CN117094464B - Lake region water ecological safety evaluation method - Google Patents
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Abstract
The invention discloses a lake region water ecological safety evaluation method, which takes the annual average water storage capacity, the maximum annual average water storage capacity and the minimum annual average water storage capacity of a lake as core evaluation factors, selects quantifiable indexes of pressure, state and response factors in natural, social and economic factors as evaluation factors, and analyzes the evaluation factors based on a fuzzy system to obtain an evaluation vector; in the fuzzy evaluation equation, the evaluation method comprises the steps of establishing an original data matrix, carrying out standardized processing on the original data, establishing a fuzzy similar matrix, setting a confidence level and calculating the weight of each item of data; and finally, obtaining the comprehensive evaluation vector through calculation. In the process, remote sensing image data are used for obtaining the water surface area of the lake, then the water storage capacity of the lake is calculated through the water surface area of the lake, and then the relationship between the water surface area/water storage capacity and the water level is fitted so as to calculate the annual average water storage capacity.
Description
Technical Field
The invention relates to the technical field of water data management. In particular to a lake region water ecological safety evaluation method.
Background
The lake areas in various places in China are used as buffer areas in the middle of various river sections, and have various ecological service functions of regulating and accumulating river runoff, resisting flood, purifying environment, maintaining biodiversity and the like. However, with the increasing global climate change and human activity, the health of the lake hydrologic system is severely compromised. The problem of ecological safety of water in a lake region is not only related to ecological environmental protection, but also directly affects the economic development and quality of life of human society. The water ecological safety evaluation can systematically evaluate the water resource condition of the lake region, provide scientific decision basis for policy makers, and has important practical significance for water ecological restoration and management of the lake region.
At present, the lake region water ecological safety evaluation is mainly based on a series of evaluation indexes, including various fields related to economy, society, nature and the like. However, these traditional evaluation methods often ignore the dynamic and spatial heterogeneity of the lake-region hydrologic system and its complex interactions with climate change, human activity. In addition, due to factors such as difficulty in data acquisition, limitation of method application and the like, the conventional water ecological safety evaluation method has a certain limitation in practical application. Therefore, development of a comprehensive, scientific and effective lake water ecological safety evaluation method is urgently needed to accurately evaluate the water ecological health condition of the lake, discover and early warn ecological risks in time, and guide water resource management and ecological restoration work of the lake.
According to the disclosed technical scheme, the technical scheme with the bulletin number of CN109598439B provides a method for improving the water environment of a shallow lake by utilizing wind speed, wherein a lake area is divided into a plurality of management areas, and different management scheme adjustment is carried out on each management area according to the actually measured wind speed and a simulation model; the technical proposal with the publication number of AU2021100946A4 provides a lake eutrophication water environment evaluation method, fully considers the possibility that index layer elements directly walk into a target layer in abrupt water, and comprehensively evaluates the water quality of the lake in multiple aspects; the technical proposal with publication number of JP2002316141A proposes a water work treatment operation control center and a network system, which arranges the flow and working parameters of the lake region work of a system based on dynamic strategies by arranging a plurality of sensors of various types for a plurality of regions of the lake region.
The technical schemes all provide a plurality of short-term evaluation methods and quick response methods for lakes, but the comprehensive evaluation methods of lakes under the long-period system appearance, especially the evaluation methods suitable for various types of lakes are needed, and the current methods still need to be perfected.
The foregoing discussion of the background art is intended to facilitate an understanding of the present invention only. This discussion is not an admission or admission that any of the material referred to was common general knowledge.
Disclosure of Invention
The invention aims to provide a lake region water ecological safety evaluation method, which takes the annual average water storage capacity, the maximum annual average water storage capacity and the minimum annual average water storage capacity of a lake as core evaluation factors, selects quantifiable indexes of pressure, state and response factors in natural, social and economic elements as evaluation factors, and analyzes the evaluation factors based on a fuzzy system to obtain an evaluation vector; in the fuzzy evaluation equation, the evaluation method comprises the steps of establishing an original data matrix, carrying out standardized processing on the original data, establishing a fuzzy similar matrix, setting a confidence level and calculating the weight of each item of data; and finally, obtaining the comprehensive evaluation vector through calculation. In the process, remote sensing image data are used for obtaining the water surface area of the lake, then the water storage capacity of the lake is calculated through the water surface area of the lake, and then the relationship between the water surface area/water storage capacity and the water level is fitted so as to calculate the annual average water storage capacity.
The invention adopts the following technical scheme: a lake region water ecological safety evaluation method, comprising the following steps:
s100: the annual average water storage capacity of the target lake area in a plurality of statistical years is counted, and the maximum annual average water storage capacity and the minimum annual average water storage capacity in the middle are counted;
s200: respectively selecting one or more quantifiable indexes of pressure factors and response factors in a natural domain in a target lake region and one or more quantifiable indexes of pressure factors, state factors and response factors in a social domain and an economic domain as evaluation factors, and acquiring specific data of the evaluation factors in a plurality of statistical years;
wherein, the state factor of the target lake area in the natural domain takes the annual average water storage capacity, the maximum annual average water storage capacity and the minimum annual average water storage capacity as the main hardness factors to be selected;
s300: obtaining a first evaluation vector based on the evaluation factors selected in the step S200 of the fuzzy system analysis;
wherein, in step S100, the following sub-steps are included:
s110: acquiring satellite remote sensing image data of a target lake region in a plurality of statistical years, so as to calculate the water surface area of the target lake region;
s120: solving the corresponding water storage amount of the target lake region in a plurality of time phases based on the lake water surface area data of the target lake region in the plurality of time phases in a plurality of years;
s130: fitting and calculating the calculation relation between the water surface area/water storage capacity and the water level of the target lake area, calculating the annual average water storage capacity in a plurality of statistical years, sequencing the annual average water storage capacity in the plurality of statistical years, and counting the maximum annual average water storage capacity and the minimum annual average water storage capacity;
preferably, in step S300, the analysis of the selected evaluation factors based on the fuzzy system comprises the following sub-steps:
s310: establishing an original data matrix for the historical data of the evaluation factors, namely, establishing an N-by-M-scale original data matrix A (x ik )，i＝1,2,……,N,k＝1,2,……,M，x ij I.e., the data value of the j-th evaluation factor in the i-th year;
s320: carrying out standardization processing on the original data matrix; the adopted standardized processing mathematical method is a minimum-maximum standardized method; that is, the annual statistical data X of the historical statistical data of N statistical years is set as X respectively 1 ,x 2 ，…，x n A total of N data; mapping original data x to interval 0,1 by normalization]Between them;
the specific calculation mode is as follows:
for data representing higher water ecological safety degree with larger value of x, there are:
x′ i ＝(x i -min(x i ))/(max(x i )-min(x i ) Formula (1);
for data representing higher water ecological safety degree with smaller value of x, there are:
x′ i ＝(max(x i )-x i )/(max(x i )-min(x i ) Formula (2);
in the formula (1) and the formula (2), x' i I.e. normalized data, x i I.e. evaluating the original data of factor X in the ith year of N statistical years; max (x) i ) I.e. N x i The maximum value, min (x i ) I.e. N x i The minimum of the values;
finally obtaining a fuzzy matrix X '= (X' ik ) N×M ；
S330: establishing a fuzzy similarity matrix R; i.e. blurring matrix (x' ik ) N×M Data x 'of the i and j th years' ik And x' jk Substituting the following calculation formula:
thereby obtaining a fuzzy similar matrix R= (R) ij ) N×N
In the formula (3), r ij The similarity coefficient of the integral data samples in the i year and the j year is the similarity coefficient of the integral data samples in the i year and the j year, and the similarity coefficient represents the similarity degree of the integral data samples in the i year and the j year; wherein,
s340: setting a confidence level and calculating the weight of each item of data; taking the largest matrix element of each row in the fuzzy similarity matrix R as a confidence level lambda, namely the confidence level of the ith row data is lambda i ；
Calculating the weight w of the evaluation factors:
after calculation by the formula (4), finally forming a weight vector W for obtaining an evaluation factor;
s350: calculating a comprehensive evaluation vector Y, namely:
in the formula (5), Y is an evaluation vector, Y 1 ,y 2 ,…，y n The score was comprehensively evaluated for each year.
To further optimize the evaluation method, the evaluation method further comprises the following steps in step S300:
s400: optimizing the multiple evaluation factors, eliminating p evaluation factors with the lowest weight, reselecting another p evaluation factors to replace the p evaluation factors, and performing a fuzzy system analysis step like S300 again to obtain a second evaluation vector;
s500: comparing the standard deviation of the comprehensive evaluation scores of the first evaluation vector and the second evaluation vector in the natural domain, the social domain and the economic domain, and taking the evaluation vector with larger standard deviation as the evaluation vector of the target lake area;
furthermore, an evaluation system is provided, and the evaluation system is applied to the lake region water ecological safety evaluation method; the evaluation system includes:
the data acquisition module is used for acquiring any data information required by applying the evaluation method, wherein the data information at least comprises satellite remote sensing image data of a target lake area in a plurality of time sequences in a plurality of statistical years and necessary statistical data of the target lake area in a natural domain, a social domain and an economic domain in the plurality of statistical years;
the communication module is used for carrying out communication connection on the evaluation system and the Internet so as to enable the evaluation system to carry out data communication with the Internet;
and the processing module is used for carrying out data processing according to the steps of the evaluation method and outputting an evaluation result.
The beneficial effects obtained by the invention are as follows:
1. according to the evaluation method, the appropriate factors are selected to participate in comprehensive evaluation in the natural domain, the economic domain and the social domain based on the lake region, and specific evaluation factors are further refined based on the pressure, the state and the response of the logic relationship of each factor, so that targeted comprehensive evaluation can be more effectively performed on the target lake region;
2. the evaluation method is based on a calculation mode of a fuzzy evaluation system, and can further optimize each step of a specific evaluation method by analyzing the interrelation of each evaluation factor and the weight in the evaluation score;
3. in the evaluation method, by taking the historical water storage data of the lake as a core factor and taking the water storage as an evaluation factor with the most visual and best observation performance in the natural factors of the lake area, the evaluation scores of a plurality of time nodes of the lake area in history can be obtained excellently;
4. the software and hardware parts in the evaluation system adopt modularized design, so that the related software and hardware environments are convenient to upgrade or replace in the future, and the use cost is reduced.
Drawings
The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. Like reference numerals designate corresponding parts throughout the different views.
Reference numerals illustrate:
FIG. 1 is a schematic diagram showing the steps of the evaluation method of the present invention;
FIG. 2 is a schematic diagram of a target lake area satellite remote sensing image after binarization processing according to an embodiment of the present invention;
fig. 3 is a DEM data image of an extracted target lake area according to an embodiment of the invention.
Detailed Description
In order to make the technical scheme and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the following examples thereof; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. Other systems, methods, and/or features of the present embodiments will be or become apparent to one with skill in the art upon examination of the following detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description. Included within the scope of the invention and protected by the accompanying claims. Additional features of the disclosed embodiments are described in, and will be apparent from, the following detailed description.
The same or similar reference numbers in the drawings of embodiments of the invention correspond to the same or similar components; in the description of the present invention, it should be understood that, if any, the terms "upper," "lower," "left," "right," and the like indicate an orientation or a positional relationship based on the orientation or the positional relationship shown in the drawings, this is for convenience of description and simplification of the description, and does not indicate or imply that the apparatus or component to be referred to must have a specific orientation. The terms describing the positional relationship in the drawings are merely for illustrative purposes and are not to be construed as limiting the present patent, and specific meanings of the terms are understood by those of ordinary skill in the art according to specific circumstances.
Embodiment one: an exemplary method for evaluating the ecological safety of water in a lake region is provided, as shown in fig. 1, and comprises the following steps:
s100: the annual average water storage capacity of the target lake area in a plurality of statistical years is counted, and the maximum annual average water storage capacity and the minimum annual average water storage capacity in the middle are counted;
s200: respectively selecting one or more quantifiable indexes of pressure factors and response factors in a natural domain in a target lake region and one or more quantifiable indexes of pressure factors, state factors and response factors in a social domain and an economic domain as evaluation factors, and acquiring specific data of the evaluation factors in a plurality of statistical years;
wherein, the state factor of the target lake area in the natural domain takes the annual average water storage capacity, the maximum annual average water storage capacity and the minimum annual average water storage capacity as the main hardness factors to be selected;
s300: obtaining a first evaluation vector based on the evaluation factors selected in the step S200 of the fuzzy system analysis;
wherein, in step S100, the following sub-steps are included:
s110: acquiring satellite remote sensing image data of a target lake region in a plurality of statistical years, so as to calculate the water surface area of the target lake region;
s120: solving the corresponding water storage amount of the target lake region in a plurality of time phases based on the lake water surface area data of the target lake region in the plurality of time phases in a plurality of years;
s130: fitting and calculating the calculation relation between the water surface area/water storage capacity and the water level of the target lake area, calculating the annual average water storage capacity in a plurality of statistical years, sequencing the annual average water storage capacity in the plurality of statistical years, and counting the maximum annual average water storage capacity and the minimum annual average water storage capacity;
preferably, in step S300, the analysis of the selected evaluation factors based on the fuzzy system comprises the following sub-steps:
s310: establishing an original data matrix for the historical data of the evaluation factors, namely, establishing an N-by-M-scale original data matrix A (x ik )，i＝1,2,……,N,k＝1,2,……,M，x ij I.e., the data value of the j-th evaluation factor in the i-th year;
s320: carrying out standardization processing on the original data matrix; the adopted standardized processing mathematical method is a minimum-maximum standardized method; that is, the annual statistical data X of the historical statistical data of N statistical years is set as X respectively 1 ,x 2 ，…，x n A total of N data; mapping original data x to interval 0,1 by normalization]Between them;
the specific calculation mode is as follows:
for data representing higher water ecological safety degree with larger value of x, there are:
x′ i ＝(x i -min(x i ))/(max(x i )-min(x i ) Formula (1);
for data representing higher water ecological safety degree with smaller value of x, there are:
x′ i ＝(max(x i )-x i )/(max(x i )-min(x i ) Formula (2);
in the formula (1) and the formula (2), x' i I.e. normalized data, x i I.e. evaluating the original data of factor X in the ith year of N statistical years; max (x) i ) I.e. N x i The maximum value, min (x i ) I.e. N x i The minimum of the values;
finally obtaining a fuzzy matrix X '= (X' ik ) N×M ；
S330: establishing a fuzzy similarity matrix R; i.e. blurring matrix (x' ik ) N×M Data x 'of the i and j th years' ik And x' jk Substituting the following calculation formula:
thereby obtaining a fuzzy similar matrix R= (R) ij ) N×N
In the formula (3), r ij The similarity coefficient of the integral data samples in the i year and the j year is the similarity coefficient of the integral data samples in the i year and the j year, and the similarity coefficient represents the similarity degree of the integral data samples in the i year and the j year; wherein,
s340: setting a confidence level and calculating the weight of each item of data; taking the largest matrix element of each row in the fuzzy similarity matrix R as a confidence level lambda, namely the confidence level of the ith row data is lambda i ；
Calculating the weight w of the evaluation factors:
after calculation by the formula (4), finally forming a weight vector W for obtaining an evaluation factor;
s350: calculating a comprehensive evaluation vector Y, namely:
in the formula (5), Y is an evaluation vector, Y 1 ,y 2 ,…，y n The score was comprehensively evaluated for each year.
To further optimize the evaluation method, the evaluation method further comprises the following steps in step S300:
s400: optimizing the multiple evaluation factors, eliminating p evaluation factors with the lowest weight, reselecting another p evaluation factors to replace the p evaluation factors, and performing a fuzzy system analysis step like S300 again to obtain a second evaluation vector;
s500: comparing the standard deviation of the comprehensive evaluation scores of the first evaluation vector and the second evaluation vector in the natural domain, the social domain and the economic domain, and taking the evaluation vector with larger standard deviation as the evaluation vector of the target lake area;
further, an evaluation system is exemplarily provided, and the evaluation system applies the above-mentioned method for evaluating ecological safety of water in a lake region; the evaluation system includes:
the data acquisition module is used for acquiring any data information required by applying the evaluation method, wherein the data information at least comprises satellite remote sensing image data of a target lake area in a plurality of time sequences in a plurality of statistical years and necessary statistical data of the target lake area in a natural domain, a social domain and an economic domain in the plurality of statistical years;
the communication module is used for carrying out communication connection on the evaluation system and the Internet so as to enable the evaluation system to carry out data communication with the Internet;
the processing module is used for carrying out data processing according to the steps of the evaluation method and outputting an evaluation result;
in the exemplary embodiment, in evaluating the lake, it is necessary to comprehensively consider factors of three fields of natural, social and economic fields, because the lake ecosystem is a complex social-economic-natural system; these three field elements represent the key factors in the lake ecosystem and have close interactions with each other;
wherein, the natural domain generally comprises factors such as water storage capacity, water level height, water quality condition, biological diversity and the like of the lake, and the factors are important indexes for reflecting the basic condition of the ecological system of the lake; the water storage capacity and the water level height are taken as the most visual and easier statistics; the water storage capacity and the water level height of the lake are core elements of the natural system state of the lake, and directly reflect the health condition of the lake and the capability of supporting biodiversity and providing services required by human beings;
firstly, the water storage capacity and the water level height are important components of lake water circulation; they are affected by a variety of factors such as rainfall, evaporation, and human activity; if the water storage capacity of the lake is too low or the water level is reduced, the living environment of aquatic organisms in the ecological system of the lake can be deteriorated, so that the biodiversity is affected;
secondly, the water storage capacity and the water level height also influence the ecological service which can be provided by the lake ecological system; for example, a high water storage capacity may provide a richer water resource for human use while also providing habitat for wetland organisms; conversely, if the water storage capacity is reduced or the water level is lowered, it may negatively affect the water supply, fishery and other functions of the lake ecosystem;
furthermore, the water storage capacity and the water level height are quantifiable and relatively easy indicators of data acquisition, which makes them highly practical in comprehensive evaluation; therefore, the water storage capacity and the water level height are selected as core factors in natural factors of the lake, so that the health condition of the ecological system of the lake can be accurately reflected, and an effective decision basis can be provided for an environment manager;
further, the social domain mainly focuses on the influence of human activities on the lake ecosystem, such as population density, community participation, environmental protection policy and the like;
further, the economic domain mainly focuses on the influence of the lake ecosystem on the local economy, including fishery income, travel industry development, investment of related industries and the like;
for the evaluation in these three fields, a comprehensive evaluation is preferably performed using a pressure-state-response model (PSR); the PSR model is an environment management and decision making model, which divides the environment problem into three parts, namely pressure, state and response; pressure represents a variety of factors that negatively impact the environment, such as pollution emissions, reduced biodiversity, etc.; the state refers to the current condition of the environment, including air quality, water quality and the like; responses are countermeasures taken by society to environmental problems, including legislation, education, technical innovation, and the like. Through the model, the problems in the lake ecosystem can be better understood, and the best way for solving the problems is found out, so that better decisions are made; the PSR model can clearly show the causal relationship in the system, and the current water ecological safety evaluation method does not break through, mainly depends on a PSR theoretical model frame, and the index weight is mainly obtained according to an analytic hierarchy process and an expert scoring method, has high subjectivity, lacks effective grasp of a system structure and a decision process, and is not suitable for a complex feedback system;
in a preferred embodiment, the evaluation method adopts a weight calculation mode of each evaluation factor by a fuzzy evaluation system; because the water ecological safety evaluation is a multi-index comprehensive and system evaluation process, clear correlations among evaluation factors are difficult to find due to the complexity of the system; the fuzzy mathematical method is favorable for processing the comprehensive evaluation problem of the system with the ambiguity, the problem is undefined in limit or membership, in the water ecological system, whether macroscopic and microscopic phenomena are in a huge system of society, economy and nature, and the uncertainty and the ambiguity of the water ecological safety evaluation are determined due to the complexity of the system; fuzzy mathematical methods are theoretically dominant in solving such problems;
processing power for fuzzy information: various factors and parameters of water systems often have some uncertainty and ambiguity; for example, environmental factors such as climate change, population growth, etc., often have difficulty accurately measuring the impact on water systems, and fuzzy evaluation methods can effectively address such ambiguities and uncertainties.
Embodiment two: this embodiment should be understood to include at least all of the features of any one of the preceding embodiments, and be further modified based thereon;
for example, the following method may be used to calculate the water storage capacity of the target lake region through the water surface area using the satellite remote sensing image of the target lake region as shown in fig. 2 (a):
step (1): acquiring a water meter area S, namely:
S＝N·R 2 ；
wherein N is the number of pixel elements in the satellite remote sensing image of the target lake region, and R is the image spatial resolution, such as 30m;
preferably, the following is performed by using a software algorithm in the remote sensing image processing platform (ENVI):
counting the number of pixel elements, setting a binarization threshold value, carrying out binarization on an original satellite remote sensing image after determining to obtain a binarization image, and then carrying out quick states or density segmentation, and counting the number of pixel elements of a target lake area in a binary image as shown in a figure 2 (b);
step (2): shp files forming the target lake region, i.e
Loading the obtained binary image of the target lake area from NEVIClasic; selecting Vector-Rastertovector to obtain Vector data in the evf format; selecting file-ExportLayertoShapefile in a vector file window, and d to obtain a vector file in a shp format;
step (3): loading shp files of the obtained target lake region into Arcgis, carrying out surface transformation to obtain boundary lines (data management tool-element-surface transformation) of the lake, reversely selecting and deleting small areas after attribute surface area descending arrangement, namely, making a buffer region, carrying out geographic treatment, namely, buffering the buffer region left and right by 15m (FULL);
step (4): extracting DEM data of a target lake region, as shown in figure 3; cutting DEM with buffer boundary, namely Spatial-extraction analysis-mask extraction-opening attribute after extraction, and obtaining average water level of lake surface by average value;
step (5): the DEM is cut by shp of the whole lake surface, or extracted according to a mask, so as to obtain the DEM of the lake bottom, then a 3DAnalyst tool, a functional surface, a surface volume and a DEM (not the original DEM) of the lake bottom which is just cut by shp is selected, because the water quantity is the volume from the lake bottom to the upper side, an ABOVE is selected, the plane height is the average water level elevation value of the lake surface, namely the average value in the step (4), and the water storage capacity of the target lake area based on the time sequence of the current satellite remote sensing image is obtained in the last table.
Embodiment III: this embodiment should be understood to include at least all of the features of any one of the preceding embodiments, and be further modified based thereon;
in some preferred embodiments, the choice of the evaluable factors may preferably be chosen based on the following factors:
natural domain:
pressure factor: the degree of climate change, such as the trend of annual average gas temperature and annual precipitation; these factors may change the hydrologic situation of the lake, bringing pressure to the lake ecosystem;
response factor: the biodiversity of the target lake area and variations thereof, such as variations in the number of species in the lake; this may reflect the response and adaptation of the lake ecosystem to pressure;
social domain:
pressure factor: population growth rate in lake areas; the rapid population growth may cause excessive use and pollution of lake resources, and stress on lake ecology;
status factors: the public attitude and cognition of lake protection can be obtained through community investigation and interviews, which reflects the attention degree and knowledge of the society to lake protection;
response factor: the investment of communities in lake protection and management, such as investment of manpower and financial resources, can be regarded as the response of society to lake protection;
economic field:
pressure factor: the extent and speed of the development of the lake perimeter industry; this may lead to deterioration of the lake water quality, bringing pressure to the lake ecology;
status factors: the economic value of lakes, such as the contribution of the lake travel industry to the local economy;
response factor: the government investment in lake protection, such as public investment in protecting and restoring lake ecology, reflects the response of the economic system to lake problems;
the suitable evaluation factors need to be selected, namely the lake region is based on a complex natural ecological system, and the geographical region division of the lake region can directly influence the evaluation range and the emphasis point of the lake; from a geographic perspective, the zoning is typically based on physical and ecological boundaries, such as mountains, rivers, vegetation types, etc.; the natural attribute and ecological process of the lake are focused on by the dividing mode, such as watershed water circulation, geological landform, vegetation coverage and the like; on this premise, the choice of evaluation factors mainly focuses on the condition and influence of natural environment, including but not limited to the influence of water quality, biodiversity, climate change and the like;
however, from an administrative point of view, the regional division may be based on political or socioeconomic boundaries, such as cities, counties, administrative districts, and the like. Under the premise, the focus of lake evaluation may be turned to the relationship between the lake and the human activity, such as the influence of the urban process on the lake, the pollution load generated by the human activity, the requirement of the social and economic activities on water resources and the like; thus, the choice of evaluation factors will mainly focus on the impact of socioeconomic activities on lakes, including but not limited to population density, the intensity and variety of the economic activities, the implementation of environmental policies, etc.;
therefore, whether the lake area is divided from the geographic or administrative management point of view, the selection of corresponding evaluation factors is required on the basis of different evaluation preconditions so as to ensure the effectiveness and accuracy of the lake evaluation.
While the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications can be made without departing from the scope of the invention. That is, the methods, systems and devices discussed above are examples. Various configurations may omit, replace, or add various procedures or components as appropriate. For example, in alternative configurations, the methods may be performed in a different order than described, and/or various components may be added, omitted, and/or combined. Moreover, features described with respect to certain configurations may be combined in various other configurations, such as different aspects and elements of the configurations may be combined in a similar manner. Furthermore, as the technology evolves, elements therein may be updated, i.e., many of the elements are examples, and do not limit the scope of the disclosure or the claims.
Specific details are given in the description to provide a thorough understanding of exemplary configurations involving implementations. However, configurations may be practiced without these specific details, e.g., well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring configurations. This description provides only an example configuration and does not limit the scope, applicability, or configuration of the claims. Rather, the foregoing description of the configuration will provide those skilled in the art with an enabling description for implementing the described techniques. Various changes may be made in the function and arrangement of elements without departing from the spirit or scope of the disclosure.
It is intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is intended that it be regarded as illustrative rather than limiting. Various changes and modifications to the present invention may be made by one skilled in the art after reading the teachings herein, and such equivalent changes and modifications are intended to fall within the scope of the invention as defined in the appended claims.
Claims (3)
1. A lake region water ecological safety evaluation method, which is characterized by comprising the following steps:
s100: the annual average water storage capacity of the target lake area in a plurality of statistical years is counted, and the maximum annual average water storage capacity and the minimum annual average water storage capacity in the middle are counted;
s200: respectively selecting one or more quantifiable indexes of pressure factors and response factors in a natural domain in a target lake region and one or more quantifiable indexes of pressure factors, state factors and response factors in a social domain and an economic domain as evaluation factors, and acquiring specific data of the evaluation factors in a plurality of statistical years;
wherein, the state factor of the target lake area in the natural domain takes the annual average water storage capacity, the maximum annual average water storage capacity and the minimum annual average water storage capacity as the main hardness factors to be selected;
s300: obtaining a first evaluation vector based on the evaluation factors selected in the step S200 of the fuzzy system analysis;
wherein, in step S100, the following sub-steps are included:
s110: acquiring satellite remote sensing image data of a target lake region in a plurality of statistical years, so as to calculate the water surface area of the target lake region;
s120: solving the corresponding water storage amount of the target lake region in a plurality of time phases based on the lake water surface area data of the target lake region in the plurality of time phases in a plurality of years;
s130: fitting and calculating the calculation relation between the water surface area/water storage capacity and the water level of the target lake area, calculating the annual average water storage capacity in a plurality of statistical years, sequencing the annual average water storage capacity in the plurality of statistical years, and counting the maximum annual average water storage capacity and the minimum annual average water storage capacity;
in step S300, the selected evaluation factor is analyzed based on the fuzzy system, including the following sub-steps:
s310: establishing an original data matrix for the historical data of the evaluation factors, namely, establishing an N-by-M-scale original data matrix A (x ik )，i=1,2,……,N,k=1,2,……,M，x ij I.e., the data value of the j-th evaluation factor in the i-th year;
s320: carrying out standardization processing on the original data matrix; adopted markThe mathematical method of the normalization treatment is a minimum-maximum normalization method; that is, the annual statistical data X of the historical statistical data of N statistical years is set as X respectively 1 ,x 2 ，…，x n A total of N data; mapping original data x to interval 0,1 by normalization]Between them;
the specific calculation mode is as follows:
for data representing higher water ecological safety degree with larger value of x, there are:
formula (1);
for data representing higher water ecological safety degree with smaller value of x, there are:
formula (2);
in the formulas (1) and (2),i.e. normalized data,/->I.e. evaluating the original data of factor X in the ith year of N statistical years; max (x) i ) I.e. N x i The maximum value, min (x i ) I.e. N x i The minimum of the values;
finally obtaining the fuzzy matrix；
S330: establishing a fuzzy similarity matrix R; i.e. blurring matrixData of the i and j th year>And->Substituting the following calculation formula:
formula (3);
thereby obtaining fuzzy similar matrix；
In the formula (3), r ij The similarity coefficient of the integral data samples in the i year and the j year is the similarity coefficient of the integral data samples in the i year and the j year, and the similarity coefficient represents the similarity degree of the integral data samples in the i year and the j year; wherein,
；
s340: setting a confidence level and calculating the weight of each item of data; taking the largest matrix element of each row in the fuzzy similarity matrix R as a confidence level lambda, namely the confidence level of the ith row data is lambda i ；
Calculating the weight w of the evaluation factors:
formula (4);
after calculation by the formula (4), finally forming a weight vector W for obtaining an evaluation factor;
s350: calculating a comprehensive evaluation vector Y, namely:
formula (5);
in the formula (5), Y is an evaluation vector, Y 1 ,y 2 ,…，y n The score was comprehensively evaluated for each year.
2. The evaluation method according to claim 1, characterized in that the evaluation method further comprises the following step in step S300:
s400: optimizing the multiple evaluation factors, eliminating p evaluation factors with the lowest weight, reselecting another p evaluation factors to replace the p evaluation factors, and performing a fuzzy system analysis step like S300 again to obtain a second evaluation vector;
s500: and comparing the standard deviation of the comprehensive evaluation scores of the first evaluation vector and the second evaluation vector in the natural domain, the social domain and the economic domain, and taking the evaluation vector with the large standard deviation as the evaluation vector of the target lake area.
3. A lake-area water ecology safety evaluation system, characterized in that the evaluation system applies a lake-area water ecology safety evaluation method as described in claims 1 to 2; the evaluation system includes:
the data acquisition module is used for acquiring any data information required by applying the evaluation method, wherein the data information at least comprises satellite remote sensing image data of a target lake area in a plurality of time sequences in a plurality of statistical years and statistical data of the target lake area in a plurality of statistical years;
the communication module is used for carrying out communication connection on the evaluation system and the Internet so as to enable the evaluation system to carry out data communication with the Internet;
and the processing module is used for carrying out data processing according to the steps of the evaluation method and outputting an evaluation result.
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