动态评估方法。The invention relates to the intersecting field of environmental science and soil science, and in particular to a dynamic evaluation method for the relationship between watershed land use and soil erosion.
背景技术Background Art
流域土地利用与土壤侵蚀关系的评估是土壤科学和环境管理领域的一个重要课题。土壤侵蚀不仅影响土地的肥沃度和农业生产力，还对水质、生态系统健康和地质稳定性产生深远影响。随着人类活动的增加和气候变化的加剧，有效监测和评估流域内土壤侵蚀与土地利用之间的关系变得尤为重要。The assessment of the relationship between land use and soil erosion in a watershed is an important topic in the field of soil science and environmental management. Soil erosion not only affects the fertility of the land and agricultural productivity, but also has a profound impact on water quality, ecosystem health and geological stability. With the increase of human activities and the intensification of climate change, it is particularly important to effectively monitor and assess the relationship between soil erosion and land use in a watershed.
传统的评估方法通常依赖于简化的模型和有限的数据类型，这些方法可能无法充分捕捉到土壤特性、土地利用模式、地形水文条件及气候因素之间的复杂相互作用。此外，这些方法往往缺乏动态性和适应性，难以准确预测未来的土壤侵蚀趋势或适应环境变化和新数据。Traditional assessment methods often rely on simplified models and limited data types, which may not fully capture the complex interactions between soil properties, land use patterns, topographic hydrological conditions and climate factors. In addition, these methods often lack dynamism and adaptability, making it difficult to accurately predict future soil erosion trends or adapt to environmental changes and new data.
我国专利申请号：CN202111389125.4，公开日：2023.05.23，公开了一种基于RUSLE的流域土地利用与土壤侵蚀关系定量评价方法，首先收集多源数据，包括研究区气象站点的逐日降雨数据、土壤物理性质数据、数字高程数据、植被指数空间分布数据、土地利用类型数据，并应用ArcGIS10.5对数据进行图像融合拼接、裁剪和投影等预处理。其次，计算土壤侵蚀评价因子，包括降雨侵蚀力因子、土壤可蚀性因子、地形起伏度因子、植被覆盖因子和水土保持措施因子。最后，基于修正的通用土壤流失方程计算流域土壤侵蚀模数并分级，以土地利用类型和土壤侵蚀模数为输入层进行叠加分析，得出不同土地利用类型的土壤侵蚀特征。该发明为合理规划流域土地利用、有效管理水土资源并为生态环境建设提供科学依据和基础支持。my country's patent application number: CN202111389125.4, publication date: 2023.05.23, discloses a quantitative evaluation method for the relationship between watershed land use and soil erosion based on RUSLE. First, multi-source data are collected, including daily rainfall data, soil physical property data, digital elevation data, vegetation index spatial distribution data, and land use type data of meteorological stations in the study area, and ArcGIS10.5 is used to perform image fusion, splicing, clipping, and projection on the data. Secondly, soil erosion evaluation factors are calculated, including rainfall erosion factor, soil erodibility factor, terrain relief factor, vegetation cover factor, and soil and water conservation measures factor. Finally, the soil erosion modulus of the watershed is calculated and classified based on the modified general soil loss equation, and the land use type and soil erosion modulus are used as input layers for superposition analysis to obtain soil erosion characteristics of different land use types. This invention provides a scientific basis and basic support for the rational planning of watershed land use, the effective management of water and soil resources, and the construction of an ecological environment.
但上述技术至少存在如下技术问题：现有技术存在数据处理的单一性和局限性、分析的静态性和缺乏适应性、过拟合风险以及决策支持和可视化工具的不足，依赖于简化的模型和有限的数据类型，未能充分融合多源数据，如地形水文数据和气候环境因素，限制了全面性和精确性，缺乏动态分析土壤属性随时间变化的能力，难以适应新数据或环境变化，导致预测和分析的准确性降低；在决策支持方面往往缺乏有效的可视化和地理信息集成工具，限制了其在土壤保护和土地管理决策中的实用性的技术问题。However, the above technologies have at least the following technical problems: the existing technologies have the problems of singleness and limitation of data processing, static analysis and lack of adaptability, overfitting risk, and insufficient decision support and visualization tools. They rely on simplified models and limited data types, and fail to fully integrate multi-source data, such as topographic and hydrological data and climate and environmental factors, which limits the comprehensiveness and accuracy. They lack the ability to dynamically analyze the changes in soil properties over time and have difficulty adapting to new data or environmental changes, resulting in reduced accuracy of predictions and analysis. In terms of decision support, they often lack effective visualization and geographic information integration tools, which limits their practicality in soil protection and land management decisions.
发明内容Summary of the invention
本发明提供一种流域土地利用与土壤侵蚀关系动态评估方法，解决了现有技术存在的数据处理的单一性和局限性、分析的静态性和缺乏适应性、过拟合风险以及决策支持和可视化工具的不足，依赖于简化的模型和有限的数据类型，未能充分融合多源数据，如地形水文数据和气候环境因素，限制了全面性和精确性，缺乏动态分析土壤属性随时间变化的能力，难以适应新数据或环境变化，导致预测和分析的准确性降低；在决策支持方面往往缺乏有效的可视化和地理信息集成工具，限制了其在土壤保护和土地管理决策中的实用性的技术问题。实现了一种综合多源数据的高维动态决策树算法，用于全面、精确地评估流域土地利用与土壤侵蚀之间的复杂关系。The present invention provides a method for dynamic assessment of the relationship between watershed land use and soil erosion, which solves the problems of the existing technology, such as the singleness and limitation of data processing, the static nature and lack of adaptability of analysis, the risk of overfitting, and the insufficiency of decision support and visualization tools. The method relies on simplified models and limited data types, fails to fully integrate multi-source data, such as topographic hydrological data and climate and environmental factors, limits comprehensiveness and accuracy, lacks the ability to dynamically analyze changes in soil properties over time, and is difficult to adapt to new data or environmental changes, resulting in reduced accuracy of prediction and analysis; and often lacks effective visualization and geographic information integration tools in decision support, which limits its practicality in soil protection and land management decision-making. A high-dimensional dynamic decision tree algorithm that integrates multi-source data is implemented to comprehensively and accurately assess the complex relationship between watershed land use and soil erosion.
本发明的一种流域土地利用与土壤侵蚀关系动态评估方法，具体包括以下技术方案：A dynamic evaluation method for the relationship between watershed land use and soil erosion of the present invention specifically includes the following technical solutions:
一种流域土地利用与土壤侵蚀关系动态评估方法，包括以下步骤：A method for dynamically evaluating the relationship between watershed land use and soil erosion comprises the following steps:
S1. 收集原始土壤数据，将原始土壤数据转换到高维特征空间，并对转换后的高维数据进行时间序列分析；时间序列分析的具体公式如下：S1. Collect the original soil data, transform the original soil data into a high-dimensional feature space, and perform time series analysis on the transformed high-dimensional data; the specific formula for time series analysis is as follows:
， ,
其中，表示时间序列分析的结果；代表原始土壤数据向量；是时间变量；表示第个高维数据；是第个高维数据的权重；是时间因子；是高维数据的数量；是时间多项式的阶数索引；是最大阶数；in, Represents the results of time series analysis; represents the original soil data vector; is a time variable; Indicates High-dimensional data; It is The weight of high-dimensional data; is the time factor; is the amount of high-dimensional data; is the order index of the time polynomial; is the maximum order;
基于时间序列分析的结果和土地利用数据构建非线性自适应网络模型，量化土地利用变化对土壤的物理和化学属性的影响；非线性自适应网络模型的具体实现如下：Based on the results of time series analysis and land use data, a nonlinear adaptive network model is constructed to quantify the impact of land use changes on the physical and chemical properties of soil. The specific implementation of the nonlinear adaptive network model is as follows:
， ,
其中，是非线性自适应网络模型的输出；是土地利用数据；是第个网络节点的权重；是网络参数；是网络非线性行为的阶数索引；是网络节点的数量；in, is the output of the nonlinear adaptive network model; It is land use data; It is The weight of each network node; are network parameters; is the order index of the network’s nonlinear behavior; is the number of network nodes;
S2. 设计高维动态决策树算法，构建并动态调整决策树，通过综合评估函数，综合所有决策树的输出，形成对土壤侵蚀和土地利用关系的评估；综合评估函数的公式为：S2. Design a high-dimensional dynamic decision tree algorithm, build and dynamically adjust the decision tree, and use the comprehensive evaluation function to integrate the outputs of all decision trees to form an evaluation of the relationship between soil erosion and land use; the formula of the comprehensive evaluation function is:
， ,
其中，是综合评估函数；是归一化权重；是第棵决策树对于特征向量的动态调整输出；并分析决策树的每个节点从综合评估函数的值中提取出的各个影响因素的贡献度。in, is the comprehensive evaluation function; is the normalized weight; It is A decision tree for feature vector Dynamically adjust the output; and analyze the contribution of each influencing factor extracted from the value of the comprehensive evaluation function by each node of the decision tree.
优选的，所述S1，具体包括：Preferably, the S1 specifically includes:
对原始土壤数据进行数据转换，将原始土壤数据转换到高维特征空间，得到转换后的高维数据；具体实现过程如下：The original soil data is converted into a high-dimensional feature space to obtain the converted high-dimensional data. The specific implementation process is as follows:
， ,
其中，表示转换后的高维数据；是Sigmoid函数；、、、是转换参数；是数据维度的总数；表示第个数据维度；表示多项式的阶数索引；原始土壤数据涵盖了土壤物理和化学属性数据、土壤侵蚀数据以及地形和水文数据。in, Represents the transformed high-dimensional data; is the Sigmoid function; , , , is the conversion parameter; is the total number of data dimensions; Indicates Data dimensions; Represents the order index of the polynomial; the original soil data covers soil physical and chemical property data, soil erosion data, and topographic and hydrological data.
优选的，所述S2，具体包括：Preferably, the S2 specifically includes:
设计高维动态决策树算法，分析土壤侵蚀与土地利用的非线性关系。A high-dimensional dynamic decision tree algorithm was designed to analyze the nonlinear relationship between soil erosion and land use.
优选的，所述S2，还包括：Preferably, the S2 further includes:
在高维动态决策树算法的实现过程中，将土壤物理和化学属性数据、土地利用数据以及地形和水文数据进行融合，得到融合后的特征向量，具体数学公式表示为：In the implementation process of the high-dimensional dynamic decision tree algorithm, soil physical and chemical property data, land use data, topographic and hydrological data are fused to obtain the fused feature vector. The specific mathematical formula is expressed as follows:
， ,
其中，表示融合后的特征向量；表示原始土壤数据向量的第i个元素；是在时间的分析结果；、是用于控制指数转换强度的调节系数；是用于控制对数转换强度的调节系数；、是用于控制双曲正切转换强度的调节系数；是原始土壤数据向量中元素的数量。in, Represents the fused feature vector; represents the i-th element of the original soil data vector; yes In time The analysis results; , is the adjustment factor used to control the intensity of exponential conversion; is the adjustment factor used to control the strength of the logarithmic transformation; , is the adjustment coefficient used to control the intensity of the hyperbolic tangent conversion; is the number of elements in the original soil data vector.
优选的，所述S2，还包括：Preferably, the S2 further includes:
在构建决策树时，基于融合后的特征向量，通过最小化分割准则识别决策树最佳分割点；具体的数学表达式为：When constructing a decision tree, based on the fused feature vector, the optimal segmentation point of the decision tree is identified by minimizing the segmentation criterion; the specific mathematical expression is:
， ,
其中，表示确定决策树最佳分割点的分割准则函数；是调节分割准则的权重系数；是融合后的特征向量中第个元素；是分割阈值；是幂次；是用于平衡不同分割标准的调节系数。in, Represents the segmentation criterion function that determines the best segmentation point of the decision tree; is the weight coefficient for adjusting the segmentation criterion; is the fused feature vector Middle elements; is the segmentation threshold; is a power; It is an adjustment factor used to balance different segmentation criteria.
优选的，所述S2，还包括：Preferably, the S2 further includes:
引入控制决策树生长的函数，公式表达为：Introduce the function to control the growth of decision tree, the formula is expressed as:
， ,
其中，是控制决策树生长的函数；是分割前后的熵差；是当前树的深度；、、是控制树生长的关键参数；通过动态调整树的深度和分割的复杂性，平衡树的生长；根据树的当前深度和熵差调整生长速度和复杂性。in, is the function that controls the growth of the decision tree; is the entropy difference before and after segmentation; is the depth of the current tree; , , It is a key parameter for controlling tree growth; it balances tree growth by dynamically adjusting the depth of the tree and the complexity of the split; it adjusts the growth rate and complexity according to the current depth and entropy difference of the tree.
本发明的技术方案的有益效果是：The beneficial effects of the technical solution of the present invention are:
1、通过结合土壤物理化学属性、土地利用、地形和水文数据及气候环境因素，能够全面评估影响土壤侵蚀的各种因素，提高了分析的精确性，有助于更好地理解土壤侵蚀过程和土地利用的相互影响；通过高阶时间序列分析和非线性自适应网络模型，能够捕捉土壤属性随时间的变化趋势，以及土壤属性与土地利用之间的非线性关系，能够根据新数据或环境变化进行自我调整和更新，保持其预测和分析的准确性；1. By combining soil physical and chemical properties, land use, topographic and hydrological data and climate and environmental factors, it can comprehensively evaluate various factors affecting soil erosion, improve the accuracy of analysis, and help better understand the mutual influence of soil erosion process and land use; through high-order time series analysis and nonlinear adaptive network models, it can capture the changing trend of soil properties over time and the nonlinear relationship between soil properties and land use, and can self-adjust and update according to new data or environmental changes to maintain the accuracy of its prediction and analysis;
2、通过动态调整决策树的深度和复杂性，能有效控制模型的复杂度，避免过拟合，不仅能捕获数据中的复杂模式，而且能保持对新数据的泛化能力；通过提供一个强大的决策支持工具，帮助土壤科学家、环境工程师和土地管理者理解土壤侵蚀与土地利用之间的关系，预测未来的变化趋势，并据此制定有效的土壤保护和土地管理策略；2. By dynamically adjusting the depth and complexity of the decision tree, the complexity of the model can be effectively controlled to avoid overfitting. It can not only capture complex patterns in the data, but also maintain the ability to generalize to new data. By providing a powerful decision support tool, it helps soil scientists, environmental engineers and land managers understand the relationship between soil erosion and land use, predict future trends, and formulate effective soil protection and land management strategies accordingly.
3、通过将决策树算法的输出与地理信息系统（GIS）集成，可以直观地展示土壤侵蚀和土地利用状况，以及它们在不同地理位置的分布，有助于识别土壤侵蚀的高风险区域和土壤健康状况较差的区域，为地理特定的干预措施提供依据。3. By integrating the output of the decision tree algorithm with the geographic information system (GIS), soil erosion and land use conditions, as well as their distribution in different geographical locations, can be visually displayed, which helps to identify high-risk areas for soil erosion and areas with poor soil health, providing a basis for geographically specific intervention measures.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明一个实施例所提供的一种流域土地利用与土壤侵蚀关系动态评估方法流程图。FIG1 is a flow chart of a method for dynamically evaluating the relationship between watershed land use and soil erosion provided by an embodiment of the present invention.
具体实施方式DETAILED DESCRIPTION
为了更进一步阐述本发明为达成预定发明目的所采取的技术手段及功效，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
除非另有定义，本文所使用的所有的技术和科学术语与属于本发明的技术领域的技术人员通常理解的含义相同。Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
下面结合附图具体的说明本发明所提供的一种流域土地利用与土壤侵蚀关系动态评估方法的具体方案。The specific scheme of the method for dynamic assessment of the relationship between watershed land use and soil erosion provided by the present invention is described in detail below with reference to the accompanying drawings.
参照附图1，其示出了一种流域土地利用与土壤侵蚀关系动态评估方法流程图，该方法包括以下步骤：Referring to FIG. 1 , a flow chart of a method for dynamically evaluating the relationship between watershed land use and soil erosion is shown. The method comprises the following steps:
S1.收集原始土壤数据，将原始土壤数据转换到高维特征空间，并对转换后的高维数据进行时间序列分析，进一步基于时间序列分析的结果构建非线性自适应网络模型；S1. Collect original soil data, convert the original soil data into a high-dimensional feature space, perform time series analysis on the converted high-dimensional data, and further construct a nonlinear adaptive network model based on the results of the time series analysis;
从不同的流域区域采集土壤样本，确保土壤样本具有代表性，并覆盖了不同的土地利用类型（如农田、林地、建筑用地等）。将土壤样本在实验室中进行物理和化学属性分析，利用光谱分析、色谱法、质谱法等技术，确保得到准确的土壤属性数据，如土壤质地、pH值、有机物含量和重金属浓度等。为了增强对土壤侵蚀过程的理解，还收集土壤侵蚀数据，例如侵蚀速率和类型，以及地形和水文数据，如坡度、坡向和地下水位等。最终得到原始土壤数据。原始土壤数据涵盖了土壤物理和化学属性数据、土壤侵蚀数据以及地形和水文数据。Soil samples were collected from different watershed areas to ensure that the soil samples were representative and covered different land use types (such as farmland, forest land, construction land, etc.). The soil samples were analyzed for physical and chemical properties in the laboratory, using spectral analysis, chromatography, mass spectrometry and other techniques to ensure accurate soil property data, such as soil texture, pH value, organic matter content and heavy metal concentration. In order to enhance the understanding of the soil erosion process, soil erosion data, such as erosion rate and type, as well as topographic and hydrological data, such as slope, aspect and groundwater level, were also collected. Finally, the original soil data was obtained. The original soil data covers soil physical and chemical property data, soil erosion data, and topographic and hydrological data.
对原始土壤数据进行多维数据转换，将原始土壤数据转换到高维特征空间，揭示土壤特性和土地利用之间潜在的、非线性的关系。使用以下公式来实现这一转换：The original soil data is transformed into a high-dimensional feature space to reveal the potential, nonlinear relationship between soil properties and land use. The following formula is used to achieve this transformation:
， ,
其中，表示转换后的高维数据；代表原始土壤数据向量；是一个Sigmoid函数，用于标准化，确保数据在合理的范围内；、、、是转换参数，用于控制数据映射到高维空间的方式；是数据维度的总数；表示第个数据维度；表示多项式的阶数索引。正弦函数是一种能够模拟自然界周期性变化的基本函数，例如季节性的温度变化对土壤特性的影响。在处理复杂数据模式时，将数据从原始空间映射到高维空间，是机器学习中常用的技术；使得在原始空间中线性不可分的数据，在高维空间中变得线性可分。组合使用正弦函数和Sigmoid函数，可以增强模型的泛化能力。对原始土壤数据向量中的每一个分量进行变换,变换方式为，其中、、、是控制数据映射方式的参数，通过次方阶数增加维度，将不同阶的变换结果组合起来，加上偏置项,偏置项能够控制数据整体的平移，用Sigmoid函数标准化后，再将不同维度的数据累加，得到原始土壤数据向量高维映射转换后的高维数据。in, Represents the transformed high-dimensional data; represents the original soil data vector; It is a Sigmoid function, which is used for standardization to ensure that the data is within a reasonable range; , , , is a transformation parameter that controls how the data is mapped into a high-dimensional space; is the total number of data dimensions; Indicates Data dimensions; Represents the order index of the polynomial. The sine function is a basic function that can simulate periodic changes in nature, such as the impact of seasonal temperature changes on soil properties. When processing complex data patterns, mapping data from the original space to a higher-dimensional space is a common technique in machine learning; it makes data that is linearly inseparable in the original space become linearly separable in the higher-dimensional space. Combining the sine function with the sigmoid function can enhance the generalization ability of the model. For each component in the original soil data vector Transformation is performed by ,in , , , It is a parameter that controls the data mapping method. The power order increases the dimension, combines the transformation results of different orders, and adds the bias term ,The bias term can control the overall translation of the data. After standardization with the Sigmoid function, the data of different dimensions are accumulated to obtain the high-dimensional data after the high-dimensional mapping of the original soil data vector. .
为了捕捉和分析土壤属性随时间的变化趋势，进行高阶时间序列分析，预测未来土壤特性的变化，为土壤管理和保护提供决策支持。采用下面的公式进行时间序列分析：In order to capture and analyze the changing trends of soil properties over time, high-order time series analysis is performed to predict future changes in soil properties and provide decision support for soil management and protection. The following formula is used for time series analysis:
， ,
其中，表示时间序列分析的结果，即土壤特性；表示第个高维数据；是第个高维数据的权重，反映了不同数据对于土壤分析的重要性；是时间因子，决定时间变化对第个高维数据的影响；是高维数据的数量；是时间变量；是时间多项式的阶数索引；是最大阶数。是一个时间的多项式表示，用于模拟土壤属性随时间变化的非线性趋势。引入权重，调整不同高维数据在最终时间序列分析结果中的重要性。对转换后的第j个高维数据,乘以时间多项式，模拟土壤属性随时间变化的非线性趋势，再乘以权重参数,然后将时间序列中的所有数据组合起来得到时间序列分析结果。in, represents the results of the time series analysis, i.e., soil properties; Indicates High-dimensional data; It is The weight of each high-dimensional data reflects the importance of different data for soil analysis; is the time factor, which determines the effect of time change on the The impact of high-dimensional data; is the amount of high-dimensional data; is a time variable; is the order index of the time polynomial; is the maximum order. is a polynomial representation of time, used to simulate the nonlinear trend of soil properties changing with time. , adjust different high-dimensional data The importance of the final time series analysis results. , multiplied by the time polynomial , simulating the nonlinear trend of soil properties over time, multiplied by the weight parameter ,Then all the data in the time series are combined to obtain the time series analysis results.
构建非线性自适应网络模型，模拟土壤属性与土地利用之间的非线性关系，理解和量化土地利用变化如何影响土壤的物理和化学属性，进而影响土壤侵蚀过程。使用以下公式来构建非线性自适应网络模型：Construct a nonlinear adaptive network model to simulate the nonlinear relationship between soil properties and land use, understand and quantify how land use changes affect the physical and chemical properties of soil, and thus affect the soil erosion process. Use the following formula to construct a nonlinear adaptive network model:
， ,
其中，是非线性自适应网络模型的输出；是土地利用数据；是第r个网络节点的权重；是网络参数，用于控制第个网络节点的非线性行为；是网络非线性行为的阶数索引；是网络节点的数量。公式中的对数和指数函数组合，实质上是一个函数(，即将看作是)，是为了创建一个平滑的、非线性的转换，可以处理输入变量之间的非线性关系。使用来表示土壤特性与土地利用数据之间的差异，引入非线性行为的阶数索引，允许非线性自适应网络模型捕捉输入变量（如土壤特性和土地利用）之间的复杂、高阶互动。通过的形式引入函数。函数是一个平滑、非线性的函数，可以很好地处理输入变量之间的非线性关系。将每个网络节点经过非线性变换后的结果乘以相应的权重，对所有节点的结果进行求和，最终输出，代表了在给定土地利用数据下土壤属性通过非线性自适应网络模型处理后的结果，用来量化土地利用变化对土壤属性的影响。in, is the output of the nonlinear adaptive network model; It is land use data; is the weight of the rth network node; is a network parameter used to control the Nonlinear behavior of network nodes; is the order index of the network’s nonlinear behavior; is the number of network nodes. The logarithmic and exponential functions in the formula are combined , is essentially a function( , will soon Seen as ), is to create a smooth, nonlinear transformation that can handle nonlinear relationships between input variables. To represent soil properties and land use data The difference between the two introduces the order index of nonlinear behavior , allowing nonlinear adaptive network models to capture complex, high-order interactions between input variables such as soil properties and land use. Introduced in the form of function. The function is a smooth, nonlinear function that can handle nonlinear relationships between input variables well. The result of nonlinear transformation of each network node is multiplied by the corresponding weight , sum the results of all nodes and finally output , represents the result of soil properties processed by nonlinear adaptive network model under given land use data, and is used to quantify the impact of land use change on soil properties.
S2. 设计高维动态决策树算法，构建并动态调整决策树，综合所有决策树的输出，对土壤侵蚀和土地利用关系进行评估。S2. Design a high-dimensional dynamic decision tree algorithm, construct and dynamically adjust decision trees, integrate the outputs of all decision trees, and evaluate the relationship between soil erosion and land use.
设计一种高维动态决策树算法，对土壤侵蚀与土地利用的非线性关系进行深入分析，通过结合土壤物理化学属性、土地利用、地形和水文数据以及气候环境因素，形成对土壤侵蚀过程的全面评估。为了确保高维动态决策树算法可以处理来自不同源的数据，捕捉数据之间的潜在关系，将土壤物理化学属性、土地利用、地形和水文数据融合成一个统一的特征向量。考虑不同数据类型的特性，例如，土壤物理化学属性更适合通过指数函数处理，土地利用数据需要对数转换以反映其影响的非线性特性。数学公式为：A high-dimensional dynamic decision tree algorithm is designed to conduct an in-depth analysis of the nonlinear relationship between soil erosion and land use, and to form a comprehensive assessment of the soil erosion process by combining soil physical and chemical properties, land use, topographic and hydrological data, and climatic and environmental factors. In order to ensure that the high-dimensional dynamic decision tree algorithm can process data from different sources and capture the potential relationship between the data, the soil physical and chemical properties, land use, topographic and hydrological data are fused into a unified feature vector. Consider the characteristics of different data types. For example, soil physical and chemical properties are more suitable for processing by exponential functions, and land use data require logarithmic transformation to reflect the nonlinear characteristics of their influence. The mathematical formula is:
， ,
其中，表示融合后的特征向量；表示原始土壤数据向量的第i个元素；是在时间的分析结果；、是用于控制指数转换强度的调节系数；用于控制对数转换强度的调节系数；、是用于控制双曲正切转换强度的调节系数；是原始土壤数据向量中元素的数量。使用指数函数来处理土壤物理化学属性，适用于模拟土壤属性对侵蚀过程的影响，尤其是当影响随属性增加而快速变化时。采用对数函数转换土地利用数据，由调节系数控制，对数转换有助于反映土地利用对土壤侵蚀的非线性影响，适合于处理其变化的广度和深度，不是元素中时间t的分析结果，考虑了时间对土壤侵蚀过程的影响。通过双曲正切函数处理地形、水文数据和气候环境因素，双曲正切函数能够将数据规范化到-1到1的范围内，适合于表示地形、水文数据和气候环境因素的影响强度和方向。通过对每个原始土壤数据向量元素应用上述转换，并结合相应的调节系数（、、），计算出融合后的特征向量；综合多源数据和不同数据处理方法的结果，是进行土壤侵蚀分析的基础。in, Represents the fused feature vector; represents the i-th element of the original soil data vector; yes In time The analysis results; , is the adjustment factor used to control the intensity of exponential conversion; The adjustment factor used to control the strength of the log transformation; , is the adjustment coefficient used to control the intensity of the hyperbolic tangent conversion; is the number of elements in the original soil data vector. Using the exponential function To deal with soil physical and chemical properties, it is suitable for simulating the influence of soil properties on the erosion process, especially when the influence changes rapidly with the increase of properties. Convert land use data by adjusting the coefficients Control, logarithmic transformation helps to reflect the nonlinear effect of land use on soil erosion and is suitable for dealing with the breadth and depth of its changes. Not an element The analysis results of time t in the above example take into account the influence of time on the soil erosion process. To process topography, hydrological data and climate environmental factors, the hyperbolic tangent function can normalize the data to the range of -1 to 1, which is suitable for representing the intensity and direction of the impact of topography, hydrological data and climate environmental factors. Apply the above transformations, combined with the corresponding adjustment coefficients ( , , ), calculate the fused feature vector ; The results of integrating multi-source data and different data processing methods are the basis for soil erosion analysis.
作为一个实施例，在高维动态决策树算法的实现过程中，首先可以基于经验或随机初始化来初始化参数、、、、的值；使用如梯度下降法、遗传算法或其他现有的优化算法，来调整参数的值，以最小化预测误差或最大化预测准确率。通过交叉验证，如k折交叉验证，评估高维动态决策树算法的性能。As an embodiment, in the implementation process of the high-dimensional dynamic decision tree algorithm, the parameters can first be initialized based on experience or random initialization. , , , , The value of the parameter is adjusted using gradient descent, genetic algorithm or other existing optimization algorithms to minimize the prediction error or maximize the prediction accuracy. The performance of the high-dimensional dynamic decision tree algorithm is evaluated through cross-validation, such as k-fold cross-validation.
具体的，使用梯度下降法作为优化算法，目标是最小化预测误差。在这个过程中，参数的初始值可以随机设置，例如：Specifically, the gradient descent method is used as the optimization algorithm, and the goal is to minimize the prediction error. In this process, the initial values of the parameters can be set randomly, for example:
步骤一：初始值范围 [0.1, 1]；初始值范围 [0.01, 0.1]；初始值范围 [0.1, 1]；初始值范围 [0.1, 1]；初始值范围 [0.1, 1]；Step 1: Initial value range: [0.1, 1]; Initial value range: [0.01, 0.1]; Initial value range: [0.1, 1]; Initial value range: [0.1, 1]; Initial value range: [0.1, 1];
步骤二：在每一步迭代中，计算损失函数（如均方误差）并通过梯度下降法更新参数值；Step 2: In each iteration, calculate the loss function (such as mean square error) and update the parameter value by gradient descent;
步骤三：当对损失函数的改善小于根据专家经验法预设的阈值或达到最大迭代次数时，停止迭代；Step 3: When the improvement of the loss function is less than the threshold preset by the expert experience method or the maximum number of iterations is reached, stop the iteration;
步骤四：完成优化过程，得到高维动态决策树算法参数的具体数值：Step 4: Complete the optimization process and obtain the specific values of the high-dimensional dynamic decision tree algorithm parameters:
；；；；； ; ; ; ; ;
在构建决策树时，通过最小化某个特定的分割准则，识别最佳的分割点，分割准则不仅考虑了单一特征的影响，而且还考虑了特征之间的相互作用和复合效应。采用混合方法，结合传统的绝对值差和欧氏距离，提高分割的准确性和鲁棒性。数学表达式为：When building a decision tree, the best segmentation point is identified by minimizing a specific segmentation criterion. The segmentation criterion not only considers the influence of a single feature, but also the interaction and compound effect between features. A hybrid method is used to improve the accuracy and robustness of segmentation by combining the traditional absolute value difference and Euclidean distance. The mathematical expression is:
， ,
其中，表示确定决策树最佳分割点的分割准则函数，是调节分割准则的权重系数，是融合后的特征向量中第个元素，是分割阈值，用于确定分割点，是幂次，用于增加分割的敏感性，是用于平衡不同分割标准的调节系数。综合使用绝对值差和欧氏距离，分割准则不仅关注单一的异常值或数据分布的偏差，还考虑到数据点之间的整体关系。较高的值使分割过程中更敏感于离分割点近的数据点，有助于捕捉细微的数据结构变化。in, represents the segmentation criterion function that determines the optimal segmentation point of the decision tree, is the weight coefficient for adjusting the segmentation criterion, is the fused feature vector Middle elements, is the segmentation threshold, used to determine the segmentation point, is a power, used to increase the sensitivity of the segmentation, is a tuning factor used to balance different segmentation criteria. By combining absolute value difference and Euclidean distance, the segmentation criterion not only focuses on single outliers or deviations in data distribution, but also takes into account the overall relationship between data points. The value makes the segmentation process more sensitive to data points close to the segmentation point, which helps capture subtle changes in data structure.
作为一个实施例，首先获取数据集，包括含有不同土壤样本的某种化学成分浓度和对应的土壤侵蚀率的观测值；假设，，；进一步，定义目标函数：最小化，则目标函数的求解过程如下：As an example, firstly, a data set is obtained, including the concentration of a certain chemical component and the corresponding soil erosion rate of different soil samples. Observed value of ; assuming , , ; Further, define the objective function: minimize , then the solution process of the objective function is as follows:
首先计算的初始值，假设（土壤侵蚀率作为分割阈值）；进一步，采用网格搜索法来调整和，搜索范围为：1到3，：0.1到1，步长均为0.1；进一步，对于每个和值的组合，计算，选取使最小的参数组合作为最优参数；基于最优参数，对新数据点计算，找到最佳分割点；最后，得到不同值下的如下：First calculate Assuming the initial value of (Soil erosion rate as the segmentation threshold); further, a grid search method is used to adjust and , the search range is : 1 to 3, : 0.1 to 1, with a step size of 0.1; further, for each and Combination of values, calculation , select The smallest parameter combination is taken as the optimal parameter; based on the optimal parameters, the new data point is calculated , find the best split point; finally, get different Under value as follows:
当时，；当时，；当时，；when hour, ;when hour, ;when hour, ;
即当时，分割准则函数的值最小；因此，基于最小化的来选择参数，那么最优的值将是1。When When the segmentation criterion function The value of is the smallest; therefore, based on the minimization of To choose parameters, then the optimal The value will be 1.
通过动态调整树的深度和分割的复杂性，平衡树的生长，以防止过拟合，根据树的当前深度和熵变调整生长速度和复杂性。公式表达为：By dynamically adjusting the depth of the tree and the complexity of the segmentation, the growth of the tree is balanced to prevent overfitting, and the growth rate and complexity are adjusted according to the current depth and entropy change of the tree. The formula is expressed as:
， ,
其中，是控制决策树生长的函数；是分割前后的熵差，衡量信息增益的变化；是当前树的深度；、、是控制树生长的关键参数。通过控制决策树生长的函数调整树的生长，生成决策树（包括其结构和节点决策），供下一步的动态调整使用。使用Sigmoid函数来动态调整决策树的生长速度，该函数随着树的深度的增加，其值逐渐接近于，确保树的生长速度随深度增加而逐渐稳定，防止生长过快。参数控制Sigmoid函数的斜率，即控制生长速度调整的敏感性。作为函数的一个系数，决定了函数的上限，影响树的最大生长速度。加入熵变量()以调整树在分割时的复杂性，熵差衡量了分割前后信息增益的变化，其平方强调了高信息增益变化的重要性。调节了信息增益变化对树生长复杂性的影响程度；in, is the function that controls the growth of the decision tree; It is the entropy difference before and after segmentation, which measures the change in information gain; is the depth of the current tree; , , It is a key parameter for controlling tree growth. By adjusting the growth of the decision tree through the function that controls the growth of the decision tree, a decision tree (including its structure and node decisions) is generated for the next step of dynamic adjustment. To dynamically adjust the growth rate of the decision tree, this function increases with the depth of the tree As the value of , ensuring that the tree's growth rate gradually stabilizes as the depth increases, preventing it from growing too fast. Controls the slope of the Sigmoid function, that is, controls the sensitivity of the growth rate adjustment. As a coefficient of the function, it determines the upper limit of the function and affects the maximum growth rate of the tree. ) is used to adjust the complexity of the tree during splitting. The entropy difference measures the change in information gain before and after splitting, and its square emphasizes the importance of high information gain changes. The influence of information gain changes on the complexity of tree growth was adjusted;
作为一个实施例，首先基于文献调研或先前研究，给、、设定初步的估计值；再采用交叉验证结合网格搜索或贝叶斯优化来寻找最优参数组合，这需要定义一个性能指标作为优化目标，性能指标如分类准确率、AUC值或者均方误差（MSE）；进一步，将数据集划分为训练集和验证集，使用训练集来训练控制决策树生长的函数的模型，并在验证集上测试模型性能，以评估参数组合的效果。As an example, firstly based on literature research or previous studies, , , Set a preliminary estimate; then use cross-validation combined with grid search or Bayesian optimization to find the optimal parameter combination, which requires defining a performance indicator as the optimization target, such as classification accuracy, AUC value or mean square error (MSE); further, divide the data set into a training set and a validation set, use the training set to train the model of the function that controls the growth of the decision tree, and test the model performance on the validation set to evaluate the effect of the parameter combination.
定义、、的搜索范围和步长，系统地遍历所有可能的参数组合，找到性能最优的参数组合。采用贝叶斯优化来智能地搜索参数空间，以更有效率地找到最优参数，具体实现过程如下：definition , , The search range and step size are set to systematically traverse all possible parameter combinations to find the parameter combination with the best performance. Bayesian optimization is used to intelligently search the parameter space to find the optimal parameters more efficiently. The specific implementation process is as follows:
步骤一：根据选定的寻找最优参数组合的方法执行参数搜索过程，记录每一组参数的性能指标；Step 1: Perform parameter search process according to the selected method for finding the optimal parameter combination, and record the performance indicators of each set of parameters;
步骤二：分析不同参数组合下模型的性能，找到最优化的参数组合、、；Step 2: Analyze the performance of the model under different parameter combinations and find the optimal parameter combination , , ;
步骤三：使用独立的测试集验证最优参数组合的模型性能，确保模型具有良好的泛化能力；Step 3: Use an independent test set to verify the model performance of the optimal parameter combination to ensure that the model has good generalization ability;
经过上述过程，最终得到的最优参数估计值为：；；。After the above process, the optimal parameter estimate is finally obtained: ; ; .
为了使决策树能够适应新数据的加入，确保对于数据变化保持敏感和适应性。通过对多棵树的输出进行加权平均，并根据新数据调整权重，实现决策树的动态调整。公式为：In order to make the decision tree adapt to the addition of new data and ensure that it remains sensitive and adaptable to data changes, the decision tree can be dynamically adjusted by taking a weighted average of the outputs of multiple trees and adjusting the weights according to the new data. The formula is:
， ,
其中，是动态调整函数，用于根据新数据调整已有的决策树结构；是决策树的权重，反映了其对总决策的贡献程度；是决策树数量序数；是生成的决策树；分别代表输入的特征向量和深度参数；逻辑函数（或Sigmoid函数），表达式为，即可将看作是，是机器学习中常用的一个函数；体现了加权平均的思想。in, It is a dynamic adjustment function used to adjust the existing decision tree structure according to new data; is the weight of the decision tree, reflecting its contribution to the overall decision; is the ordinal number of decision trees; is the resulting decision tree; Represent the input feature vector and depth parameter respectively; the logical function (or Sigmoid function) is expressed as , you can Seen as , is a function commonly used in machine learning; It embodies the idea of weighted average.
综合所有决策树的输出，形成对土壤侵蚀和土地利用关系的全面评估。采用加权平均的方式来汇总所有决策树的输出，公式为：The outputs of all decision trees are combined to form a comprehensive assessment of the relationship between soil erosion and land use. The outputs of all decision trees are summarized by weighted average, and the formula is:
， ,
其中，是综合评估函数，汇总所有决策树的输出，即综合评分，是归一化权重，是第棵决策树对于给定特征向量的动态调整输出。in, is a comprehensive evaluation function that summarizes the outputs of all decision trees, i.e., the comprehensive score. is the normalized weight, It is A decision tree for a given feature vector Dynamically adjust the output.
分析决策树的每个节点从综合评估函数值中提取出的各个影响因素的贡献度，即子分数；在每个节点，决策树算法根据不同的特征（如土壤化学成分、土地利用类型等）做出分割决策，从而影响最终的综合评分。通过记录每个特征在决策过程中的作用和重要性，可以评估其对综合评分的贡献。例如，如果某个土壤化学成分在多个关键的分割点中起到了重要作用，那么可以认为它对综合评分有较大的贡献。Each node of the decision tree is analyzed to extract the contribution of each influencing factor from the comprehensive evaluation function value, i.e., the sub-score; at each node, the decision tree algorithm makes segmentation decisions based on different features (such as soil chemical composition, land use type, etc.), thereby affecting the final comprehensive score. By recording the role and importance of each feature in the decision-making process, its contribution to the comprehensive score can be evaluated. For example, if a soil chemical component plays an important role in multiple key segmentation points, it can be considered to have a greater contribution to the comprehensive score.
使用地理信息系统（GIS）将子分数和综合评分映射到具体的地理位置，可以识别土壤侵蚀风险高于预设阈值或土壤健康状况低于预设阈值的区域。通过专家经验法设定预设阈值。对不同因素与评分之间的关系进行统计分析，使用散点图、热力图等可视化工具展示不同因素如何影响土壤状况。Using a geographic information system (GIS) to map sub-scores and composite scores to specific geographic locations, we can identify areas where soil erosion risk is above a preset threshold or soil health is below a preset threshold. The preset thresholds are set using expert experience. Statistical analysis is performed on the relationship between different factors and the scores, and visualization tools such as scatter plots and heat maps are used to show how different factors affect soil conditions.
综上所述，便完成了一种流域土地利用与土壤侵蚀关系动态评估方法。In summary, a dynamic assessment method for the relationship between watershed land use and soil erosion has been completed.
发明实施例先后顺序仅仅为了描述，不代表实施例的优劣。在附图中描绘的过程不一定要求示出的特定顺序或者连续顺序才能实现期望的结果。在某些实施方式中，多任务处理和并行处理也是可以的或者可能是有利的。The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同或相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.
上述实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围，均应包含在本发明的保护范围之内。The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118246345B (en)* | 2024-05-22 | 2024-07-26 | 云南云金地科技有限公司 | A multi-objective collaborative optimization method for three-life space |
| CN118707988B (en)* | 2024-06-03 | 2025-02-11 | 江苏新杰智科技有限责任公司 | A UAV-assisted inspection method and system for power transmission lines |
| CN118898408A (en)* | 2024-10-09 | 2024-11-05 | 陕西省林业科学院 | Intelligent analysis method and system of forest and grassland soil nutrients based on multi-source data fusion |
| CN119513223A (en)* | 2025-01-21 | 2025-02-25 | 河北石安特智能科技有限公司 | A multidimensional data management system and method based on geographic information |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109033599A (en)* | 2018-07-18 | 2018-12-18 | 福州大学 | A kind of Influencing Factor of Soil Erosion importance analysis method based on random forest |
| CN113902580A (en)* | 2021-10-18 | 2022-01-07 | 四川农业大学 | A Reconstruction Method of Historical Farmland Distribution Based on Random Forest Model |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3161730B1 (en)* | 2014-06-30 | 2025-05-07 | Evolving Machine Intelligence Pty Ltd | A system and method for modelling system behaviour |
| AR109623A1 (en)* | 2018-02-16 | 2019-01-09 | Pescarmona Enrique Menotti | PROCESS AND SYSTEM OF ANALYSIS AND HYDROLOGICAL MANAGEMENT FOR BASINS |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109033599A (en)* | 2018-07-18 | 2018-12-18 | 福州大学 | A kind of Influencing Factor of Soil Erosion importance analysis method based on random forest |
| CN113902580A (en)* | 2021-10-18 | 2022-01-07 | 四川农业大学 | A Reconstruction Method of Historical Farmland Distribution Based on Random Forest Model |
| Publication number | Publication date |
|---|---|
| CN117993305A (en) | 2024-05-07 |
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