CN112445856B - Sea surface height influence correlation analysis method and device - Google Patents
Sea surface height influence correlation analysis method and device Download PDFInfo
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Abstract
The application discloses a sea surface height influence correlation analysis method, which comprises the steps of receiving base map template data, environmental factors and sea surface height, and calculating and storing average environmental factors and average sea surface height; carrying out correlation analysis and causal relation analysis on the average environmental factors and the average sea surface height to obtain correlation coefficients and information flow from each environmental factor to the sea surface height to obtain main influence factors; the main influencing factors and sea surface heights are subjected to seasonal removal and discretization, and a sea factor transaction database is constructed; calculating normalized mutual information of main influence factors and sea surface height to obtain frequent 1-item sets; according to the minimum support and the minimum confidence of the input, a frequent 2-item set is found; the lifting degree of the frequent 2-item set is calculated to obtain a strong association rule, so that the association rule between the environmental factors influencing the sea surface height change and the sea surface height can be mined, the influence of the influence factors on the sea surface height is quantitatively described, and the sea surface height influence association analysis device is also disclosed.
Description
Technical Field
The invention belongs to the technical field of remote sensing data analysis and application, and particularly relates to a sea surface height influence correlation analysis method and device.
Background
Sea surface altitude is one of the basic marine power environment elements, and is affected by the combined action of the atmosphere and the sea. The global air temperature rises, precipitation increases, glaciers melt, submarine geological structure movements and the like cause sea surface height rise, and the sea surface height rise can cause the aggravation of tropical cyclone and storm surge disasters, the aggravation of flood disasters and the frequent occurrence of disasters and sea waves, simultaneously aggravate the erosion of sea water to coastal areas and island reefs, and threaten the life safety of coastal people and the economic development of coastal cities. The prior researches mainly have the problems of more atmospheric and marine environment elements influencing the sea surface altitude change, and more qualitative description of influence relations between sea surface altitude and various environment factors. The ocean data often records the trend and characteristics of the environmental factors changing along with time in a time sequence form, has characteristics of mass, multidimensional, heterogeneous and the like, and implies a large number of unknown ocean environmental factor association modes. At present, a characteristic value statistical method is mainly adopted for mining a time sequence hidden association mode of marine environment elements, and the method generally uses statistical methods such as principal component analysis, singular value decomposition, model establishment and the like to obtain a typical mode and a characteristic sequence, and further mining the hidden association mode among the environment elements, but the statistical method is low in stability and relatively sensitive to noise.
Disclosure of Invention
In order to solve the problems, the invention provides a sea surface height influence correlation analysis method and device, which can excavate the correlation rule between the environmental factors influencing the sea surface height change and the sea surface height, quantitatively describe the influence of the influence factors on the sea surface height, provide a basis for the prediction analysis of the sea surface height, better avoid various damages caused by the sea surface rise, and provide guarantee and basis for coastal facility construction and coastal engineering of the country.
The invention provides a sea surface height influence correlation analysis method, which comprises the following steps:
receiving base map template data, environmental factors and sea surface height of a preset area input by a user, and calculating and storing average environmental factors and average sea surface height of the preset area;
carrying out correlation analysis and causal relation analysis on the average environmental factors and the average sea surface height to obtain correlation coefficients between each environmental factor and the sea surface height and information flow from each environmental factor to the sea surface height, and obtaining environmental factors with great influence on the sea surface height as main influence factors according to the correlation coefficients and the information flow;
the main influencing factors and sea surface heights are subjected to seasonal removal and discretization, and a sea factor transaction database is constructed;
calculating normalized mutual information of the main influence factors and sea surface height, and determining traversed environmental factors to obtain frequent 1-item sets;
traversing the candidate sets after linking the frequent 1-item sets, calculating the support degree and the confidence degree of each candidate set, and finding the frequent 2-item set according to the minimum support degree and the minimum confidence degree of the input;
and calculating the lifting degree of the frequent 2-item set to obtain a strong association rule, wherein the lifting degree is used for describing the association influence relation between the environmental factor and the sea surface height.
Preferably, in the above sea level height influence correlation analysis method, the receiving the base map template data, the environmental factor and the sea level height of the preset area input by the user, and calculating and storing the average environmental factor and the average sea level height of the preset area includes:
and carrying out data masking on the base map template data, and calculating the average value of the environmental factors of the preset area to obtain a plurality of long-time-sequence marine remote sensing data.
Preferably, in the above sea level height influence correlation analysis method, the performing correlation analysis and causal relation analysis on the average environmental factor and the average sea level height to obtain a correlation coefficient between each environmental factor and the sea level height and an information flow from each environmental factor to the sea level height, and obtaining, as the main influence factors, the environmental factors having a large influence on the sea level height according to the correlation coefficient and the information flow includes:
and judging the influence relation between the environmental factors and the sea surface height, and extracting main influence factors with great influence on the sea surface height by taking a union of correlation analysis and causal relation analysis as a result basis.
Preferably, in the above sea level altitude impact correlation analysis method, the main impact factor and sea level altitude are de-seasonal using a z-score criterion.
Preferably, in the above sea level height influence correlation analysis method, the calculating the normalized mutual information of the main influence factor and the sea level height, and determining the traversed environmental factor, the obtaining the frequent 1-term set includes:
the main influencing factors after the seasonal removal and the discretization of the sea surface height time sequence are completed by using the mean value and the standard deviation, and the discretization comprises the following steps:
discretizing the environmental factor and the sea level height into 5 grades according to the standards of mu+/-0.5 delta and mu+/-delta, wherein the 5 grades are respectively represented by-2, -1,0,1 and 2, and the 5 grades are respectively represented by five states of abnormal elevation, normal state, reduction and abnormal reduction of the environmental factor or the sea level height;
calculating the normalized mutual information value among the discretized main influence factors, and recording and storing sequences of the two main influence factors as frequent 1-item sets when the normalized mutual information value is larger than an average value.
Preferably, in the sea surface altitude impact correlation analysis method, the step of traversing the candidate sets after linking the frequent 1-item sets, calculating the support degree and the confidence degree of each candidate set, and the step of finding the frequent 2-item set according to the input minimum support degree and minimum confidence degree includes:
calculating the support degree of each frequent 1-item set, and recording the support degree as a candidate set of the frequent 2-item set when the support degree is larger than the minimum support degree of 0.1;
and calculating the confidence coefficient, and recording the confidence coefficient of more than 60 percent as the mined frequent 2-item set.
The invention provides a sea surface height influence correlation analysis device, which comprises:
the calculating and storing component is used for receiving base map template data, environment factors and sea surface heights of a preset area input by a user, and calculating and storing average environment factors and average sea surface heights of the preset area;
the analysis component is used for carrying out correlation analysis and causal relation analysis on the average environmental factors and the average sea surface height to obtain correlation coefficients between each environmental factor and the sea surface height and information flow from each environmental factor to the sea surface height, and obtaining environmental factors with great influence on the sea surface height as main influence factors according to the correlation coefficients and the information flow;
the database construction component is used for carrying out seasonal removal and discretization on the main influence factors and sea surface heights to construct a sea factor transaction database;
the frequent 1-item set determining component is used for calculating the normalized mutual information of the main influence factors and sea surface height, determining the traversed environmental factors and obtaining frequent 1-item sets;
a frequent 2-item set determining unit, configured to traverse candidate sets after linking the frequent 1-item sets, calculate a support degree and a confidence degree of each of the candidate sets, and find a frequent 2-item set according to an input minimum support degree and a minimum confidence degree;
and the strong association rule determining component is used for calculating the lifting degree of the frequent 2-item set to obtain a strong association rule, and the lifting degree is used for describing the association influence relation between the environmental factor and the sea surface height.
Preferably, in the sea level height influence correlation analysis device, the calculating and storing unit is specifically configured to perform data masking on the base map template data, and calculate an average value of environmental factors of the preset area, so as to obtain a plurality of long-time-sequence marine remote sensing data.
Preferably, in the above sea surface height influence correlation analysis device, the analysis unit is specifically configured to determine an influence relationship between the environmental factor and the sea surface height, and extract a main influence factor having a large influence on the sea surface height by using a union of correlation analysis and causal relationship analysis as a result basis.
Preferably, in the above sea level altitude impact correlation analysis apparatus, the database construction means is specifically configured to use a z-score criterion to de-season the main impact factor and the sea level altitude.
As can be seen from the above description, the sea surface height influence correlation analysis method provided by the invention comprises the steps of receiving the base map template data, the environmental factors and the sea surface height of a preset area input by a user, and calculating and storing the average environmental factors and the average sea surface height of the preset area; carrying out correlation analysis and causal relation analysis on the average environmental factors and the average sea surface height to obtain correlation coefficients between each environmental factor and the sea surface height and information flow from each environmental factor to the sea surface height, and obtaining environmental factors with great influence on the sea surface height as main influence factors according to the correlation coefficients and the information flow; the main influencing factors and sea surface heights are subjected to seasonal removal and discretization, and a sea factor transaction database is constructed; calculating normalized mutual information of the main influence factors and sea surface height, and determining traversed environmental factors to obtain frequent 1-item sets; traversing the candidate sets after linking the frequent 1-item sets, calculating the support degree and the confidence degree of each candidate set, and finding the frequent 2-item set according to the minimum support degree and the minimum confidence degree of the input; and calculating the lifting degree of the frequent 2-item set to obtain a strong association rule, wherein the lifting degree is used for describing the association relation between the environmental factors and the sea surface height, so that the association rule between the environmental factors influencing the change of the sea surface height and the sea surface height can be mined, the influence of the influence factors on the sea surface height is quantitatively described, a foundation is provided for the predictive analysis of the sea surface height, various damages caused by the rising of the sea surface are better avoided, and the guarantee and the basis are provided for coastal facility construction and coastal engineering of the country. The device provided by the invention has the same advantages as the method.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an embodiment of a sea surface altitude impact correlation analysis method provided by the present invention;
FIG. 2 is a schematic diagram of a hysteresis correlation function of sea level height and a portion of environmental factors;
FIG. 3 is a schematic diagram of a causal analysis result of sea surface altitude and marine environmental factors;
FIG. 4 is a flow chart of a conventional Apriori algorithm;
FIG. 5 is a graph showing the relationship between the number of frequent 2 candidate sets and the minimum support;
FIG. 6 is a flow chart of a sea surface altitude impact correlation analysis method provided by the present application;
fig. 7 is a schematic diagram of an embodiment of a sea surface height effect correlation analysis device provided by the invention.
Detailed Description
The invention provides a sea surface height influence correlation analysis method and device, which can excavate the correlation rule between the environmental factors influencing the sea surface height change and the sea surface height, quantitatively describe the influence of the influence factors on the sea surface height, provide a foundation for the prediction analysis of the sea surface height, better avoid various damages caused by the sea surface rise, and provide guarantee and basis for coastal facility construction and coastal engineering of the country.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
An embodiment of a sea surface height effect correlation analysis method provided by the invention is shown in fig. 1, and fig. 1 is a schematic diagram of an embodiment of a sea surface height effect correlation analysis method provided by the invention, and the method comprises the following steps:
s1: receiving base map template data, environmental factors and sea surface heights of a preset area input by a user, and calculating and storing average environmental factors and average sea surface heights of the preset area;
specifically, the data mask can be performed on the base map template data, and an average value of environmental factors of a preset area is calculated to obtain a plurality of long-time-sequence marine remote sensing data, which comprises the following sub-steps:
1.1, cutting the environmental factors and the sea surface height data according to a base map template of a research area, and setting a land value as NAN;
1.2, calculating the regional average time sequence of each environmental factor and sea surface height by using a regional average method, wherein the NAN is not calculated.
i＝1,2,...,nlon
j＝1,2,...,nlat
t＝1,2,...,L
Wherein X (i, j, t) represents the value of the environmental element X at the point (i, j) at the moment t, L is the time length of the data, nlon and nlat are the length and width pixel points of the research area, and N is the number of the effective values of the data.
S2: carrying out correlation analysis and causal relation analysis on the average environmental factors and the average sea surface height to obtain correlation coefficients between each environmental factor and the sea surface height and information flow from each environmental factor to the sea surface height, and obtaining environmental factors with great influence on the sea surface height as main influence factors according to the correlation coefficients and the information flow;
it should be noted that, the correlation between two variables is an influence relationship without directionality, which can be said to be that the variable a lags the variable B, and the variable B leads the variable a, which describes that the A, B variable has a certain relation in the development and change process. Specifically, to reduce the number of traversals of the Apriori algorithm during operation, before association rule mining is performed, the influence relationship between the environmental factor and the sea surface height can be judged, and the main influence factor with great influence on the sea surface height can be extracted by taking the union of correlation analysis and causal relationship analysis as a result basis, which comprises the following substeps:
2.1, respectively calculating the hysteresis correlation functions of the sea surface height and each environmental factor, wherein the hysteresis correlation functions of the sea surface height and each environmental factor are shown in fig. 2 and table 1, fig. 2 is a schematic diagram of the hysteresis correlation functions of the sea surface height and a part of environmental factors, and table 1 is a correlation analysis result table of the sea surface height and each environmental factor, and when the hysteresis correlation coefficient is larger than 0.5, the sea surface height and the environmental factors are regarded as having a remarkable correlation relationship.
TABLE 1
Wherein X represents other environmental factors and Y represents the Pacific average sea level sequence. When l <0, X lags the average sea level of the Pacific, and when l >0, X lags the average sea level of the Pacific.
2.2, the information flow is a physical concept, has strict physical meaning and calculation formula, and has directionality, so that the information flow between two events can be used for measuring the causal relationship between dynamic events. The invention calculates the information flow from the sea surface height to each environmental factor respectively, as shown in fig. 3, fig. 3 is a schematic diagram of the analysis result of the causal relation between the sea surface height and the sea environmental factors, and the data used is satellite remote sensing data, wherein sla is sea surface height, pr is precipitation, ws is wind speed, sss is salinity, sst is sea surface temperature, and ice is the coverage rate of the bipolar sea ice. When |T 2→1 I+.0, the change in sea level is considered to be affected by some environmental factor.
Wherein 1 represents sea surface altitude sequence, 2 represents a certain environmental factor sequence, C is covariance, C 1,d1 X represents 1 And (3) with
Covariance of (2)
S3: the main influencing factors and sea surface heights are decompened and discretized, and a sea factor transaction database is constructed;
specifically, referring to fig. 4, fig. 4 is a flow chart of a conventional Apriori algorithm, and the data operated on by the algorithm is discrete data, so that continuous marine remote sensing data needs to be discretized. The main influencing factors and sea level altitude may be de-seasonal using the z-score criterion, and in particular may comprise the sub-steps of:
3.1, using the z-score criterion to decompensate the data, eliminating the change of ocean environment elements caused by solar motion. Wherein i represents year, j represents day, X i,j Raw data representing the j th year, X 'in a long time series' i,j Represents the corresponding average daily distance value,
and delta j The distribution represents the mean and standard deviation of the time series of composition on day j of the year i.
3.2, using the mean and standard deviation to complete the discretization of the time series of the main influencing factors and the sea surface heights after the de-seasonal operation, wherein the discretization of the environment factors and the sea surface heights into 5 grades according to the standards of mu+/-0.5 delta and mu+/-delta can be included, and the grades of-2 and-1,0,1,2,5 are sequentially used for respectively representing five states of abnormal elevation, normal state, reduction and abnormal reduction of the environment factors or the sea surface heights; and calculating normalized mutual information values among the discretized main influence factors, and recording and storing sequences of the two main influence factors as frequent 1-item sets when the normalized mutual information values are larger than the average value. The data after the seasonal removal is discretized, and a transaction data table is shown in table 2, and table 2 is a marine environment element transaction data table.
TABLE 2
| Time | SLA | SST | SSS | ICEC | ... |
| 1 | 0 | 1 | 0 | -1 | |
| 2 | 1 | 2 | -1 | -1 | |
| 3 | 2 | 2 | -2 | -2 | |
| 4 | 2 | 2 | -2 | -2 | |
| 5 | 2 | 0 | -1 | -1 | |
| ... | |||||
| T | -1 | 1 | 0 | 1 |
The method comprises the following steps:
wherein X is a time sequence after the distance is flattened, mu and delta are the mean value and standard deviation of X respectively, X' is a discretized marine environmental factor sequence, thus, the marine environmental factors are discretized into 5 grades, which are sequentially-2, -1,0,1,2, and respectively represent five states of abnormal rise, normal state, fall and abnormal fall of a marine environmental factor. Similar to the discretization of marine environmental factors, the time series of the Nino index is also classified into five classes according to the mean and standard deviation, and sequentially represents the strong, weak, normal, weak and strong lanina events.
S4: calculating normalization mutual information of main influence factors and sea surface heights, and determining traversed environmental factors to obtain frequent 1-item sets;
specifically, there is no correlation between environmental factors, in order to reduce the number of times of traversing the transaction data table, normalized mutual information MI between discretized environmental factors is calculated, and when the value of the normalized mutual information MI is greater than the average MI, two sequences of X and Y are recorded and stored as frequent 1 item sets:
wherein H (X) and H (Y) are the entropy of X, Y respectively:
i (X, Y) is the mutual information of X, Y:
s5: traversing the candidate sets after linking the frequent 1-item sets, calculating the support degree and the confidence degree of each candidate set, and finding the frequent 2-item set according to the minimum support degree and the minimum confidence degree of the input;
specifically, the support degree of each frequent 1-item set can be calculated, and when the support degree is greater than the minimum support degree of 0.1, the support degree is recorded as a candidate set of the frequent 2-item set; and calculating the confidence coefficient, and recording the confidence coefficient of more than 60 percent as the mined frequent 2-item set. In a specific operation, traversing the X, Y two sequences in the S4, obtaining frequent 2-item set candidate sets according to the minimum support degree, namely calculating the support degree of each frequent item set, and recording the frequent 2 item set candidate sets only when the support degree is greater than the minimum support degree of 0.1, as shown in fig. 5, wherein fig. 5 is a schematic diagram of a relation between the number of the frequent 2 item set candidate sets and the minimum support degree:
where n is the number of times and T is the length of time. Further, calculating the confidence coefficient, and considering the records with the confidence coefficient higher than 60% as the mined frequent 2 item sets:
s6: and calculating the lifting degree of the frequent 2-item set to obtain a strong association rule, wherein the lifting degree is used for describing the association influence relation between the environmental factor and the sea surface height.
The degree of elevation can quantitatively characterize the degree of influence of the rule, and for an association rule the degree of elevation indicates that event A will occur several times the occurrence of event B, where the degree of elevation should be greater than 1, and the greater the degree of elevation, the greater the influence of the environmental factor on sea surface elevation, the more the change in X will be the change in lift,
the results are shown in table 3, table 3 is an association rule mining results table,
it should be noted that the Apriori algorithm may be used to mine the association relationship, and the association relationship is a directional relationship, which describes how the variable a changes and then causes the variable B to change. Examples: icec+2 to sla-2, which is a correlation rule, indicates that when the sea ice coverage area increases abnormally (a value greater than the average +1 standard deviation of the past year), the sea surface height decreases abnormally (a value less than the average-1 standard deviation of the past year). In summary, as shown in fig. 6, fig. 6 is a flowchart of a method for analyzing sea surface height influence correlation provided in the present application, where the method for analyzing sea surface height influence correlation based on Apriori algorithm can quantitatively provide a mode of influencing sea surface height change by environmental factors, compared with a conventional method for analyzing sea surface height influence correlation.
As can be seen from the above description, in the embodiment of the sea surface height influence correlation analysis method provided by the present invention, since the method includes receiving the base map template data, the environmental factor and the sea surface height of the preset area input by the user, calculating and storing the average environmental factor and the average sea surface height of the preset area; carrying out correlation analysis and causal relation analysis on the average environmental factors and the average sea surface height to obtain correlation coefficients between each environmental factor and the sea surface height and information flow from each environmental factor to the sea surface height, and obtaining environmental factors with great influence on the sea surface height as main influence factors according to the correlation coefficients and the information flow; the main influencing factors and sea surface heights are decompened and discretized, and a sea factor transaction database is constructed; calculating normalization mutual information of main influence factors and sea surface heights, and determining traversed environmental factors to obtain frequent 1-item sets; traversing the candidate sets after linking the frequent 1-item sets, calculating the support degree and the confidence degree of each candidate set, and finding the frequent 2-item set according to the minimum support degree and the minimum confidence degree of the input; the lifting degree of the frequent 2-item set is calculated to obtain a strong association rule, and the lifting degree is used for describing the association influence relation between the environmental factors and the sea surface height, so that the association rule between the environmental factors influencing the change of the sea surface height and the sea surface height can be mined, the influence of the influence factors on the sea surface height can be quantitatively described, a foundation is provided for the prediction analysis of the sea surface height, various hazards caused by the rising of the sea surface are better avoided, and the guarantee and the basis are provided for coastal facility construction and coastal engineering of the country.
Fig. 7 is a schematic diagram of an embodiment of a sea surface height effect correlation analysis device provided by the present invention, where the embodiment of the sea surface height effect correlation analysis device provided by the present invention includes:
the calculating and storing unit 701 is configured to receive the base map template data, the environmental factors and the sea level height of the preset area input by the user, calculate and store the average environmental factors and the average sea level height of the preset area, specifically, perform data masking on the base map template data and calculate the average value of the environmental factors of the preset area to obtain a plurality of long-time-sequence marine remote sensing data, specifically, the calculating and storing unit may be specifically configured to perform data masking on the base map template data and calculate the average value of the environmental factors of the preset area to obtain a plurality of long-time-sequence marine remote sensing data;
the analysis component 702 is configured to perform correlation analysis and causal relation analysis on the average environmental factor and the average sea level height to obtain a correlation coefficient between each environmental factor and the sea level height and an information flow from each environmental factor to the sea level height, obtain an environmental factor with a large influence on the sea level height as a main influence factor according to the correlation coefficient and the size of the information flow, and it is to be noted that the correlation between two variables is an influence relation without directivity, so to speak of a change of a variable a with a lag variable B, so to speak of a change of a variable B with a lead variable a, which describes a certain relation between the direction and the size of a A, B variable in terms of development change, specifically, in order to reduce the traversal times of the Apriori algorithm when running, before performing association rule mining, determine the influence relation between the environmental factor and the sea level height, extract the main influence factor with a large influence on the sea level height by the union of correlation analysis and causal relation analysis as a result basis, and in addition, the analysis component can be used for determining the influence relation between the environmental factor and the sea level height by the correlation relation and the main influence factor with the causal relation analysis as a result of the major influence on the sea level;
a database construction unit 703, configured to de-season the main impact factor and the sea level, and discretize, to construct a sea factor transaction database, where the data operated by the Apriori algorithm is discrete data, so that continuous sea remote sensing data needs to be discretized, and the main impact factor and the sea level may be de-seasoned using a z-score criterion, and in particular, the database construction unit may be specifically configured to de-season the main impact factor and the sea level using a z-score criterion;
a frequent 1-item set determining unit 704, configured to calculate normalized mutual information of a main influence factor and sea surface altitude, determine traversed environmental factors, and obtain a frequent 1-item set, specifically, the environmental factors do not necessarily have relevance, and in order to reduce the number of times of traversing the transaction data table, calculate normalized mutual information MI between discretized environmental factors, and record and store two sequences of X and Y as the frequent 1-item set when the value of the normalized mutual information MI is greater than an average MI;
a frequent 2-item set determining part 705, configured to traverse the candidate sets after linking the frequent 1-item sets, calculate a support degree and a confidence degree of each candidate set, find the frequent 2-item set according to the input minimum support degree and the minimum confidence degree, specifically, may calculate a support degree of each frequent 1-item set, and record that the frequent 1-item set is a candidate set of the frequent 2-item set when the support degree is greater than the minimum support degree of 0.1; calculating confidence coefficient, and recording the confidence coefficient which is more than 60% as a mined frequent 2-item set;
the strong association rule determining unit 706 is configured to calculate a degree of elevation of the frequent 2-term set, to obtain a strong association rule, where the degree of elevation is used to describe an association influence relationship between the environmental factor and the sea surface altitude, the degree of elevation can quantitatively describe an influence degree of the rule, and for an association rule, the degree of elevation indicates that occurrence of the event a will raise occurrence of the event B several times, where the degree of elevation should be greater than 1, and the greater the degree of elevation, the greater the influence of the environmental factor on the sea surface altitude.
The embodiment of the sea surface height influence correlation analysis device can mine the correlation rule between the environmental factors influencing the sea surface height change and the sea surface height, quantitatively describe the influence of the influence factors on the sea surface height, provide a foundation for the prediction analysis of the sea surface height, better avoid various damages caused by the sea surface rise, and provide guarantee and basis for coastal facility construction and coastal engineering of the country.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method of sea surface altitude impact correlation analysis, comprising:
receiving base map template data, environmental factors and sea surface height of a preset area input by a user, and calculating and storing average environmental factors and average sea surface height of the preset area;
carrying out correlation analysis and causal relation analysis on the average environmental factors and the average sea surface height to obtain correlation coefficients between each environmental factor and the sea surface height and information flow from each environmental factor to the sea surface height, and obtaining environmental factors with great influence on the sea surface height as main influence factors according to the correlation coefficients and the information flow;
the main influencing factors and sea surface heights are subjected to seasonal removal and discretization, and a sea factor transaction database is constructed;
calculating normalized mutual information of the main influence factors and sea surface height, and determining traversed environmental factors to obtain frequent 1-item sets;
traversing the candidate sets after linking the frequent 1-item sets, calculating the support degree and the confidence degree of each candidate set, and finding the frequent 2-item set according to the minimum support degree and the minimum confidence degree of the input;
calculating the lifting degree of the frequent 2-item set to obtain a strong association rule, wherein the lifting degree is used for describing the association influence relation between the environmental factors and the sea surface height;
carrying out correlation analysis and causal relation analysis on the average environmental factors and the average sea surface height to obtain correlation coefficients between each environmental factor and the sea surface height and information flow from each environmental factor to the sea surface height, wherein the method comprises the following steps:
respectively calculating a hysteresis correlation function of each environmental factor and the sea surface height, and determining the correlation coefficient according to the hysteresis correlation function;
respectively calculating Liang-Kleeman information flows of each environmental factor and the sea surface height, and determining the causal relationship between the factors and the sea surface height according to the information flows;
the main influencing factors and sea surface heights are subjected to seasonal removal and discretization, and a sea factor transaction database is constructed, wherein the method comprises the following steps of:
and (3) performing out-of-season on the main influence factors and the sea surface heights by using a z-score criterion, and performing discretization on the time series of the out-of-season main influence factors and the sea surface heights by using a mean value and a standard deviation to obtain the transaction database.
2. The sea level height effect correlation analysis method according to claim 1, wherein the receiving the base map template data, the environmental factor and the sea level height of the preset area input by the user, calculating and storing the average environmental factor and the average sea level height of the preset area comprises:
and carrying out data masking on the base map template data, and calculating the average value of the environmental factors of the preset area to obtain a plurality of long-time-sequence marine remote sensing data.
3. The sea level height influence correlation analysis method according to claim 1, wherein the performing correlation analysis and causal relation analysis on the average environmental factor and the average sea level height to obtain a correlation coefficient between each environmental factor and the sea level height and an information flow from each environmental factor to the sea level height, and obtaining the environmental factor with a large influence on the sea level height as a main influence factor according to the correlation coefficient and the information flow comprises:
and judging the influence relation between the environmental factors and the sea surface height, and extracting main influence factors with great influence on the sea surface height by taking a union of correlation analysis and causal relation analysis as a result basis.
4. The method of claim 1, wherein calculating normalized mutual information of the primary impact factors and sea level heights, determining traversed environmental factors, and obtaining frequent 1-term sets comprises:
the main influencing factors after the seasonal removal and the discretization of the sea surface height time sequence are completed by using the mean value and the standard deviation, and the discretization comprises the following steps:
discretizing the environmental factor and the sea level height into 5 grades according to the standards of mu+/-0.5 delta and mu+/-delta, wherein the 5 grades are respectively represented by-2, -1,0,1 and 2, and the 5 grades are respectively represented by five states of abnormal elevation, normal state, reduction and abnormal reduction of the environmental factor or the sea level height;
calculating the normalized mutual information value among the discretized main influence factors, and recording and storing sequences of the two main influence factors as frequent 1-item sets when the normalized mutual information value is larger than an average value.
5. The sea level altitude impact correlation analysis method of claim 1, wherein the traversing the candidate sets after linking the frequent 1-item sets, calculating a support and a confidence level for each of the candidate sets, and finding a frequent 2-item set based on the input minimum support and minimum confidence level comprises:
calculating the support degree of each frequent 1-item set, and recording the support degree as a candidate set of the frequent 2-item set when the support degree is larger than the minimum support degree of 0.1;
and calculating the confidence coefficient, and recording the confidence coefficient of more than 60 percent as the mined frequent 2-item set.
6. A sea surface altitude impact correlation analysis apparatus, comprising:
the calculating and storing component is used for receiving base map template data, environment factors and sea surface heights of a preset area input by a user, and calculating and storing average environment factors and average sea surface heights of the preset area;
the analysis component is used for carrying out correlation analysis and causal relation analysis on the average environmental factors and the average sea surface height to obtain correlation coefficients between each environmental factor and the sea surface height and information flow from each environmental factor to the sea surface height, and obtaining environmental factors with great influence on the sea surface height as main influence factors according to the correlation coefficients and the information flow;
the database construction component is used for carrying out seasonal removal and discretization on the main influence factors and sea surface heights to construct a sea factor transaction database;
the frequent 1-item set determining component is used for calculating the normalized mutual information of the main influence factors and sea surface height, determining the traversed environmental factors and obtaining frequent 1-item sets;
a frequent 2-item set determining unit, configured to traverse candidate sets after linking the frequent 1-item sets, calculate a support degree and a confidence degree of each of the candidate sets, and find a frequent 2-item set according to an input minimum support degree and a minimum confidence degree;
the strong association rule determining component is used for calculating the lifting degree of the frequent 2-item set to obtain a strong association rule, and the lifting degree is used for describing the association influence relation between the environmental factors and the sea surface height;
the analysis component is specifically used for respectively calculating a hysteresis correlation function of each environmental factor and the sea surface height, and determining the correlation coefficient according to the hysteresis correlation function;
respectively calculating Liang-Kleeman information flows of each environmental factor and the sea surface height, and determining the causal relationship between the factors and the sea surface height according to the information flows;
according to the correlation coefficient and the information flow, obtaining an environmental factor with great influence on sea surface height as a main influence factor;
the database construction component is specifically configured to use a z-score criterion to de-season the main influencing factor and the sea surface altitude, and use a mean value and a standard deviation to complete discretization of a time sequence of the de-seasoned main influencing factor and the sea surface altitude, so as to obtain the transaction database.
7. The sea level altitude impact correlation analysis device according to claim 6, wherein the calculating and storing unit is specifically configured to perform data masking on the base map template data, and calculate an average value of environmental factors of the preset area to obtain a plurality of long-time-series marine remote sensing data.
8. The sea level height influence correlation analysis device according to claim 6, wherein the analysis means is specifically configured to determine an influence relationship between the environmental factor and the sea level height, and extract a main influence factor having a large influence on the sea level height by using a union of correlation analysis and causal relationship analysis as a result basis.
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