CN113256101B - Key driving force analysis method for water storage capacity change of urban and rural lakes - Google Patents
Key driving force analysis method for water storage capacity change of urban and rural lakes Download PDFInfo
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Abstract
The invention discloses a key driving force analysis method for water storage capacity change of urban and rural lakes, which comprises the following steps: carrying out equidistant grid division on a research area, and selecting a plurality of typical sampling points of unit area water storage capacity according to a screening standard; establishing a database for bearing driving force factors, and performing data preprocessing; scanning a lake basin characteristic trend database to form a candidate item set C1; judging the candidate item set C1, generating a frequent item set L1, and drawing a histogram of L1 items; combining each item of data in the frequent item set L1 by adopting an Apriori-Gen algorithm to form a candidate item set C2; judging the candidate item set C2 to form a frequent item set L2; and (4) generating a maximum frequent item set in an iteration mode, and determining key driving force of the change of the water storage capacity of a plurality of lakes. The method utilizes the correlation algorithm to analyze the factors influencing the change of the water storage capacity of the urban and rural lakes one by one, and obtains the key driving force influencing the change of the water storage capacity of the urban and rural lakes through iterative calculation and judgment of the correlation algorithm.
Description
Technical Field
The invention relates to the field of landscape hydrology, in particular to a key driving force analysis method for water storage capacity change of urban and rural lakes.
Background
The lake water body is an important component of urban and rural water resources, bears multiple functions of preventing and draining flood, adjusting climate, improving urban and rural ecological environment, providing leisure and entertainment places and the like, and is closely related to the survival and development of human beings; however, lake resources often face severe problems such as excessive development, ecological function degradation, water environment deterioration and the like. The method is used for researching the water quantity and water ecology of the urban and rural lake ecosystem, analyzing the relative importance of the influence factors of the ecological system based on the evolution process of the service function of the ecological system, and providing a basis for the sustainable utilization and ecological restoration of lake resources.
The hydrological process is closely related and interactive with the formation and evolution of the ecological landscape pattern, and the runoff volume of the lake, the precipitation and evaporation on the surface of the lake, the vegetation environment, the dominant soil type, the climate type, the terrain condition, the hydrological type and the like are considered as main influencing factors of the variation of the urban and rural lakes, but controversy exists about the explanation of the dominant factors of the variation of the water storage capacity of the lakes. The landscape hydrology quantitative analysis methods widely applied at present comprise a pairing basin test method, an ecological hydrology model method and a non-model method, and all the methods have certain limitations. The paired watershed test is only suitable for watersheds with small areas and needs long-term positioning observation; the requirement of the ecological hydrological model on data is high, the quality of the data can influence the simulation quality to a large extent, and the ecological hydrological model for a large watershed is more time-consuming and labor-consuming; the ecological hydrological non-model method mainly utilizes a statistical method and a hydrological graph to carry out rapid evaluation, has low requirement on data, but is difficult to reveal specific hydrological responses and mechanisms inside a drainage basin.
Disclosure of Invention
In order to solve the above mentioned deficiencies in the background art, the present invention provides a method for analyzing key driving force of water storage change in urban and rural lakes, which comprises analyzing the factors affecting the water storage change in urban and rural lakes one by using an association algorithm (Apriori), determining the key driving force affecting the water storage change in urban and rural lakes by iterative calculation of the association algorithm, and realizing quantitative analysis of each influence factor of landscape hydrology, and performing sustainable management, utilization and ecological restoration on the urban and rural lake basins according to the analysis.
The purpose of the invention can be realized by the following technical scheme:
a key driving force analysis method for water storage capacity change of urban and rural lakes comprises the following steps:
step A: carrying out equidistant grid division on a research area, and selecting a plurality of typical sampling points of the water storage capacity per unit area according to a screening standard;
and B: defining 60 attribute data change trends of 10 types of influence factors including hydrology, runoff, climate, evaporation, terrain, soil, vegetation environment, agricultural production, peripheral land type and urbanization degree as items, establishing a database for bearing driving force factors, and performing data preprocessing;
and C: scanning a lake basin characteristic trend database to form a candidate item set C1, wherein each group in the C1 comprises an attribute data change trend influencing lake water storage capacity, and the total number of the groups is 60;
step D: judging the candidate item set C1, comparing whether the support degree of the change trend of each item of attribute data is greater than the minimum support degree, generating a frequent item set L1 by all items greater than the minimum support degree, and drawing various item histograms of the L1;
step E: combining the data in the frequent item set L1 by adopting an Apriori-Gen algorithm to form a candidate item set C2, so that each group in the C2 contains 2 attribute data change trends influencing the lake water storage capacity;
step F: judging the candidate item set C2, comparing whether the support degree of the change trend of each group of attribute data is greater than the minimum support degree, and forming a frequent item set L2 by all items greater than the minimum support degree, wherein all items are candidate sets bearing the key driving force of the change of the lake water storage capacity, and generating an analysis chart describing the correlation of the watershed attributes;
step G: iteratively generating a maximum frequent item set, and determining key driving forces of the water storage capacity changes of several lakes;
step H: and (5) performing further inspection by combining the professional related knowledge of the landscape architecture with a statistical analysis method to verify the accuracy of the result.
Further preferably, step a specifically is: equidistant grid division is carried out on a research area, lakes are named as A, B, C, & gt, N, after research objects and ranges are determined, grids of 100m x 100m are selected and are equidistantly distributed in all parts of the research area, the grids are numbered, the first grid in the northwest corner is taken as a number 1, and the grids are numbered sequentially from left to right and from top to bottom, namely 1, 2, 3, & gt, N; selecting a typical sampling point of the water storage capacity per unit area according to a screening standard; after the sampling points are determined, the sampling points with increased water storage capacity and reduced water storage capacity are distinguished, the areas with increased water storage capacity are marked by circular points, the areas with reduced water storage capacity are marked by square points, and the overall marking on the base map is completed;
the screening standard of a typical sampling point of the water storage capacity per unit area simultaneously meets the following conditions:
(1) The water storage capacity per unit area of the ecological unit in unit time has obvious change. Considering the feasibility of data collection, determining the unit time to be 1 year, and collecting data of nearly ten years for statistical analysis;
(2) The ecological unit has one or more driving force data with obvious change;
(3) The ecological unit sampling points finally appear in groups, and the condition that only one sampling point exists in a certain area is avoided.
Further preferably, step B specifically is: defining each attribute change trend influencing the change of the lake water storage capacity as one item, establishing a database, and performing data preprocessing; taking 60 attribute data in 10 types of influence factors including hydrology, runoff, climate, evaporation, terrain, soil, vegetation environment, agricultural production, peripheral land type and urbanization degree as the driving force alternative factors influencing the change of the water storage capacity of urban and rural lakes, taking each change trend of each attribute, namely an increasing trend or a decreasing trend as one item, importing all related data in all sampling points, establishing a database, namely the database bearing the driving force factors, and then preprocessing the data of the database, wherein the method mainly comprises the following steps: data cleaning, data integration, data transformation and data specification.
Further preferably, step C specifically is: scanning the database to form candidate item sets C1 containing the variation trend of one item of attribute data, wherein each item in the candidate item sets C1 corresponds to one variation trend of one attribute, namely the driving force influencing the variation of the lake water storage capacity.
Further preferably, step D specifically includes: judging the candidate item set C1, comparing whether the support degree of each item of attribute data change trend is greater than the minimum support degree, and generating a frequent item set L by comparing all items greater than the minimum support degree, wherein the value of the minimum support degree determines the generated result, the generation of an effective frequent item set is influenced by an excessively high value, and a meaningless result is caused by an excessively low value, through repeated experiments, the value of the minimum support degree can be defined between 0.5 and 0.6, namely, the trend change of the related attribute data influencing the lake water storage capacity reaches at least 50%, the frequent item set L1 is entered, the Apriori algorithm is realized at an interval of 0.02, a corresponding graph is drawn, the horizontal axis is the value of the minimum support degree, and the vertical axis corresponds to the number of the maximum frequent item set.
Further preferably, step E specifically is: combining various data in the frequent item set L1 by using an Apriori-Gen algorithm to form a candidate item set C2 containing 2 attribute data change trends in each group, wherein an Apriori-Gen function is a combination of a connecting step and a pruning step of the candidate item set, and the connecting process connects the two subsets to generate a potential candidate item set, so that the calculation amount is reduced; in the pruning process, if a certain K-1 item set in the candidate K item sets is infrequent, the K-1 item sets are removed according to properties, the calculation efficiency of the algorithm is also improved, each candidate is formed by combining a pair of frequent K-1 item sets, so that the rest K-2 subsets of the candidate are ensured to be frequent only by an additional candidate pruning step, and in this link, the candidate 2 item sets formed by the research object are a combination of attribute data change trends or driving forces, wherein each group of the candidate 2 item sets comprises 2 items, namely 2 items influence the lake water storage capacity.
Further preferably, step F is specifically: and judging the candidate item set C2, comparing the support degree of the change trend of each group of attribute data with the minimum support degree, forming a frequent 2 item set L2 by all items greater than the minimum support degree, generating an analysis chart for describing the relevance of the attributes of the basin, and analyzing the relevance.
Further preferably, step G specifically is: continuously recombining the frequent item set L2 by using an Apriori-Gen algorithm, and combining each item of data in the frequent item set L2 to form a candidate item set C3 containing 3 item attribute data change trends in each group; judging the candidate item set C3, comparing the support degree of the change trend of each group of attribute data with the minimum support degree, and forming a frequent 3 item set L3 by all items greater than the minimum support degree; the process is repeated until the maximum frequent K item set LK with all items being key driving forces is formed in an iteration mode, and therefore the key driving forces of which lake water storage amount changes at most are determined.
Further preferably, step H is specifically: after key driving force factors influencing the lake water storage capacity of the research region are obtained by running the association algorithm for multiple times, further checking by using landscape and garden professional related knowledge and combining a statistical analysis method, and verifying the accuracy of a result; the remote sensing image is used as a main information source in the testing process, the geographical information system technology and the mathematical statistics method are utilized to analyze the landscape pattern change of the researched region, and the final testing result is consistent with the result generated by the correlation algorithm.
The invention has the beneficial effects that:
the invention utilizes the association algorithm (Apriori) to carry out one-by-one analysis on the factors influencing the variation of the water storage capacity of the urban and rural lakes, obtains the key driving force influencing the variation of the water storage capacity of the urban and rural lakes through iterative calculation and judgment of the association algorithm, realizes quantitative analysis on each influencing factor of landscape hydrology, and carries out sustainable management, utilization and ecological restoration on the urban and rural lake watershed on the basis of the analysis.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic flow chart of a key driving force analysis method for water storage capacity variation of urban and rural lakes according to the present invention;
FIG. 2 is an exemplary illustration of a lake partition according to an embodiment of the present invention;
FIG. 3 is an exemplary diagram of a grid division of a lake research area according to an embodiment of the present invention;
FIG. 4 is an exemplary graph of sample points after screening in accordance with an embodiment of the present invention;
FIG. 5 is a variation graph of the minimum support value of lake A according to an embodiment of the present invention;
FIG. 6 is a bar graph of an embodiment L1 of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "opening," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like are used in an orientation or positional relationship that is merely for convenience in describing and simplifying the description, and do not indicate or imply that the referenced component or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be considered as limiting the present invention.
The invention aims to analyze the factors influencing the change of the water storage capacity of the urban and rural lakes one by utilizing an association algorithm (Apriori), judge and obtain the key driving force influencing the change of the water storage capacity of the urban and rural lakes through iterative calculation of the association algorithm, realize quantitative analysis of each influencing factor of landscape hydrology, and carry out sustainable management, utilization and ecological restoration on the urban and rural lake watershed according to the key driving force.
A key driving force analysis method for the water storage capacity change of urban and rural lakes based on a correlation algorithm utilizes an artificial intelligence correlation algorithm to quantitatively explore the key driving force and the mechanism thereof which affect the back of the water storage capacity change of the urban and rural lakes, and comprises the following steps:
step 1: and carrying out equidistant grid division on the research area, and selecting a plurality of typical sampling points of the water storage capacity per unit area according to the screening standard.
Taking the area of Huanglong of Jiangning Nanjing as an example, the study objects are determined by numbering the larger lakes from A to E. (see fig. 2)
The mesh division operation method comprises the following steps:
and (2) carrying out grid division on the research area, selecting grids of 100m x 100m, equidistantly distributing the grids in each part of the research area, numbering the grids, sequentially numbering from left to right and from top to bottom by taking the first grid in the northwest corner as a number 1, and carrying out 1, 2 and 3. (see fig. 3)
The screening standard of a typical sampling point of the water storage capacity per unit area simultaneously meets the following conditions:
(1) The water storage capacity per unit area of the ecological unit in unit time has obvious change. Considering the feasibility of data collection, determining the unit time to be 1 year, and collecting data of nearly ten years for statistical analysis;
(2) The ecological unit has one or more driving force data with obvious change;
(3) The ecological unit sampling points are finally grouped, and the condition that only one sampling point exists in a certain area is avoided.
After the sampling points are determined, the sampling points with increased water storage capacity and the sampling points with decreased water storage capacity are distinguished, the areas with increased water storage capacity are marked by circular points, the areas with decreased water storage capacity are marked by square points, and the overall marking on the base map is completed. (see fig. 4)
Step 2: defining each attribute change trend influencing the change of the lake water storage capacity as one item, establishing a database and carrying out data preprocessing.
The Huang Longxian area natural geographic environment and social human environment contain various different attributes, including 60 attribute data in 10 types of influence factors including hydrology, runoff, climate, evaporation, terrain, soil, vegetation environment, agricultural production, peripheral land type and urbanization degree, as driving force alternative factors influencing the water storage capacity change of the area. And (3) importing all relevant data in all sampling points by taking each change trend, namely an increasing trend or a decreasing trend, of each attribute as one item, and establishing a database. The database is a database for bearing driving force factors.
When the database is established, two groups are needed to be distinguished, namely a driving force factor causing the water storage capacity of the lake to be increased and a driving force factor causing the water storage capacity of the lake to be reduced.
The factors influencing the change of the water storage capacity of the urban and rural lakes comprise the following 10 types of factors and 60 attributes:
(1) Hydrologic S factors, relevant attributes include: s1, lake water quality, S2 flood season water level, S3 normal water level, S4 dry water level, S5 lake surface area, S6 lake catchment area, S7 surface water use and S8 underground water use;
(2) Runoff J factor, relevant attributes include: j1 surface runoff lake inflow amount, J2 surface runoff lake outflow amount, J3 underground runoff lake inflow amount and J4 underground runoff lake outflow amount;
(3) Climate Q factor, relevant attributes include: the average precipitation per year on the surface of a Q1 lake, the precipitation distribution per year on the surface of a Q2 lake, the average temperature per day of a Q3, the average relative humidity per day of a Q4, the sunshine hours of a Q5, the average wind speed of a Q6 and the solar radiation daily average value of a Q7;
(4) Evaporation Z factor, relevant attributes include: potential evapotranspiration quantity of the Z1 lake surface and actual evapotranspiration quantity of the Z2 lake surface;
(5) Terrain D factors, relevant attributes include: d1 elevation, D2 gradient, D3 slope and D4 undulation degree;
(6) Soil T factor, data types include: the soil type of T1, the soil type distribution of T2, the soil humidity of T3, the soil texture of T4, the soil thickness of T5, the organic matter content of T6 and the soil erosion strength of T7;
(7) Vegetation environment H factors, and the relevant attributes include: h1 vegetation type, H2 vegetation distribution, H3 growth status, H4 vegetation coverage, H5 three-dimensional green volume, H6 vegetation season change, H7 net primary productivity;
(8) Agricultural production N factor, and related attributes comprise: n1 planting scale, N2 planting structure and N3 water-saving engineering;
(9) Peripheral land type Y factor, and the related attributes comprise: y1 arable area, Y2 forest land area, Y3 grassland area, Y4 wetland area, Y5 garden area, Y6 fishpond area, Y7 construction land area, and Y8 unused land area;
(10) The degree of urbanization C factor, and the related attributes comprise: the system comprises a C1 road grade, a C2 road network density, a C3 building density, a C4 building height, a C5 town population density, a C6 agricultural population density, a C7 crowd activity period, a C8 town industrialization rate, a C9 industrial development degree and a C10 rural industrialization rate.
When attribute data are summarized, areas with increased or decreased water amount are separated, and then follow-up analysis of each attribute element is carried out.
The method for preprocessing the collected data comprises the following steps:
data cleaning: and deleting irrelevant data in the original data and the repeated data, and processing missing values, abnormal values and the like. The processing content is geographic information data acquired after the field exploration, and comprises all data in driving force factor reference attributes influencing the water storage capacity of the lake. For example, if the value of precipitation in the lake A in a certain year is obviously higher than the value in the adjacent two years, the abnormal value is regarded as a missing value, and a missing value processing method, namely a plug-in method, is used for processing.
(2) Data integration: data from multiple data sources is consolidated and stored in a consistent data warehouse. Entity identification and attribute redundancy issues need to be considered because real-world entity expressions from multiple data sources differ, potentially resulting in mismatches between data. For example, agr _ area in data source a and agr _ ar in data source B both describe the farmland area in this study, i.e., agr _ area = agr _ ar, and the farmland area data in the two data sources should be merged and stored in the data warehouse.
(3) Data transformation: and carrying out normalization processing on the data, and converting the data into a proper form so as to be suitable for the requirement of an algorithm. Taking an attribute construction method in data transformation as an example, the method needs to construct a new attribute by using an existing attribute set and add the new attribute into the existing attribute set. For example, in the present study, the existing attribute of land utilization type is annual cultivated land area, and in order to more clearly explore the driving force factors affecting the change of lake water storage, a new attribute, namely the change trend of cultivated land area, needs to be constructed, and the attribute corresponds to two items, namely, "increase of cultivated land area" and "decrease of cultivated land area", namely, agr _ area _ INC and agr _ area _ DEC. Wherein, if the number of the cultivated land area in the previous year is more than that in the next year, the increase of the cultivated land area is determined, otherwise, the decrease of the cultivated land area is determined.
(4) Data specification: complex data analysis and mining on large data sets takes a long time, data conventions result in new data sets that are smaller but maintain the integrity of the original data, thereby reducing the impact of invalid, erroneous data on modeling, reducing the cost of storing data, and significantly reducing computation time with small and representative amounts of data. For example, in the present study, when dealing with attributes related to land use types, a merge attribute method is adopted, where the original attributes include "tea tree area", "willow area", "bamboo forest area", and the like, and after stipulation, these attributes are merged into a new attribute "forest area", that is, for _ area.
And step 3: and scanning the database to form a candidate item set C1 containing the variation trend of one item of attribute data in each group. Each item in C1 corresponds to a variation trend of one attribute, namely, a driving force influencing the change of the lake water storage capacity. For example: increased grass area, decreased annual average precipitation, etc.
And 4, step 4: and judging the candidate item set C1, and comparing whether the support degree of the change trend of each item of attribute data is greater than the minimum support degree or not, wherein all items greater than the minimum support degree generate a frequent item set L1.
The value of the minimum support degree determines the result, the generation of an effective frequent item set is influenced by an excessively high value, and a meaningless result is caused by an excessively low value. Through repeated experiments, the research defines the minimum support reference value between 0.5 and 0.6, namely the trend change of the relevant attribute data influencing the lake water storage capacity reaches at least 50 percent, and then the frequent item set L1 is entered. The interval is 0.02, an Apriori algorithm is realized, a corresponding chart is drawn, the horizontal axis is a value of the minimum support degree, and the vertical axis corresponds to the number of the frequent items in the maximum frequent item set. With the increase of the minimum support degree, the number of the frequent items is reduced, namely the key driving force factor is reduced. (see fig. 5)
And drawing a histogram according to the support degree of the change trend of each item of attribute data in the L1. The drawing mode is as follows: the method comprises the steps of distinguishing data of different regions and increasing and decreasing of water storage capacity, drawing bar charts of the different water storage capacity of the different regions respectively, arranging all items in the L1 in a horizontal direction according to the sequence of the support degree from large to small, comparing the support degree of all items of the L1, namely the change trend of all attributes, and primarily judging the possible attributes of the key driving force. In the figure, the lake A is taken as an example, and the key driving force factors are preliminarily considered to comprise three items which are arranged according to the size of the support degree and are rainfall reduction, forest area reduction and surface water reduction in sequence. (see fig. 6)
And 5: and combining the data in the frequent item set L1 by using an Apriori-Gen algorithm to form a candidate 2 item set C2 containing 2 item attribute data change trends in each group.
The Apriori-Gen function is a collective term of a connecting step and a pruning step of the candidate item set, and the connecting process connects the two subsets to generate a potential candidate item set, so that the calculation amount is reduced; in the pruning process, if a certain K-1 item set in the candidate K item sets is infrequent, the K-1 item sets are removed according to the properties, and the calculation efficiency of the algorithm is also improved. Since each candidate is merged by a pair of frequent K-1 sets of entries, only additional candidate pruning steps are needed to ensure that the remaining K-2 subsets of the candidate are frequent.
In this link, the candidate 2 sets formed by the research objects are each set including 2 items, that is, 2 items are the combination of the attribute data change trend, or the driving force, affecting the lake water storage capacity. Taking lake A as an example, the frequent 2 items include items such as [ increase of urbanization rate, decrease of wetland ], [ decrease of forest, decrease of agricultural land ], [ decrease of forest, decrease of precipitation ], [ increase of urbanization rate, decrease of agricultural land ] and the like.
Step 6: and judging the candidate item set C2, comparing the support degree of the change trend of each group of attribute data with the minimum support degree, forming a frequent 2 item set L2 by all items greater than the minimum support degree, generating an analysis chart for describing the relevance of the attributes of the basin, and analyzing the relevance.
The chart is prepared in the following way:
first, data of water storage amount increase and water storage amount decrease are distinguished, and increase graphs and decrease graphs are drawn respectively. Taking the drawing of the water storage capacity increase chart as an example, the table is transversely and sequentially arranged with different value intervals of the association degree, and is longitudinally and sequentially arranged in different regions of research. Wherein the degree of association represents a correlation between the attributes of the watershed.
For example, the township rate increase and the agricultural land area decrease have the highest correlation among the driving force factors of the change of the water storage capacity of the lake A, namely the highest correlation.
And 7: and continuously utilizing the Apriori-Gen algorithm to recombine the frequent item set L2, and combining all data in the frequent item set L2 to form a candidate 3 item set C3 containing 3 item attribute data change trends in each group. And judging the candidate item set C3, and comparing the support degree of the change trend of each group of attribute data with the minimum support degree, wherein all items greater than the minimum support degree form a frequent 3 item set L3. The process is repeated until the maximum frequent K item set LK with all items being key driving forces is formed in an iteration mode, and therefore the key driving forces of which lake water storage amount changes at most are determined.
For example, the key driving factors influencing the reduction of the water storage capacity of the lake A are three through multiple analyses, including the reduction of the annual average precipitation, the reduction of forests and the increase of the urbanization rate, as shown in the following table 1:
TABLE 1 confidence analysis
And 8: and (6) checking the result. After key driving force factors influencing the water storage capacity of the lake in the Huanglong region are obtained by running the association algorithm for multiple times, the accuracy of the result is verified by further testing by combining the professional related knowledge of landscape architecture with a statistical analysis method.
The testing process takes remote sensing images as a main information source, and analyzes the landscape pattern change of Huanglong in 2000-2020 by utilizing a geographic information system technology and a mathematical statistics method. Selecting 6 landscape indexes for research, namely landscape patch Number (NP), landscape Patch Density (PD), landscape Shape Index (LSI), shannon landscape diversity index (SHDI), shannon landscape dominance index (SHEI) and landscape spreading index (CONTAG), analyzing the influence of each factor on lake water storage capacity, screening driving force factors by using a statistical method, and inducing key factors influencing lake water storage capacity change.
The final result is checked to be consistent with the result generated by the correlation algorithm, taking A lake as an example, the annual average precipitation reduction, forest reduction and urbanization rate increase are key driving force factors influencing the reduction of the water storage capacity of the A lake
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are given by way of illustration of the principles of the present invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, and such changes and modifications are within the scope of the invention as claimed.
Claims (9)
1. A key driving force analysis method for water storage capacity change of urban and rural lakes is characterized by comprising the following steps:
step A: carrying out equidistant grid division on a research area, and selecting a plurality of typical sampling points of unit area water storage capacity according to a screening standard;
and B: defining 60 attribute data change trends of 10 types of influence factors including hydrology, runoff, climate, evaporation, terrain, soil, vegetation environment, agricultural production, peripheral land type and urbanization degree as items, establishing a database for bearing driving force factors, and performing data preprocessing;
step C: scanning a lake basin characteristic trend database to form a candidate item set C1, wherein each group in the C1 comprises an attribute data change trend influencing lake water storage capacity, and the total number of the groups is 60;
step D: judging the candidate item set C1, comparing whether the support degree of the change trend of each item of attribute data is greater than the minimum support degree, generating a frequent item set L1 by all items greater than the minimum support degree, and drawing various item histograms of the L1;
step E: combining the data in the frequent item set L1 by adopting an Apriori-Gen algorithm to form a candidate item set C2, so that each group in the C2 contains 2 attribute data change trends influencing the lake water storage capacity;
step F: judging the candidate item set C2, comparing whether the support degree of the change trend of each group of attribute data is greater than the minimum support degree, and forming a frequent item set L2 by all items greater than the minimum support degree, wherein all items are candidate sets bearing the key driving force of the change of the lake water storage capacity, and generating an analysis chart describing the correlation of the watershed attributes;
step G: iteratively generating a maximum frequent item set, and determining key driving force of the change of the water storage capacity of a plurality of lakes;
step H: and (5) performing further inspection by combining the professional related knowledge of the landscape architecture with a statistical analysis method to verify the accuracy of the result.
2. The method for analyzing key driving force of water storage capacity change of urban and rural lakes according to claim 1, wherein the step A is specifically as follows: equidistant grid division is carried out on a research area, lakes are named as A, B, C, & gt, N, after research objects and ranges are determined, grids of 100m x 100m are selected and are equidistantly distributed in all parts of the research area, the grids are numbered, the first grid in the northwest corner is taken as a number 1, and the grids are numbered sequentially from left to right and from top to bottom, namely 1, 2, 3, & gt, N; selecting a typical sampling point of the water storage capacity per unit area according to a screening standard; after the sampling points are determined, the sampling points with increased water storage capacity and reduced water storage capacity are distinguished, the areas with increased water storage capacity are marked by circular points, the areas with reduced water storage capacity are marked by square points, and the overall marking on the base map is completed;
the screening standard of a typical sampling point of the water storage capacity per unit area simultaneously meets the following conditions:
(1) The water storage capacity of the ecological unit in unit time per unit area has obvious change, the feasibility of data collection is considered, the unit time is determined to be 1 year, and data of nearly ten years are collected for statistical analysis;
(2) The ecological unit has one or more driving force data with obvious change;
(3) The ecological unit sampling points are finally grouped, and the condition that only one sampling point exists in a certain area is avoided.
3. The method for analyzing key driving force of water storage capacity change of urban and rural lakes according to claim 1, wherein the step B specifically comprises the following steps: defining each attribute change trend influencing the lake water storage capacity change as one item, establishing a database, and performing data preprocessing; taking 60 attribute data in 10 types of influence factors including hydrology, runoff, climate, evaporation, terrain, soil, vegetation environment, agricultural production, peripheral land type and urbanization degree as the driving force alternative factors influencing the change of the water storage capacity of urban and rural lakes, taking each change trend of each attribute, namely an increasing trend or a decreasing trend as one item, importing all related data in all sampling points, establishing a database, namely the database bearing the driving force factors, and then preprocessing the data of the database, wherein the method mainly comprises the following steps: data cleaning, data integration, data transformation and data specification.
4. The method for analyzing the key driving force of the variation of the water storage capacity of urban and rural lakes according to claim 1, wherein the step C is specifically as follows: scanning the database to form candidate item sets C1 containing the variation trend of one item of attribute data, wherein each item in the candidate item sets C1 corresponds to one variation trend of one attribute, namely the driving force influencing the variation of the lake water storage capacity.
5. The method for analyzing key driving force of water storage capacity change of urban and rural lakes according to claim 1, wherein the step D is specifically as follows: judging the candidate item set C1, comparing whether the support degree of each item of attribute data change trend is greater than the minimum support degree, and generating a frequent item set L by comparing all items greater than the minimum support degree, wherein the value of the minimum support degree determines the generated result, the generation of an effective frequent item set is influenced by an excessively high value, and a meaningless result is caused by an excessively low value, through repeated experiments, the value of the minimum support degree can be defined between 0.5 and 0.6, namely, the trend change of the related attribute data influencing the lake water storage capacity reaches at least 50%, the frequent item set L1 is entered, the Apriori algorithm is realized at an interval of 0.02, a corresponding graph is drawn, the horizontal axis is the value of the minimum support degree, and the vertical axis corresponds to the number of the maximum frequent item set.
6. The method for analyzing key driving force of water storage capacity change of urban and rural lakes according to claim 1, wherein the step E is specifically as follows: combining various data in the frequent item set L1 by using an Apriori-Gen algorithm to form a candidate item set C2 containing 2 attribute data change trends in each group, wherein an Apriori-Gen function is a combination of a connecting step and a pruning step of the candidate item set, and the connecting process connects the two subsets to generate a potential candidate item set, so that the calculation amount is reduced; in the pruning process, if a certain K-1 item set in the candidate K item sets is infrequent, the K-1 item sets are removed according to properties, the calculation efficiency of the algorithm is also improved, each candidate is formed by combining a pair of frequent K-1 item sets, so that the rest K-2 subsets of the candidate are ensured to be frequent only by an additional candidate pruning step, and in this link, the candidate 2 item sets formed by the research object are a combination of attribute data change trends or driving forces, wherein each group of the candidate 2 item sets comprises 2 items, namely 2 items influence the lake water storage capacity.
7. The method for analyzing key driving force of water storage capacity change of urban and rural lakes according to claim 1, wherein the step F is specifically as follows: and judging the candidate item set C2, comparing the support degree of the change trend of each group of attribute data with the minimum support degree, forming a frequent 2 item set L2 by all items greater than the minimum support degree, generating an analysis chart for describing the relevance of the attributes of the basin, and analyzing the relevance.
8. The method for analyzing key driving force of water storage capacity change of urban and rural lakes according to claim 1, wherein the step G is specifically as follows: continuously recombining the frequent item set L2 by using an Apriori-Gen algorithm, and combining each item of data in the frequent item set L2 to form a candidate item set C3 containing 3 item attribute data change trends in each group; judging the candidate item set C3, comparing the support degree of the change trend of each group of attribute data with the minimum support degree, and forming a frequent 3 item set L3 by all items greater than the minimum support degree; the process is repeated until the maximum frequent K item set LK with all items being key driving forces is formed in an iteration mode, and therefore the key driving forces of which lake water storage amount changes at most are determined.
9. The method for analyzing key driving force of water storage capacity change of urban and rural lakes according to claim 1, wherein the step H is specifically as follows: after key driving force factors influencing the lake water storage capacity of the research region are obtained by running the association algorithm for multiple times, further checking by using landscape and garden professional related knowledge and combining a statistical analysis method, and verifying the accuracy of a result; the remote sensing image is used as a main information source in the inspection process, the geographical information system technology and the mathematical statistics method are utilized to analyze the landscape pattern change of the researched area, and the final inspection result is consistent with the result generated by the correlation algorithm.
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