CN111090831A - Lake area change key driving factor identification method - Google Patents

Lake area change key driving factor identification method Download PDF

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CN111090831A
CN111090831A CN201911146370.5A CN201911146370A CN111090831A CN 111090831 A CN111090831 A CN 111090831A CN 201911146370 A CN201911146370 A CN 201911146370A CN 111090831 A CN111090831 A CN 111090831A
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任黎
高家琛
周悦
阴帅妮
徐伟
徐健
李大勇
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Abstract

The invention discloses a method for identifying key driving factors of lake area change, which comprises the following steps: counting long-time lake surface area sequences; acquiring driving factor data; drawing a single-factor linear regression graph of each driving factor and the lake surface area change according to a single-factor linear analysis method, and analyzing the key degree of each driving factor; analyzing the correlation coefficient between each driving factor and the lake surface area by using a pearson correlation analysis method; analyzing the obtained driving factors which have obvious linear relation with the lake surface area by adopting a multivariate linear regression analysis method, and checking the fitting degree; and comprehensively analyzing to obtain key driving factors influencing the lake surface area change according to the analysis result. The method provided by the invention integrates multiple factors, improves the accuracy and the rationality of identifying the key driving factors of the lake area change based on methods such as a single-factor linear analysis method, a pearson correlation analysis method and a multiple linear regression analysis method, and has strong practicability and universality in the area protection aspect of the lake area.

Description

Lake area change key driving factor identification method
Technical Field
The invention belongs to the technical field of wave-resistant forests, and particularly relates to a method for identifying key driving factors of lake area change.
Background
Lakes are key components of global water resources, are important carriers for surrounding ecological environments and human life, and the fluctuation of areas of lakes can directly affect the production and living water of surrounding human beings and the stability of the ecological environments. The lake area is reduced, the water storage and flood discharge capacity is poor, sufficient water sources cannot be provided in dry seasons, the flood peak regulation capacity is poor in flood season, flood disasters easily occur, the climate regulation capacity is reduced, the ecological environment is damaged, and the survival and development of human beings are affected. Therefore, research on long time-series change trend and driving force of the lake area becomes a current hotspot, and the accuracy of identifying key driving factors of the lake area change depends on a correlation analysis method. Therefore, a set of identification technology is needed to be established so as to provide technical support for identifying the key driving factor of the lake area change.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, a method for identifying key driving factors of lake area change is provided.
The technical scheme is as follows: in order to achieve the purpose, the invention provides a method for identifying key driving factors of lake area change, which comprises the following steps:
s1: counting long-time lake surface area sequences;
s2: acquiring driving factor data;
s3: drawing a single-factor linear regression graph of each driving factor and the lake surface area change by using Excel according to a single-factor linear analysis method, and analyzing the key degree of each driving factor;
s4: analyzing the correlation coefficient between each driving factor and the lake surface area by using a pearson correlation analysis method;
s5: analyzing the driving factors which have obvious linear relation with the lake surface area and are obtained in the steps S3 and S4 by adopting a multivariate linear regression analysis method, and checking the fitting degree;
s6: according to the analysis results of the steps S3, S4 and S6, key driving factors influencing the lake surface area change are obtained through comprehensive analysis.
Further, the statistics of the long-time lake surface area sequence in step S1 are specifically: based on high-resolution remote sensing data, water body coverage conditions in the lake region range are extracted in batches by using Arcgis and Matlab software, and long-time lake surface area sequences are obtained through statistics.
Further, the driving factors in step S2 include natural driving factors including rainfall in the lake region, evaporation capacity, river inflow, and runoff volume at the lake outlet station, and artificial driving factors including production and domestic water consumption. When the driving factors of the area change of the lake region are analyzed, natural driving factors such as river incoming water, precipitation, evaporation capacity and runoff of the lake outlet station are selected, and meanwhile, the influence of various factors is fully considered by combining artificial activity driving factors such as production and living water consumption and the like and combining multiple factors.
The main driving factors of the lake surface area change are the rainfall, the evaporation, the production and living water consumption, the river inflow and the runoff of the lake outlet station. Selecting relevant stations of a rainfall station, an evaporation station and a river hydrological control station in a lake area, carrying out third investigation and evaluation on relevant results according to a regional hydrological network, a water resource bulletin and accessories thereof, a meteorological data network and national water resource investigation, carrying out statistics to obtain rainfall, evaporation capacity, runoff and production and living water consumption of the corresponding stations, and finally obtaining a data set of the rainfall, the evaporation capacity of the lake area and the runoff of the lake outlet station in each year according to an arithmetic mean method.
Further, a single-factor linear analysis method, i.e. a unitary linear regression analysis method, in which a mathematical relationship between variables is determined based on a set of sample data, and two variables having a linear correlation relationship can be approximated by a linear equation, includes the following calculation formula:
Figure BDA0002282316470000021
Figure BDA0002282316470000022
Figure BDA0002282316470000023
Figure BDA0002282316470000024
wherein, the formulas (1) to (3) are simple linear regression equations, and the formula (4) is a determination coefficient r2B is a constant term of a linear regression equation, representing an intercept; a is the regression coefficient, i.e. slope, of the sample; r is2Also known as R2Referred to as decision coefficients, whose values are the squares of the correlation coefficients, are measures of goodness of fit to the estimated regression equation.
Further, the Pearson correlation analysis method in step S4 is a method for determining a linear relationship between two variables by using a Pearson correlation calculation formula, wherein the correlation coefficient is a measure of the degree of linear correlation, the coefficient is between-1 and +1, the closer the absolute correlation coefficient is to 1, the closer the data point falls on a straight line, so the Pearson correlation coefficient represents the linear relationship between the factors, and the calculation formula of the Pearson correlation coefficient is formula (5).
Figure BDA0002282316470000025
Further, defining that 0< | r | < 0.4 is weak correlation or no correlation, 0.4< | r | < 0.6 is medium-range correlation, 0.6< | r | < 0.8 is strong correlation, and 0.8< | r | < 1.0 is strong correlation, and adopting T test to make significance test on the obtained correlation coefficient in the step S4, and the specific test calculation formula is formula (6):
Figure BDA0002282316470000031
wherein r is a correlation coefficient, t is a statistic value corresponding to the correlation coefficient, and n is the number of samples.
Further, in order to study the correlation between independent variables and dependent variables in more detail, the driving factors having significant linear relationship with the lake surface area obtained in steps S3 and S4 are analyzed by using a multiple linear regression analysis model, in the economics of measurement, a regression model containing two or more explanatory variables is called a multiple regression model, and the regression analysis performed on the basis of the multiple linear regression model is called multiple regression analysis, and the multiple linear regression model is specifically represented by formula (7):
Figure BDA0002282316470000032
wherein, β0Is a constant term β1，β2，β3Is a partial regression coefficient, i.e. when x increases or decreases by one unit while the other independent variables remain unchanged
Figure BDA0002282316470000033
Average amount of change of (d); x is the number of1,x2,x3The independent variables are respectively corresponding to the driving factors which are obtained in the step S3 and the step S4 and have obvious linear relation with the lake surface area;
Figure BDA0002282316470000034
is a dependent variable; ε is the random error.
Further, the fitting degree in the step S5 is specifically:
using MATLAB software to program and obtain a sample scatter diagram between dependent variables and independent variables, and using the MATLAB software to program and run to obtain β based on least square method0，β1，β2，β3The confidence interval does not contain the origin, a residual point r and a confidence interval map are made, and a statistic variable stats (correlation variable stats) is calculatedCoefficient R2, assuming test statistic F, probability P corresponding to F), judging whether linear correlation is significant, and using the obtained linear regression model to make dependent variable estimation value of multiple linear regression analysis
Figure BDA0002282316470000035
And fitting the graph and the graph of the actual value y to check the fitting degree.
Has the advantages that: compared with the prior art, the method provided by the invention integrates multiple factors, improves the accuracy and rationality of identifying the key driving factors of the lake area change based on methods such as a single-factor linear analysis method, a pearson correlation analysis method and a multiple linear regression analysis method, and has strong practicability and universality in the area protection aspect of the lake area.
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FIG. 1 is a diagram showing the results of linear analysis of the evaporation capacity of a lake region and the area of the lake surface;
FIG. 2 is a diagram showing the results of a linear analysis of the rainfall in a lake region and the area of the lake surface;
FIG. 3 is a diagram showing the results of linear analysis of the runoff of a lake exit station and the area of the lake surface;
FIG. 4 is a graph showing the results of a linear analysis of the amount of water consumed in producing a live water versus the area of a lake surface;
FIG. 5 is a graph showing the results of linear analysis of the total water of the five rivers and the area of the lake surface;
FIG. 6 is a scatter plot of the five-river incoming water aggregate versus the Poyang lake area;
FIG. 7 is a scatter plot of rainfall versus Poyang lake area;
FIG. 8 is a scatter plot of lake outlet station runoff versus Poyang lake area;
FIG. 9 is a graph of residuals and their confidence intervals;
FIG. 10 is a schematic diagram showing the variation rule between the estimated value of Poyang lake surface area and the actual value.
Detailed Description
The invention is further elucidated with reference to the drawings and the embodiments.
The embodiment applies the method of the invention to Poyang lake for identifying the key driving factor of Poyang lake area change, and the specific steps are as follows:
the method comprises the following steps: statistics of long-time lake surface area sequence of Yanghu
Based on FROM-GLC remote sensing data, a Poyang lake surface area sequence of 2001-2015 year by year is obtained through statistics of water body batch extraction in the Poyang lake range by Arcgis and Matlab software, the lake surface area of Poyang lake 2001-2015 year by year is calculated in batch by using Excel software, and statistical data are shown in Table 1.
Table 12001-2015-year Poyang lake area sequence units: km2
Figure BDA0002282316470000041
Step two: obtaining drive factor data
The reduction of the Poyang lake surface area is mainly determined by multiple factors of the water inflow of the five rivers, the natural precipitation of the lake area, the evaporation capacity of the lake area, the water consumption for production and living and the runoff of the lake outlet station. Uniformly distributed rainfall stations are selected around the lake area and are respectively a lake outlet station, a Wucheng station, a Lijiadu station and a Boyang station. According to water conservancy general survey and national water resource third survey evaluation related results and related data of a China meteorological data network, an annual precipitation data set in the year 2001-2015 of four rainfall stations in the Poyang lake region is obtained through statistics, and the precipitation in the Poyang lake region is obtained through an arithmetic mean method, which is specifically shown in Table 2.
Selecting two evaporation stations of birchleaf and Kangshan station, counting to obtain annual evaporation capacity data sets of birchleaf in Poyang lake region and 2001 + Onage in Kangshan station in 2015 according to the third investigation and evaluation result of national water resource, and obtaining the annual evaporation capacity of Yangguan water surface in 2001 + 2015 by an arithmetic mean method, as shown in Table 3.
According to the water resource bulletin, accessories of the water resource bulletin and related results of the third investigation and evaluation of national water resources, the water consumption for production and living in the Poyang lake region in 2001-2015 is obtained through statistics. According to the statistical data of the water resource communique, the water consumption rates (basically unchanged) of agriculture, thermal power, general industry, life and ecology in the Poyang lake region 2004-2008 are obtained, so the average value is taken as the water consumption rate of each industry in the 2001-2015 year, and the corresponding water consumption of each industry in the Poyang lake region 2001-2015 year is calculated, which is specifically shown in Table 4.
Yanghu is the only way for five rivers to enter the river, so the Poyang lake water resource quantity is mostly originated from the Jiangxi, the pacifying, the Xin, the West river (also called Zhanghe) and the big river of the five rivers and the Boyang river, and the water system of the West river (also called Zhanghe) flows into the lake, and the water quantity of the five rivers is an important driving factor for the change of the water surface of the Poyang lake. In this embodiment, the water volume of the five major water systems is represented by the runoff of the downstream hydrologic control station of the five river systems, and the runoff of the five river hydrologic control station 2001-2015, that is, the water volume of the five river is obtained through statistics by third investigation and evaluation of relevant results of national water resources, as shown in table 5.
The lake outlet hydrology control station is located at the intersection of the Yangtze river and the Yangtze lake and is the only outlet for collecting the Poyang lake water quantity into the Yangtze river, when the main flow quantity of the Yangtze river is increased and the main flow water level is higher than the Poyang lake water level, the water flow of the Yangtze river can flow backward to enter the Yangtze lake, and therefore the change of the runoff quantity of the lake outlet station is one of driving factors of the change of the Poyang lake water level area. According to the related results of the third investigation and evaluation of national water resources, the runoff of lake outlet hydrology stations 2001-2015 is shown in Table 6.
The unit of the precipitation in Poyang lake 2001-2015. mm is
Figure BDA0002282316470000051
In table 3, the water surface evaporation amount in the Poyang lake area 2001-2015. mm is
Figure BDA0002282316470000052
Figure BDA0002282316470000061
The. Hundred million (um)3
Figure BDA0002282316470000062
Table 5. water from five rivers. Hundred million (um)3
Figure BDA0002282316470000063
Figure BDA0002282316470000071
A. Billion m3
Figure BDA0002282316470000072
Step three: one-factor linear analysis method
In this embodiment, a single-factor linear regression graph of each driving factor and the change of the Poyang lake area is drawn by Excel according to a single-factor linear analysis method, which includes the following calculation formula:
Figure BDA0002282316470000073
Figure BDA0002282316470000074
Figure BDA0002282316470000075
Figure BDA0002282316470000076
wherein, the formulas (1) to (3) are linear regression equations, and the formula (4) is a determination coefficient r2B is a constant term of a linear regression equation, representing an intercept; a is the regression coefficient, i.e. slope, of the sample; r is2Also known as R2Referred to as the decision coefficient, whose value is the square of the correlation coefficient,is a measure of the goodness of fit of the estimated regression equation.
The finally obtained single-factor linear regression graphs of each driving factor and the Poyang lake area change are shown in FIGS. 1 to 5, and can be seen from the graphs:
(1) among all 5 indexes, 3 indexes such as the total of the water from the five rivers, the rainfall capacity of the lake area, the runoff quantity of the lake outlet and the like are positively correlated with the change of the Poyang lake surface area, and 2 indexes such as the evaporation capacity of the lake area, the water consumption for production and living of the lake area and the like are negatively correlated with the change of the lake surface area.
(2) From the decision coefficient R2The judgment coefficients of the total of five waters, the runoff of the lake outlet station and the rainfall of the lake region are all above 0.65, the linear regression fitting is good, so the contribution to the area change of the lake region is large, the judgment coefficients of the production and living water consumption of the lake region, the water surface evaporation capacity of the lake region and the area of the lake surface are about 0.1, the fitting degree is not high, and the correlation is not obvious.
Step four: pearson correlation analysis method
And analyzing the correlation between the 5 driving factors and the Poyang lake surface area by applying a pearson correlation analysis method.
The Pearson correlation analysis method is a method for measuring the linear relationship between two variables by using a Pearson correlation calculation formula, wherein the correlation coefficient is used for measuring the degree of linear correlation, the coefficient is between-1 and +1, the closer the absolute correlation coefficient is to 1, the closer the data points are located on a straight line, so the Pearson correlation coefficient represents the linear relationship between factors, and the Pearson correlation coefficient calculation formula is the formula (5):
Figure BDA0002282316470000081
in this embodiment, 0< | R | ≦ 0.4 is defined as weak correlation or no correlation, 0.4< | R | ≦ 0.6 is defined as medium-range correlation, 0.6< | R | ≦ 0.8 is strong correlation, 0.8< | R | ≦ 1.0 is very strong correlation, and the pearson correlation coefficient calculation results are shown in table 7. it can be seen from table 7 that (1) the area of the Poyang lake surface is summed up with the incoming water of five rivers, the pearson correlation coefficients of the lake outlet station runoff and the 4 indexes of the lake area rainfall are all higher than 0.8 and are very strong correlation with the area change of the Poyang lake surface, and the analysis of the determination coefficient R2 in the same step three is consistent, which indicates that the incoming water, the incoming water of five rivers, the lake outlet station runoff and the rainfall of the lake area are key driving factors for the area change of Poyang lake.
(2) Pearson correlation coefficients between the lake area evaporation capacity, the production and living water consumption and the lake surface area are respectively-0.400 and-0.358, and the correlation coefficients are lower and have no correlation with the lake surface area.
Results of correlation calculation of area diagnostic index pearson in table 7 Poyang lake
Figure BDA0002282316470000091
And (3) carrying out significance test by adopting t test on the obtained correlation coefficient, wherein the specific test calculation formula is formula (6):
Figure BDA0002282316470000092
wherein r is a correlation coefficient, t is a statistic value corresponding to the correlation coefficient, and n is the number of samples.
The t-statistic value of each correlation coefficient is obtained by applying t test, for example, when the significance level α is 0.01, the critical value of the degree of freedom (n-2) of the t distribution table is 2.650, when the significance level α is 0.05, the critical value of the degree of freedom (n-2) of the t distribution table is 1.771, and from table 8, the total amount of the five river incoming water, the lake outlet station runoff and the lake area rainfall are all significant on the 0.01 level, and have a very significant linear relation with the change of the area of the Poyang lake surface, and the evaporation capacity of the lake area, the water consumption for production and living and the area of the Poyang lake surface have no significant linear relation.
TABLE 8 t statistic values for significance test
Figure BDA0002282316470000093
Note: very significant correlation at 0.01 level (bilateral)
Step five: multiple linear regression analysis
In order to study the correlation between independent variables and dependent variables more accurately and in detail, the driving factors which have significant linear relation with the lake surface area and are obtained in the third step and the fourth step are analyzed by adopting a multiple linear regression analysis model, in the metrological economics, the regression model containing more than two explanatory variables is called a multiple regression model, and the regression analysis performed on the basis of the multiple regression model is called multiple regression analysis, wherein the multiple linear regression model is specifically shown in formula (7):
Figure BDA0002282316470000094
wherein, β0Is a constant term β1，β2，β3Is a partial regression coefficient, i.e. when x increases or decreases by one unit while the other independent variables remain unchanged
Figure BDA0002282316470000101
Average amount of change of (d); x is the number of1,x2,x3The independent variables are respectively corresponding to the driving factors which are obtained in the step S3 and the step S4 and have obvious linear relation with the lake surface area;
Figure BDA0002282316470000102
is a dependent variable; ε is the random error.
In this example, a sample scattergram between dependent variables and independent variables was programmed with MATLAB software, and the total of five water streams (x)1) Rainfall (x)2) Lake outlet station runoff (x)3) The scatter plot between Poyang lake area is shown in FIGS. 6-8. Statistical variable stats (correlation coefficient R2, hypothesis test statistic F, probability P corresponding to F) table, see Table 9, residual and confidence intervals as shown in FIG. 9, dependent variable estimates for multiple linear regression analysis
Figure BDA0002282316470000103
The fit and the actual value y are plotted in fig. 10.
TABLE 9 model parameters
Figure BDA0002282316470000104
From table 9, where P is 0.0000, and P < α is 0.01, it is described that the dependent variable and the independent variable have significant linear correlation, and the obtained linear regression model is usable, as shown in formula (8), fig. 9 shows that all confidence intervals of residuals include zero points and no abnormal points, and further shows that the multiple linear regression model is usable, as shown in fig. 10, the change law of the estimated value of the Poyang lake area and the change law of the actual value are substantially consistent, the fitting degree is high, and it is known that the total of the five river incoming waters, the rainfall in the lake area, and the station runoff at the lake outlet are 3 key driving factors affecting the change of the Poyang lake area.
Figure BDA0002282316470000105

Claims (8)

1. A lake area change key driving factor identification method is characterized by comprising the following steps:
s1: counting long-time lake surface area sequences;
s2: acquiring driving factor data;
s3: drawing a single-factor linear regression graph of each driving factor and the lake surface area change according to a single-factor linear analysis method, and analyzing the key degree of each driving factor;
s4: analyzing the correlation coefficient between each driving factor and the lake surface area by using a pearson correlation analysis method;
s5: analyzing the driving factors which have obvious linear relation with the lake surface area and are obtained in the steps S3 and S4 by adopting a multivariate linear regression analysis method, and checking the fitting degree;
s6: according to the analysis results of the steps S3, S4 and S6, key driving factors influencing the lake surface area change are obtained through comprehensive analysis.
2. The method for identifying key driving factors of lake area change according to claim 1, wherein the statistics of the long-time lake surface area sequence in the step S1 are specifically as follows: based on high-resolution remote sensing data, water body coverage conditions in the lake region range are extracted in batches by using Arcgis and Matlab software, and long-time lake surface area sequences are obtained through statistics.
3. The method as claimed in claim 1, wherein the driving factors in step S2 include natural driving factors including rainfall in the lake area, evaporation capacity, river inflow, runoff at the lake outlet station, and artificial driving factors including production and domestic water consumption.
4. The method for identifying the key driving factor of the change of the lake area according to claim 1, wherein the single-factor linear analysis method in the step S3 includes the following calculation formula:
Figure FDA0002282316460000011
Figure FDA0002282316460000012
Figure FDA0002282316460000013
Figure FDA0002282316460000014
wherein, the formulas (1) to (3) are linear regression equations, and the formula (4) is a determination coefficient r2B is a constant term of a linear regression equation, representing an intercept; a is the regression coefficient, i.e. slope, of the sample; r is2Also known as R2Referred to as decision coefficients, whose values are the squares of the correlation coefficients, are measures of goodness of fit to the estimated regression equation.
5. The method for identifying key driving factors for lake area change according to claim 1, wherein the Pearson correlation analysis method in step S4 is a method for determining a linear relationship between two variables by using Pearson correlation calculation formula, the correlation coefficient thereof is a measure of the degree of linear correlation, the coefficient is between-1 and +1, the Pearson correlation coefficient represents the linear relationship between the factors, and the calculation formula of the Pearson correlation coefficient is formula (5).
Figure FDA0002282316460000021
6. The method for identifying key driving factors of lake area change according to claim 5, wherein the significance test is performed on the obtained correlation coefficient in step S4 by using T test, and the specific test calculation formula is formula (6):
Figure FDA0002282316460000022
wherein r is a correlation coefficient, t is a statistic value corresponding to the correlation coefficient, and n is the number of samples.
7. The method according to claim 1, wherein the multivariate linear regression analysis method in the step S5 is analyzed by a multivariate linear regression model, wherein the multivariate linear regression model is specifically represented by formula (7):
Figure FDA0002282316460000023
wherein, β0Is a constant term β1，β2，β3Is a partial regression coefficient, i.e. when x increases or decreases by one unit while the other independent variables remain unchanged
Figure FDA0002282316460000024
Average amount of change of (d); x is the number of1,x2,x3The independent variables are respectively corresponding to the driving factors which are obtained in the step S3 and the step S4 and have obvious linear relation with the lake surface area;
Figure FDA0002282316460000025
is a dependent variable; ε is the random error.
8. The method for identifying the key driving factor of the change of the lake area according to claim 7, wherein the fitting degree in the step S5 is specifically determined as follows:
using MATLAB software to program and obtain a sample scatter diagram between dependent variables and independent variables, and using the MATLAB software to program and run to obtain β based on least square method0，β1，β2，β3The confidence interval does not contain the original point, a residual point r and a confidence interval graph are made, a variable stats table is counted, whether the linear correlation relationship is obvious or not is judged, and the obtained linear regression model is used for making the estimation value of the dependent variable of the multiple linear regression analysis
Figure FDA0002282316460000026
And fitting the graph and the graph of the actual value y to check the fitting degree.
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CN111814358A (en) * 2020-08-06 2020-10-23 中国电子科技集团公司第四十六研究所 Multi-factor co-optimization design method for comprehensive performance of microwave composite dielectric substrate
CN112529252A (en) * 2020-11-18 2021-03-19 贵州电网有限责任公司 Small hydropower station forebay water level prediction method and prediction system
CN112529252B (en) * 2020-11-18 2022-05-03 贵州电网有限责任公司 Small hydropower station forebay water level prediction method and prediction system
CN113724792A (en) * 2021-08-01 2021-11-30 北京工业大学 Correlation analysis-based virus diffusion and climate factor relationship analysis method
CN113724792B (en) * 2021-08-01 2024-04-09 北京工业大学 Virus diffusion and climate factor relation analysis method based on correlation analysis
CN115829420A (en) * 2023-02-14 2023-03-21 清华四川能源互联网研究院 Method for judging steady-state conversion threshold of shallow lake

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