CN117634990A - Method for evaluating stability of freshwater ecosystem - Google Patents
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Abstract
Description
技术领域Technical field
本发明涉及淡水生态健康评价技术领域,具体涉及一种用于评估淡水生态系统稳定性的方法。The present invention relates to the technical field of freshwater ecological health assessment, and in particular to a method for assessing the stability of freshwater ecosystems.
背景技术Background technique
淡水生态系统作为一个复杂、开放、动态、非平衡和非线性系统。对于淡水生态系统稳定性的研究,有些只专注于生态系统中的生物部分(如分类学、生物多样性等),有些虽然考虑了生物与非生物两部分,但也仅是通过统计手段(如相关性分析和CCA分析等)将两者简单的联系起来。而在复杂多变的环境中,生物与非生物之间通过非线性相互作用耦合在一起,并且由于生态系统的开放性,二者自身也往往具有复杂结构。所以以前的方法不能够客观、准确的评估淡水生态系统稳定性。Freshwater ecosystem as a complex, open, dynamic, non-equilibrium and non-linear system. For studies on the stability of freshwater ecosystems, some only focus on the biological part of the ecosystem (such as taxonomy, biodiversity, etc.), while some consider both biological and abiotic parts, but only through statistical means (such as Correlation analysis and CCA analysis, etc.) simply connect the two. In complex and changeable environments, living things and non-living things are coupled together through nonlinear interactions, and due to the openness of ecosystems, they themselves often have complex structures. Therefore, previous methods cannot objectively and accurately assess the stability of freshwater ecosystems.
为了能够真实地描述淡水生态系统在时间和空间上的动力演化过程,对河流生态系统稳定性进行科学地评价与预测,有必要基于最大流原理构建淡水生态系统稳定性评估模型,除了考虑水生生物和水环境因子指标自身的重要性外,还会将各指标间的相互作用关系纳入到模型中,极大地减少以往评估方法的主观性,克服淡水生态系统稳定性在时间和空间上的不可比性,使得淡水生态系统稳定性的评估结果更加准确。In order to truly describe the dynamic evolution process of freshwater ecosystems in time and space, and scientifically evaluate and predict the stability of river ecosystems, it is necessary to construct a freshwater ecosystem stability assessment model based on the maximum flow principle. In addition to considering aquatic organisms In addition to the importance of the water environment factor indicators themselves, the interaction between each indicator will also be incorporated into the model, which greatly reduces the subjectivity of previous assessment methods and overcomes the incomparability of freshwater ecosystem stability in time and space. characteristics, making the assessment results of freshwater ecosystem stability more accurate.
发明内容Contents of the invention
本发明的目的在于提供一种用于评估淡水生态系统稳定性的方法,以淡水生态系统中水生生物和水环境因子指标间的相互作用关系为切入点,基于最大流原理,以提高淡水生态系统稳定性评估结果的准确性和客观性。The purpose of the present invention is to provide a method for evaluating the stability of freshwater ecosystems, taking the interaction between aquatic organisms and water environment factor indicators in freshwater ecosystems as the entry point, and based on the principle of maximum flow to improve the stability of freshwater ecosystems. Accuracy and objectivity of stability assessment results.
为了实现上述目的,本发明采用的技术方案为:一种用于评估淡水生态系统稳定性的方法,包括以下步骤:In order to achieve the above objectives, the technical solution adopted by the present invention is: a method for assessing the stability of freshwater ecosystems, which includes the following steps:
步骤S1,针对选择的研究区域,通过野外监测和室内实验对水生生物数据和水环境因子数据进行收集,最终形成研究区域的水生生物数据和水环境因子数据指标原始数据集;Step S1, for the selected research area, collect aquatic organism data and water environment factor data through field monitoring and indoor experiments, and finally form an original data set of aquatic organism data and water environment factor data indicators in the study area;
步骤S2,通过步骤S1中指标原始数据集的水生生物数据计算得到每种水生生物的Shannon-Wienner多样性指数,对指标原始数据集的水环境因子数据进行多重共线性诊断;处理完成后的水生生物数据和水环境因子数据形成指标预处理数据集;Step S2: Calculate the Shannon-Wienner diversity index of each aquatic organism through the aquatic organism data of the indicator original data set in step S1, and perform multicollinearity diagnosis on the water environment factor data of the indicator original data set; the aquatic organism after processing is completed Biological data and water environment factor data form an indicator preprocessing data set;
步骤S3,对步骤S2中指标预处理数据集进行均一化处理;Step S3: homogenize the indicator preprocessing data set in step S2;
步骤S4,基于最大流原理构建淡水生态系统稳定性评估模型;Step S4: Construct a freshwater ecosystem stability assessment model based on the maximum flow principle;
步骤S5,将步骤S3均一化处理完成的指标预处理数据集运用自组织映射网络仿真步骤S4中淡水生态系统稳定性的评估模型;Step S5, use the self-organizing mapping network to simulate the freshwater ecosystem stability evaluation model in step S4 using the indicator preprocessing data set completed by the homogenization process in step S3;
步骤S6,将步骤S5仿真得到的熵值和指标权重输入到Excel表格中,通过对熵值的分析评估淡水生态系统稳定性,通过对指标权重的分析识别淡水生态系统稳定性的驱动因子。Step S6: Input the entropy value and index weight obtained through the simulation in Step S5 into the Excel table, evaluate the stability of the freshwater ecosystem through the analysis of the entropy value, and identify the driving factors of the stability of the freshwater ecosystem through the analysis of the index weight.
进一步的,步骤S3中,对步骤S2中指标预处理数据集进行均一化处理;具体为:Further, in step S3, the indicator preprocessing data set in step S2 is normalized; specifically:
淡水生态系统的稳定性随着水生生物和水环境因子指标值的增加而提高,进行预归一化处理如公式(1)所示;The stability of freshwater ecosystems increases with the increase of aquatic organisms and water environment factor index values, and pre-normalization processing is performed as shown in formula (1);
(1); (1);
其中,o表示第o个指标,bo表示第o个指标的均一化值,xo表示第o个指标的实测值,xomin表示第o个指标的最小值,xomax表示第o个指标的最大值;Among them, o represents the oth indicator, b o represents the normalized value of the oth indicator, x o represents the measured value of the oth indicator, x omin represents the minimum value of the oth indicator, and x omax represents the oth indicator. the maximum value;
淡水生态系统的稳定性随着水生生物和水环境因子指标值的增加而降低,进行预归一化处理如公式(2)所示;The stability of freshwater ecosystems decreases as the index values of aquatic organisms and water environment factors increase. Pre-normalization processing is performed as shown in formula (2);
(2)。 (2).
进一步的,所述水生生物数据包括:浮游植物、浮游动物、底栖动物和鱼类;水环境因子数据包括总氮、总磷、化学需氧量、浊度和pH。Further, the aquatic organism data includes: phytoplankton, zooplankton, benthic animals and fish; the water environment factor data includes total nitrogen, total phosphorus, chemical oxygen demand, turbidity and pH.
进一步的,步骤S2中水生生物的Shannon-Wienner多样性指数,如公式(3)所示:Further, the Shannon-Wienner diversity index of aquatic organisms in step S2 is as shown in formula (3):
(3); (3);
其中,表示Shannon-Wienner多样性指数,a表示物种,S表示群落中所有物种的 个体总数,为第a个物种占总个体数的百分比。 in, represents the Shannon-Wienner diversity index, a represents the species, S represents the total number of individuals of all species in the community, is the percentage of species a in the total number of individuals.
进一步的,步骤S2中多重共线性诊断,采用方差膨胀因子法进行,依次删掉方差膨胀因子法最大的变量,即删除共线性的水环境因子,直到所有的方差膨胀因子法的水环境因子都小于5。Further, multicollinearity diagnosis in step S2 is carried out using the variance inflation factor method, and the variables with the largest variance inflation factor method are deleted in sequence, that is, the collinear water environment factors are deleted until all water environment factors of the variance inflation factor method are less than 5.
进一步的,步骤S4中基于最大流原理构建淡水生态系统稳定性评估模型,其中最大流原理指一个远离平衡的开放复杂系统总是寻找一种优化过程,使得开放复杂系统在给定的约束条件或代价下所获得的广义流达到最大值;具体过程如下:Further, in step S4, a freshwater ecosystem stability assessment model is constructed based on the maximum flow principle, where the maximum flow principle means that an open complex system far from equilibrium always looks for an optimization process so that the open complex system can operate under given constraints or The generalized flow obtained at the cost reaches the maximum value; the specific process is as follows:
步骤S41,水生生物和水环境因子指标是淡水生态系统演化的驱动力,淡水生态系统演化的驱动力表明水生生物和水环境因子指标接受广义信息流的能力,写成向量的形式为x=(x1, x2, ……, xn),是由n个淡水生态系统子系统组成一个Γ空间的体积单元为dx=(dx1, dx2, ……, dxn);淡水生态系统广义通量函数计算见公式(4);Step S41, aquatic organisms and water environment factor indicators are the driving force for the evolution of freshwater ecosystems. The driving force for the evolution of freshwater ecosystems indicates the ability of aquatic organisms and water environment factor indicators to accept generalized information flow. The vector form is x=(x 1 . _ _ _ _ For the calculation of quantity function, see formula (4);
(4); (4);
其中,J(x)表示设t时刻体积单元所获得的广义熵,η和为耦合系数,i、j、k、l分别为水生生物和水环境因子的四个指标,/> 和/>为指数项,定义为势函数,通过和/>反映淡水生态系统综合性能演化的特征;xi, xj, xk和xl分别为水生生物和水环境因子指标i、j、k和l的归一化值;Among them, J(x) represents the generalized entropy obtained by the volume unit at time t, eta and is the coupling coefficient, i, j, k, and l are the four indicators of aquatic organisms and water environment factors respectively,/> and/> is an exponential term, defined as a potential function, by and/> Reflect the characteristics of the evolution of the comprehensive performance of freshwater ecosystems; x i , x j , x k and x l are the normalized values of aquatic life and water environment factor indicators i, j, k and l respectively;
t时刻所有微观状态的平均广义熵见公式(5);The average generalized entropy of all microstates at time t is shown in formula (5);
(5); (5);
其中,表示淡水生态系统信息熵的一般特征,ρ(x,t)是t时刻淡水生态系统中水 生生物和环境因子指标的随机序列集合的时变概率密度函数,满足; in, Represents the general characteristics of information entropy of freshwater ecosystems. ρ(x,t) is the time-varying probability density function of a random sequence set of aquatic organisms and environmental factor indicators in freshwater ecosystems at time t, which satisfies ;
步骤S42,淡水生态系统的t时刻体积单元所获得的广义熵J(x)没有边界,需要约束来定义淡水生态系统;约束条件转化为一阶动量方程到四阶动量方程见公式(6):Step S42, the generalized entropy J(x) obtained by the volume unit at time t of the freshwater ecosystem has no boundary and requires constraints to define the freshwater ecosystem; the constraint conditions are converted into a first-order momentum equation to a fourth-order momentum equation, see formula (6):
(6); (6);
其中,f1为一阶动量方程,f2为二阶动量方程,f3为三阶动量方程,f4为四阶动量方程,< >表示统计平均值;其中淡水生态系统中必须存在四个以上水生生物和水环境因子指标的相互作用;Among them, f 1 is the first-order momentum equation, f 2 is the second-order momentum equation, f 3 is the third-order momentum equation, f 4 is the fourth-order momentum equation, <> represents the statistical average; among them, four must exist in the freshwater ecosystem The interaction between the above aquatic organisms and water environment factor indicators;
步骤S43,对淡水生态系统进行优化,在给定约束条件下获得最大的广义熵,在公式(6)所描述给定约束条件下,利用拉格朗日乘子最大化公式(5),具体表示为公式(7):Step S43: Optimize the freshwater ecosystem to obtain the maximum generalized entropy under given constraints. Under the given constraints described in formula (6), use Lagrange multipliers to maximize formula (5). Specifically, Expressed as formula (7):
(7); (7);
其中,ρ为概率密度,exp()表示以自然常数e为底的指数函数,通过拉格朗日优化得到;Among them, ρ is the probability density, exp() represents the exponential function with the natural constant e as the base, Obtained through Lagrangian optimization;
势函数保证淡水生态系统模式的稳定性,势函数表示为:The potential function ensures the stability of the freshwater ecosystem model. The potential function is expressed as:
(8); (8);
其中,势函数表示淡水生态系统演化的基本特征,x表示淡水生态系统中水生生物和环境因子指标的随机序列,/>和/>通过拉格朗日优化得到,/>为参数;利用广义势函数/>对河流生态系统的形成与演化进行理论分析,其性质由参数/>来决定,参数/>直接由调节着信息体相互作用的微观动力学规则的参数来决定;Among them, the potential function Represents the basic characteristics of freshwater ecosystem evolution, x represents a random sequence of aquatic organisms and environmental factor indicators in freshwater ecosystems,/> and/> Obtained through Lagrangian optimization,/> is a parameter; use the generalized potential function/> Conduct a theoretical analysis of the formation and evolution of river ecosystems, whose properties are determined by parameters/> To decide, parameters/> Directly determined by the parameters of microscopic dynamic rules that regulate the interaction of information bodies;
将公式(8)进行平移变换并对变换后的常数项矩阵对角化转化,得到公式(9):Perform translation transformation on formula (8) and diagonalize the transformed constant term matrix to obtain formula (9):
(9); (9);
其中,是淡水生态系统稳定性熵值,/>由各个指标相互作用连接形成,是评估淡水生态系统稳定状态的关键参数,ai表示指标xi对应的连接权值,xi为水生生物和水环境因子指标i的归一化值;in, is the stability entropy value of freshwater ecosystem,/> It is formed by the interactive connection of various indicators and is a key parameter to evaluate the stable state of freshwater ecosystems. a i represents the connection weight corresponding to the indicator x i , and x i is the normalized value of the aquatic life and water environment factor indicator i;
根据势函数方程与动态演化方程的关系,导出淡水生态系统有序模式的随机演化方程,表示为公式(10):According to the relationship between the potential function equation and the dynamic evolution equation, the stochastic evolution equation of the orderly pattern of the freshwater ecosystem is derived, expressed as formula (10):
(10); (10);
其中,表示对时间的一阶导数,λ是由ai组成矩阵的特征值,是反 映指标间相互作用的非线性相互作用项,F(t)是随机项。 in, express The first derivative with respect to time, λ is the eigenvalue of the matrix composed of a i , is a nonlinear interaction term that reflects the interaction between indicators, and F(t) is a random term.
进一步的,步骤S5中自组织映射网络是一类无监督学习的神经网络模型,具有处理复杂非线性问题的能力,对样本空间或者外界未知环境进行模拟或学习。Furthermore, the self-organizing mapping network in step S5 is a type of unsupervised learning neural network model, which has the ability to handle complex nonlinear problems and simulate or learn the sample space or the unknown external environment.
进一步的,步骤S5中将步骤S3均一化处理完成的指标预处理数据集运用自组织映射网络仿真,具体为:归一化处理完成的数据放入MATLAB 2018的自组织映射网络中进行仿真,训练步长设置为300,通过模拟得到指标权重与表示淡水生态系统稳定水平的熵值。Further, in step S5, the indicator preprocessing data set completed by the homogenization process in step S3 is simulated using a self-organizing mapping network, specifically: the data completed by the normalization processing is put into the self-organizing mapping network of MATLAB 2018 for simulation and training. The step size is set to 300, and the indicator weight and the entropy value indicating the stable level of the freshwater ecosystem are obtained through simulation.
本发明的有益效果是:(1)依托水生生物和水环境因子数据进行淡水生态系统稳定性评估,考虑了水生生物和水环境因子指标自身的重要性外,还涉及了各指标间的相互作用关系;基于最大流原理构建淡水生态系统稳定性评估模型,并采用自组织映射网络进行仿真,不仅可以直观的比较不同时间和空间淡水生态系统稳定性的演变趋势,还可以通过指标权重判断驱动淡水生态系统演化的关键因子。The beneficial effects of the present invention are: (1) Relying on aquatic organisms and water environment factor data to conduct freshwater ecosystem stability assessments, taking into account the importance of the aquatic organisms and water environment factor indicators themselves, and also involving the interaction between the indicators. relationship; a freshwater ecosystem stability assessment model is constructed based on the maximum flow principle, and a self-organizing mapping network is used for simulation. It can not only intuitively compare the evolution trends of freshwater ecosystem stability in different times and spaces, but also judge the driving force of freshwater through indicator weights. key factors in ecosystem evolution.
(2)该方法将水生生物和水环境因子相结合,把淡水生态系统当作一个动态的整体来分析,弱化了单一的因果关系,得到的结果可能更加接近本质。基于最大流原理从信息的角度对淡水生态系统的稳定性进行评价,本发明提供的方法具有较强的可操作性,可以快速、灵敏、准确、全面客观地反映淡水生态系统稳定性状况,探明关键驱动因子,为淡水生态系统的保护与修复措施提供重要的理论依据。(2) This method combines aquatic organisms and water environment factors, analyzes the freshwater ecosystem as a dynamic whole, weakens a single cause-and-effect relationship, and the results obtained may be closer to the essence. Based on the principle of maximum flow, the stability of the freshwater ecosystem is evaluated from the perspective of information. The method provided by the present invention has strong operability and can quickly, sensitively, accurately, comprehensively and objectively reflect the stability status of the freshwater ecosystem, and explore Identify key driving factors and provide important theoretical basis for protection and restoration measures of freshwater ecosystems.
附图说明Description of drawings
图1为淡水生态系统稳定性评价流程图;Figure 1 is a flow chart for freshwater ecosystem stability assessment;
图2为黄河干流不同河段水生态系统构形图;Figure 2 shows the configuration of the aquatic ecosystem in different sections of the main stream of the Yellow River;
图3为黄河干流不同河段水生态系统中各指标的权重图;Figure 3 is a weight diagram of each indicator in the aquatic ecosystem of different reaches of the main stream of the Yellow River;
图4为黄河干流不同河段水生态系统稳定性评估结果图。Figure 4 shows the results of aquatic ecosystem stability assessment in different reaches of the Yellow River mainstream.
具体实施方式Detailed ways
为使本发明实现的技术手段、创作特征、达成目的与功效易于明白了解,下面结合具体实施方式,进一步阐述本发明。应当理解,此处所描述的具体实施例仅用以解释本发明,并不用于限定本发明。In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention will be further elaborated below in conjunction with specific implementation modes. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.
如图1所示,一种用于评估淡水生态系统稳定性的方法,包括以下步骤:As shown in Figure 1, a method for assessing the stability of freshwater ecosystems includes the following steps:
步骤S1,针对选择的研究区域,通过野外监测和室内实验对水生生物数据和水环境因子数据进行收集,最终形成研究区域的水生生物数据和水环境因子数据指标原始数据集;Step S1, for the selected research area, collect aquatic organism data and water environment factor data through field monitoring and indoor experiments, and finally form an original data set of aquatic organism data and water environment factor data indicators in the study area;
步骤S2,通过步骤S1中指标原始数据集的水生生物数据计算得到每种水生生物的Shannon-Wienner多样性指数,对指标原始数据集的水环境因子数据进行多重共线性诊断;处理完成后的水生生物数据和水环境因子数据形成指标预处理数据集;Step S2: Calculate the Shannon-Wienner diversity index of each aquatic organism through the aquatic organism data of the indicator original data set in step S1, and perform multicollinearity diagnosis on the water environment factor data of the indicator original data set; the aquatic organism after processing is completed Biological data and water environment factor data form an indicator preprocessing data set;
步骤S3,对步骤S2中指标预处理数据集进行均一化处理;Step S3: homogenize the indicator preprocessing data set in step S2;
步骤S4,基于最大流原理构建淡水生态系统稳定性评估模型;Step S4: Construct a freshwater ecosystem stability assessment model based on the maximum flow principle;
步骤S5,将步骤S3均一化处理完成的指标预处理数据集运用自组织映射网络仿真步骤S4中淡水生态系统稳定性的评估模型;Step S5, use the self-organizing mapping network to simulate the freshwater ecosystem stability evaluation model in step S4 using the indicator preprocessing data set completed by the homogenization process in step S3;
步骤S6,将步骤S5仿真得到的熵值和指标权重输入到Excel表格中,通过对熵值的分析评估淡水生态系统稳定性,通过对指标权重的分析识别淡水生态系统稳定性的驱动因子。Step S6: Input the entropy value and index weight obtained through the simulation in Step S5 into the Excel table, evaluate the stability of the freshwater ecosystem through the analysis of the entropy value, and identify the driving factors of the stability of the freshwater ecosystem through the analysis of the index weight.
实施例:Example:
本发明以水生生物和水环境因子为基础数据,基于最大流原理构建稳定性评估模型对淡水生态系统稳定性进行评估,以黄河干流不同河段水生态系统为研究对象开展评估。This invention uses aquatic organisms and water environment factors as basic data, builds a stability evaluation model based on the maximum flow principle to evaluate the stability of freshwater ecosystems, and uses water ecosystems in different sections of the main stream of the Yellow River as research objects to conduct evaluations.
沿黄河干流源区至入海口共布设44个采样断面,其中26个自然河段,6个典型水库(龙羊峡、刘家峡、青铜峡、万家寨、三门峡和小浪底水库),每个水库包括三个采样断面,分别布设在库首、库中和库尾。在这44个采样断面进行了黄河干流水环境因子测定和水生生物样品的采集工作。A total of 44 sampling sections were arranged along the source area of the main stream of the Yellow River to the estuary, including 26 natural river sections and 6 typical reservoirs (Longyangxia, Liujiaxia, Qingtongxia, Wanjiazhai, Sanmenxia and Xiaolangdi Reservoirs). Each reservoir includes Three sampling sections are respectively arranged at the beginning, middle and end of the reservoir. At these 44 sampling sections, water environmental factors in the main stream of the Yellow River were measured and aquatic biological samples were collected.
水环境因子共计17种,包括水温(WT)、电导率(Cond)、溶解氧(DO)、pH、氧化还原电位(ORP)、总溶解性固体物质(TDS)、流速(V)、浊度(Tur)、化学需氧量(COD)、总磷(TP)、总溶解性磷(TDP)、正磷酸磷、总氮(TN)、氨氮、硝态氮、亚硝酸盐氮和总溶解性氮(TDN)。There are 17 types of water environment factors, including water temperature (WT), conductivity (Cond), dissolved oxygen (DO), pH, oxidation-reduction potential (ORP), total dissolved solids (TDS), flow rate (V), and turbidity (Tur), chemical oxygen demand (COD), total phosphorus (TP), total dissolved phosphorus (TDP), orthophosphate phosphorus, total nitrogen (TN), ammonia nitrogen, nitrate nitrogen, nitrite nitrogen and total solubility Nitrogen (TDN).
水生生物样品共计3种,包括浮游植物(FYZW)、浮游动物(FYDW)和底栖动物(DQDW)。There are 3 types of aquatic life samples, including phytoplankton (FYZW), zooplankton (FYDW) and benthos (DQDW).
对水环境因子采用多重共线性诊断,依次删掉最大的变量,直到所有的变量都小于5,剩余水环境因子共计12种,包括水温(WT)、电导率(Cond)、溶解氧(DO)、pH、氧化还原电位(ORP)、流速(V)、浊度(Tur)、化学需氧量(COD)、总磷(TP)、总溶解性磷(TDP)、亚硝酸盐氮和总溶解性氮(TDN)。Multicollinearity diagnosis is used for water environment factors, and the largest variables are deleted in order until all variables are less than 5. There are a total of 12 remaining water environment factors, including water temperature (WT), conductivity (Cond), and dissolved oxygen (DO). , pH, redox potential (ORP), flow rate (V), turbidity (Tur), chemical oxygen demand (COD), total phosphorus (TP), total dissolved phosphorus (TDP), nitrite nitrogen and total dissolved Nitrogen (TDN).
计算3种水生生物的Shannon-Wienner多样性指数。Calculate the Shannon-Wienner diversity index of three aquatic organisms.
将3种水生生物Shannon-Wienner多样性指数和12种水环境因子数据进行归一化处理,结果见表1:The data of three kinds of aquatic organisms Shannon-Wienner diversity index and 12 kinds of water environment factors were normalized. The results are shown in Table 1:
表1 黄河干流水生生物和水环境因子指标归一化处理结果Table 1 Normalization results of aquatic organisms and water environment factor indicators in the main stream of the Yellow River
将各指标的归一化数据按照不同河段制作成雷达图(参见图2),在每个雷达图中,线条框内代表了河流生态系统网络的特征,这是由水生生物和水环境因子指标产生的。它们是广义信息通量的流动模式,更直观、形象地表达了淡水生态系统结构的空间扩展过程。The normalized data of each indicator are made into radar charts according to different river sections (see Figure 2). In each radar chart, the line box represents the characteristics of the river ecosystem network, which is caused by aquatic organisms and water environment factors. indicators are generated. They are flow patterns of generalized information flux, which more intuitively and vividly express the spatial expansion process of freshwater ecosystem structure.
再将各指标的归一化数据按照不同河段导入MATLAB 2018的自组织映射网络中进行仿真,最后通过模拟得到指标权重aij(参见图3)与表示淡水生态系统稳定水平的熵值(参见图4)。The normalized data of each indicator is then imported into the self-organizing mapping network of MATLAB 2018 for simulation according to different river sections. Finally, the indicator weight a ij (see Figure 3) and the entropy value indicating the stability level of the freshwater ecosystem are obtained through simulation. (See Figure 4).
其中,指标权重反映了淡水生态系统中各指标之间的竞争与配合。它们反映了指标对熵值的影响。指标的权重越大,指标对熵值的影响越大。Among them, the indicator weight reflects the competition and cooperation between indicators in the freshwater ecosystem. They reflect the impact of indicators on entropy values. The greater the weight of the indicator, the greater the impact of the indicator on the entropy value.
由图3可知,黄河四个河段的水生态系统中权重均大于0.6的指标包括Cond、V、Tur和TDP。这四个指标权重的平均值分别为0.787,0.706,0.775和0.732。It can be seen from Figure 3 that the indicators with weights greater than 0.6 in the aquatic ecosystem of the four reaches of the Yellow River include Cond, V, Tur and TDP. The average values of these four indicator weights are 0.787, 0.706, 0.775 and 0.732 respectively.
由此可知,Cond对黄河水生态系统稳定性的影响最大,其次是Tur、TDP和V。It can be seen that Cond has the greatest impact on the stability of the Yellow River water ecosystem, followed by Tur, TDP and V.
由图4可知,黄河干流水生态系统从0到130步进行自组织,从而导致在环境影响下的振荡过程。当步数超过130时,随着步数的增加,黄河干流不同河段水生态系统的优势模式均保持不变,表明仿真结果准确可信。It can be seen from Figure 4 that the water ecosystem of the main stream of the Yellow River self-organizes from 0 to 130 steps, resulting in an oscillation process under the influence of the environment. When the number of steps exceeds 130, as the number of steps increases, the dominant patterns of the water ecosystem in different reaches of the Yellow River mainstream remain unchanged, indicating that the simulation results are accurate and credible.
黄河源区至下游水生态系统稳定性的熵值分别为6.864、5.855、5.282和3.939。Entropy value of aquatic ecosystem stability from the source area to the lower reaches of the Yellow River They are 6.864, 5.855, 5.282 and 3.939 respectively.
由此可见,黄河源区水生态系统稳定性最好,其次是上游和中游,下游的最差。It can be seen that the water ecosystem stability in the source area of the Yellow River is the best, followed by the upstream and middle reaches, and the downstream is the worst.
综上所述,以水生生物和水环境因子数据为基础,基于最大流原理构建的淡水生态系统稳定性评估方法可以很好的适用于淡水生态系统稳定性的评估。In summary, the freshwater ecosystem stability assessment method based on aquatic organisms and water environment factor data and based on the maximum flow principle can be well applied to the assessment of freshwater ecosystem stability.
以上所述仅为本发明的实施例,并非因此限制本发明的专利范围,凡是利用本发明说明书内容所作的等效结构或等效流程变换,或直接或间接运用在其他相关的技术领域,均同理包括在本发明的专利保护范围内。The above are only examples of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the content of the description of the present invention, or directly or indirectly applied in other related technical fields, shall be regarded as Likewise, it is included in the patent protection scope of the present invention.
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