A method for evaluating river water ecological quality based on the optimization of index weights

Abstract

The present invention discloses a river water ecological quality evaluation method based on indicator weight optimization, comprising the following steps: collecting river water indicator data; dividing and standardizing each indicator data according to a forward indicator processing sequence and a reverse indicator processing sequence to obtain forward indicator processing values and reverse indicator processing values; constructing a comprehensive river water ecological quality evaluation model and dividing each indicator processing value; calculating the actual weight value of each indicator data in conjunction with the comprehensive river water ecological quality evaluation model; calculating a comprehensive river water ecological quality index, and classifying water ecological quality levels based on the comprehensive river water ecological quality index. The present invention can reflect the complete cycle of the river water ecological environment itself, and uses an improved hierarchical analysis method to calculate weight values, effectively solving the problem of uneven weight distribution in the traditional hierarchical analysis method due to the differences in different evaluation values and the overlap between indicators.

Description

Technical Field

[0001] The present invention relates to the technical field of water ecological quality evaluation, and in particular to a river water ecological quality evaluation method based on indicator weight optimization. Background Art

[0002] There are numerous indicator systems for evaluating the quality of various rivers and river basins. Developed countries have taken water ecology as the basic unit of environmental management and have carried out long-term and meticulous work in its practical application. Compared with these countries, my country's water environment management still focuses on pollution source reduction and water pollutant control, which does not conform to the demand-oriented shift of water environment evaluation from water quality assessment to comprehensive assessment of water quality and water ecology. A review of relevant research on river water ecology assessment at home and abroad reveals that the relevant technologies have some shortcomings:

[0003] (1) Lack of systematic evaluation, and the evaluation indicators are relatively single-minded. Most indicators focus on one aspect, such as water quality physical and chemical indicators or water ecological health, and lack comprehensiveness. They cannot systematically reflect the health of the river water environment and the cycle chain of the river water ecosystem, resulting in biased evaluation results. Alternatively, the evaluation indicators are too redundant and, although comprehensive, do not take into account the convenience of actual application and are difficult to operate.

[0004] (2) There are deficiencies in the method of determining indicator weights. For the evaluation of river water ecological quality levels, calculating the weights of various indicator data is an important part of the comprehensive evaluation system. The rationality of the weights is directly related to whether the evaluation results are objective and reasonable. At present, the main methods for determining indicator weights include expert scoring method, hierarchical analysis method, principal component analysis method and fuzzy comprehensive evaluation method. The hierarchical analysis method is widely used. However, according to current research on the hierarchical analysis method, this method has certain defects: first, it does not take into account the differences in the weights of indicator data of different categories; second, there is a correlation between different indicator data, and the overlapping parts between indicator data are not considered during calculation, resulting in an imbalance in weight distribution. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a river water ecological quality evaluation method based on indicator weight optimization, aiming to solve the shortcomings of the evaluation methods in the existing technology that the evaluation indicators are relatively single or too complicated, and effectively solve the problem of uneven weight distribution caused by the differences in different evaluation values in the hierarchical analysis method and the non-independence of data indicators.

[0006] The present invention is achieved by adopting the following technical solutions:

[0007] A river water ecological quality evaluation method based on indicator weight optimization includes the following steps:

[0008] Step S1: Collecting various indicator data of the river, the indicator data including water quality element A, water ecological element B and sediment element C, the water quality element A including chemical oxygen demand index A1, ammonia nitrogen index A2 and total phosphorus index A3, the water ecological element B including benthic zoowp index B1, benthic zoowp index B2, zooplankton Shannon-Wiener diversity index B3 and zooplankton richness index B4, the sediment element C including sediment total nitrogen index C1, sediment total phosphorus index C2 and sediment heavy metal potential risk ecological index C3;

[0009] Step S2: dividing and standardizing the indicator data according to the forward indicator processing sequence and the reverse indicator processing sequence to obtain the forward indicator processing value and the reverse indicator processing value;

[0010] Step S3: Constructing a comprehensive evaluation model for river water ecological quality including a target layer, a criterion layer, and an indicator layer. The target layer, the criterion layer, and the indicator layer are hierarchical and arranged in descending order from high to low. The target layer includes the river water ecological quality element S; the criterion layer includes the water quality element A, the water ecological element B, and the sediment element C; the positive indicator processing values and the negative indicator processing values corresponding to the water quality element A, the water ecological element B, and the sediment element C are divided into each indicator layer;

[0011] Step S4: combining the comprehensive evaluation model of river water ecological quality, calculating the theoretical weight values of various indicator data, optimizing the theoretical weight values using the improved analytic hierarchy process, and revising the comprehensive evaluation model of river water ecological quality to obtain actual weight values;

[0012] Step S5: Calculate the comprehensive index of river water ecological quality based on the positive indicator processing value and the reverse indicator processing value, combined with the actual weight value corresponding to each indicator data, and divide the water ecological quality level according to the comprehensive index of river water ecological quality.

[0013] To optimize the above technical solutions, specific measures taken also include:

[0014] Furthermore, the specific calculation process of the positive indicator processing value and the reverse indicator processing value in step S2 is:

[0015] S21: Divide the data of each indicator to obtain the original value of the positive indicator and the original value of the reverse indicator;

[0016] S22: normalize the original value of the positive indicator and the original value of the reverse indicator according to the positive indicator processing sequence and the reverse indicator processing sequence to obtain the positive indicator processing value and the reverse indicator processing value.

[0017] Forward indicator processing sequence:

[0018] Reverse indicator processing sequence:

[0019]

[0020] Where: y′ Zk Represents the kth positive indicator processing value, y′ Zk ∈(0,1);y Zk represents the original value of the kth positive indicator; y Zksu Indicates the upper limit of the kth positive indicator standard; y′ Nj Represents the j-th reverse indicator processing value, y′ Nj ∈(0,1);y Nj represents the original value of the j-th inverse indicator; y Njsu represents the upper limit of the j-th reverse indicator standard; y Njsd Indicates the lower limit of the original value of the j-th inverse indicator.

[0021] Furthermore, the process of revising the river water ecological quality comprehensive evaluation model in step S4 specifically includes the following steps:

[0022] S41: Establish an evaluation model, compare the index data of the same layer with the corresponding factors of the upper layer, and obtain multiple evaluation values according to the set AHP scale;

[0023] S42: Establish a judgment matrix for the evaluation value, use MATLAB to perform hierarchical single sorting on the judgment matrix and calculate the test coefficient CR, perform test coefficient threshold judgment on the test coefficient CR, and calculate the theoretical weight value corresponding to the test coefficient CR that meets the test coefficient threshold condition;

[0024] S43: According to the corresponding relationship between each evaluation value and each element of the criterion layer, the theoretical weight value of the evaluation model is optimized and modified. The modified weight value is calculated using the SL395-2007 technical standard combined with the classification statistics method. The formula is as follows:

[0025]

[0026] Where: Indicates the modified weight value of the indicator data Yi, Y = A, B, C, i ≤ 4;

[0027] Indicates the index data Y calculated from m evaluation values i The theoretical weight value, m∈(1,n);

[0028] Indicates the index data Y calculated from nm evaluation values i The theoretical weight value of

[0029] n represents the total number of evaluation values, and m represents the mth evaluation value;

[0030] S44: Based on the correlation coefficient between each indicator data, the indicator data corresponding to the correlation coefficient exceeding the set correlation coefficient threshold are selected, and the weight values of the revised weight values of the indicator data are redistributed to eliminate the overlapping parts. After the weight values of all the indicator data corresponding to the correlation coefficient exceeding the set correlation coefficient threshold are redistributed, the remaining indicator data corresponding to the correlation coefficient that does not exceed the set correlation coefficient threshold are adjusted accordingly, so that the sum of the actual weight values of all indicator data is 1.

[0031] Furthermore, in step S42, a judgment matrix for evaluation values is established, and MATLAB is used to perform hierarchical single sorting on the evaluation values and calculate the test coefficient CR. The specific process of threshold judgment on the test coefficient CR is as follows:

[0032] S421: Calculate the consistency index CI of the judgment matrix:

[0033]

[0034]

[0035] Where λ max Indicates the maximum eigenvalue of the matrix, n indicates the order of the judgment matrix, and to measure the size of CI, the random consistency index RI is introduced, CI m Represents the mth consistency index CI, where m = n

[0036] S422: Compare the consistency index CI and the random consistency index RI to obtain the test coefficient CR, which is as follows:

[0037]

[0038] S423: Compare the test coefficient CR with the test coefficient threshold. If the test coefficient CR is less than 0.1, the judgment matrix passes the consistency test and the theoretical weight value of each indicator data is collected. If the test coefficient CR is greater than 0.1, the judgment matrix is adjusted to make the test coefficient CR less than 0.1, and then the theoretical weight value of each indicator data is calculated.

[0039] Furthermore, the calculation method of the theoretical weight value in step S423 is as follows:

[0040] According to the hierarchical distribution of the comprehensive evaluation model of river water ecological quality, the weight value corresponding to each element in the criterion layer is used as the first weight value, and the weight value of each indicator in the indicator layer is used as the second weight value. The first weight value and the second weight value are multiplied to obtain the theoretical weight value.

[0041] Furthermore, the specific method for redistributing the weight values of the modified weight values of the indicator data in step S44 is:

[0042] S441: Calculate indicator data Y i and indicator data Y e Correlation coefficient The calculation method is as follows:

[0043]

[0044] Where, l xx Indicates indicator data Y i The sum of squares of the mean deviations of the treated values; l yy Indicates indicator data Y e The sum of squares of the mean deviations of the treated values; l xy Indicates indicator data Y i Processing value and indicator data Y e The sum of the products of the mean deviations of the treatment values.

[0045] S442: Correlation coefficient Compare with the correlation coefficient threshold. If the correlation coefficient If it is greater than the correlation coefficient threshold, the indicator data Y is calculated. i and indicator data Y e The actual weight value after correction:

[0046]

[0047]

[0048]

[0049] Where: Y = A, B, C, i≤4, e≤4; Indicates indicator data Y i The actual weight value of Indicates indicator data Y e The actual weight value of Indicates indicator data Y i The modified weight value of Indicates indicator data Y e The modified weight value of Indicates indicator data Y i and indicator data Y e Correct the overlap of weight values;

[0050] S443: The remaining index data Y that have not been independently corrected in step S442 h Make corresponding weighted adjustments so that the sum of the actual weight values of all indicator data is 1. The adjustment formula is as follows:

[0051]

[0052] Where: Indicates indicator data Y h The actual weight value of Indicates indicator data Y h The modified weight value of Indicates indicator data Y i The actual weight value of Indicates indicator data Y e The actual weight value of Indicates indicator data Y i The modified weight value of Indicates indicator data Y e The modified weight value of .

[0053] Furthermore, the specific calculation process of the comprehensive index of river water ecological quality evaluation is as follows:

[0054] S51: Calculate the sub-item evaluation index of the river water ecological quality, the sub-item evaluation index includes the water quality sub-item evaluation index, the water ecology sub-item evaluation index, and the sediment sub-item evaluation index. The calculation formula is as follows:

[0055]

[0056]

[0057]

[0058] Where: T Ax T is the water quality sub-item evaluation index of the x-th monitoring section of the river; Bx T is the water ecological sub-item evaluation index of the x-th monitoring section of the river; Cx is the sediment sub-item evaluation index of the x-th monitoring section of the river; Water quality factor A i Processing value results of indicator data; Water ecological element B of the standard layer i Indicator data processing value; C is the sediment element of the standard layer i Indicator data processing value; Water quality factor A i The actual weight value of the indicator data; Water ecological element B i The actual weight value of the indicator data; Sediment element C i The actual weight value of the indicator data;

[0059] S52: Calculate the comprehensive index of river water ecological quality evaluation:

[0060] T S =∑(T Ax +T Bx +T Cx )×(c x / c)

[0061] Where: T S is the comprehensive index for river water ecological quality evaluation; c represents the total length of the river; c x It indicates the length of the river section from the xth monitoring section to the x-1th monitoring section. When x-1 is 0, it represents the starting point of the river. The c of the xth monitoring section is x represents the length from the x-1th monitoring section to the end of the river. If the river has only one monitoring section, then c x =c;

[0062] S53: Based on the comprehensive index of river water ecological quality evaluation, the comprehensive index of river water ecological quality evaluation is divided into five levels according to different threshold intervals: Level I, Level II, Level III, Level IV, and Level V;

[0063] S54: Evaluate the quality level of river water ecology.

[0064] Beneficial effects of the present invention:

[0065] The river water ecological quality grade evaluation method of the present invention has the following beneficial effects: the river water ecological quality grade evaluation method provided by the present invention makes up for the shortcomings of the current river water ecological quality evaluation indicators being relatively single or too complicated, and effectively solves the problem that the differences in indicator weights are not considered in the hierarchical analysis method and the imbalance in weight distribution caused by the non-independence of indicators. The rating method of the present invention is highly comprehensive and highly operational. It makes multiple revisions to the indicator weights, making the improved hierarchical analysis method used for weighting more credible. The overall evaluation system can reflect the complete cycle chain of the river water ecological environment itself, and fully reflect the water environment quality status. It has opened up a new evaluation model with the river water ecological environment water-ecology-sediment full chain as the core concept, and provides support for the management to fully grasp the current status of river water ecology and carry out supervision and assessment work. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of the evaluation method provided by the present invention.

[0067] Figure 2 It is a schematic diagram of the calculation steps of the improved hierarchical analysis method provided by the present invention. DETAILED DESCRIPTION

[0068] In order to illustrate the technical solution and working principle of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0069] like Figure 1 As shown, a river water ecological quality evaluation method based on indicator weight optimization is provided, wherein the method comprises the following steps:

[0070] Step S1: Collect various indicator data for the river. Conventional water samples, sediments, and aquatic organisms from the test river are collected, processed, and tested according to their respective standards, guidelines, and specifications to obtain various types of indicator data for the test river. The collected various types of indicator data are classified into water quality element A, water ecological element B, and sediment element C. Water quality element A includes chemical oxygen demand (COD) index A1, ammonia nitrogen index A2, and total phosphorus index A3; water ecological element B includes benthic animal BMWP index B1, benthic animal BPI index B2, zooplankton Shannon-Wiener diversity index B3, and zooplankton richness index B4; and sediment element C includes sediment total nitrogen index C1, sediment total phosphorus index C2, and sediment heavy metal potential risk ecological index C3.

[0071] Water quality elements are directly measured and obtained, while water ecological elements and sediment elements are calculated based on the collected aquatic organisms and sediments to obtain the corresponding indicators.

[0072] Step S11: Calculate the benthic animal BMWP index. The calculation formula is as follows:

[0073] BMWP=∑t i

[0074] Where: t i This index represents the family-level sensitivity value of species i present in the sample. This index is scored on a scale of 1-10 based on the differences in pollution tolerance of macrobenthic animals. The expected value for the BMWP of freshwater benthic macroinvertebrates in river waters is 69. A higher BMWP index indicates less disturbance and pollution to the aquatic ecosystem.

[0075] Step S12: Calculate the benthic animal BPI index, the formula is as follows:

[0076]

[0077] Where: N1 represents the number of oligochaetes, leeches and midges in the collected samples; N2 represents the number of polychaetes, crustaceans and other aquatic insects except chironomids; N3 represents the number of mollusks. When the benthic zooplankton BPI index is greater than 5, it indicates that the water ecological environment is severely polluted; when it is less than 0.1, it indicates that the water ecological environment is clean.

[0078] Step S13: Calculate the Shannon-Wiener diversity Y of zooplankton using the following formula:

[0079] Y=∑(n i / N)×ln n i / N

[0080] Where: n i represents the number of individuals of the i-th species, and N represents the number of individuals of all species.

[0081] Step S14: Calculate the Margalef species richness index d of zooplankton Ma , the formula is as follows:

[0082] d Ma =(Q s -1) / log2S

[0083] Where: Q s Indicates the total number of species in the sample; S indicates the total number of individuals in the collected sample;

[0084] Step S15: Calculate the sediment heavy metal potential risk ecological index E. The sediment heavy metal potential risk ecological index E involves metal elements such as Cu, Cd, Cr, Zn, As, Pb, and Hg in the sediment. If the metal element is greater than 280, it indicates a strong pollution level. If the metal element is less than 70, it indicates a mild pollution level. The calculation formula is as follows;

[0085] E=∑e r ×C sr / C nr

[0086] Where: e r represents the toxicity coefficient of the rth heavy metal element (the toxicity coefficients of Cu, Cd, Cr, Zn, As, Pb and Hg are 5, 30, 2, 1, 10, 5 and 40 respectively); C sr Represents the measured value of the rth element content; C nr represents the background value of the rth element in the soil;

[0087] Step S2: Divide and standardize the indicator data according to the forward indicator processing sequence and the reverse indicator processing sequence to obtain the forward indicator processing value and the reverse indicator processing value.

[0088] Specifically, since the measurement units of various indicator data are not uniform, it is necessary to standardize the indicator data in step S1 to solve the homogenization problem of the different quality indicator values.

[0089] The specific calculation process of the positive indicator processing value and the reverse indicator processing value in step S2 is:

[0090] S21: Divide the data of each indicator to obtain the original value of the positive indicator and the original value of the reverse indicator;

[0091] S22: normalizing the original value of the positive indicator and the original value of the reverse indicator according to the positive indicator processing sequence and the reverse indicator processing sequence to obtain the positive indicator processing value and the reverse indicator processing value;

[0092] Forward indicator processing sequence:

[0093] Reverse indicator processing sequence:

[0094]

[0095] Where: y′ Zk Represents the kth positive indicator processing value, y′ Zk ∈(0,1);y Zk represents the original value of the kth positive indicator; y Zksu Indicates the upper limit of the kth positive indicator standard; y′ Nj Represents the j-th reverse indicator processing value, y′ Nj ∈(0,1);y Nj represents the original value of the j-th inverse indicator; y Njsu represents the upper limit of the j-th reverse indicator standard; y Njsd Indicates the lower limit of the original value of the j-th inverse indicator.

[0096] As shown in Table 1, it is a table for the division of indicator data.

[0097] Table 1 Index data classification table

[0098]

[0099] Step S3: Construct a comprehensive evaluation model for river water ecological quality including a target layer, a criterion layer and an indicator layer. The target layer, the criterion layer and the indicator layer are hierarchical and arranged in descending order from high to low. The target layer includes the river water ecological quality element S; the criterion layer includes the water quality element A, the water ecological element B and the sediment element C; the positive indicator standard values and the reverse indicator standard values corresponding to the water quality element A, the water ecological element B and the sediment element C are divided into each indicator layer.

[0100] Specifically, by establishing a comprehensive evaluation model for river water ecological quality, the weight values of each indicator data can be easily obtained;

[0101] Step S4: Combined with the comprehensive evaluation model of river water ecological quality, calculate the theoretical weight values of the standard values of each positive indicator and each reverse indicator, use the improved hierarchical analysis method to optimize the theoretical weight values, obtain the actual weight values, and modify the comprehensive evaluation model of river water ecological quality according to the actual weight values.

[0102] S41: Establish an evaluation model, compare the index data of the same layer with the corresponding factors of the upper layer, and obtain multiple evaluation values according to the set AHP scale;

[0103] S42: Establish a judgment matrix for the evaluation value, use MATLAB to perform hierarchical single sorting on the judgment matrix and calculate the test coefficient CR, perform test coefficient threshold judgment on the test coefficient CR, and calculate the theoretical weight value corresponding to the test coefficient CR that meets the test coefficient threshold condition;

[0104] S421: Calculate the consistency index CI of the judgment matrix:

[0105]

[0106]

[0107] Where λ max Indicates the maximum eigenvalue of the matrix, n indicates the order of the judgment matrix, and to measure the size of CI, the random consistency index RI is introduced, CI m represents the mth consistency index CI, where m = n;

[0108] S422: Compare the consistency index CI and the random consistency index RI to calculate the test coefficient CR. The formula is as follows:

[0109]

[0110] S423: Compare the test coefficient CR with the test coefficient threshold. If the test coefficient CR is less than 0.1, the judgment matrix passes the consistency test and the theoretical weight value of each indicator data is collected. If the test coefficient CR is greater than 0.1, the judgment matrix is adjusted to make the test coefficient CR less than 0.1, and then the theoretical weight value of each indicator data is calculated.

[0111] The calculation method of the theoretical weight value is as follows:

[0112] According to the hierarchical distribution of the comprehensive evaluation model of river water ecological quality, the weight value corresponding to each element in the criterion layer is used as the first weight value, and the weight value of each indicator in the indicator layer is used as the second weight value. The first weight value and the second weight value are multiplied to obtain the theoretical weight value.

[0113] S43: According to the corresponding relationship between each evaluation value and each element of the criterion layer, the theoretical weight value of the evaluation model is optimized and modified. The modified weight value is calculated using the SL395-2007 technical standard combined with the classification statistics method. The formula is as follows:

[0114]

[0115] Where: Indicates indicator data Y i Corrected weight value, Y = A, B, C, i ≤ 4;

[0116] Indicates the index data Y calculated from m evaluation values i The theoretical weight value, m∈(1,n);

[0117] The index data Y calculated from nm evaluation values i The theoretical weight value of

[0118] n represents the total number of evaluation values, and m represents the mth evaluation value;

[0119] S44: Based on the correlation coefficient between each indicator data, the indicator data corresponding to the correlation coefficient exceeding the set correlation coefficient threshold are selected, and the weight values of the revised weight values of the indicator data are redistributed to eliminate the overlapping parts. After the weight values of all the indicator data corresponding to the correlation coefficient exceeding the set correlation coefficient threshold are redistributed, the remaining indicator data corresponding to the correlation coefficient that does not exceed the set correlation coefficient threshold are adjusted accordingly, so that the sum of the actual weight values of all indicator data is 1.

[0120] The specific method for redistributing the weight values of the revised weight values of each indicator data is as follows:

[0121] S441: Calculate indicator data Y i and indicator data Y e Correlation coefficient The calculation method is as follows:

[0122]

[0123] Where, l xx Indicates indicator data Y i The sum of squares of the mean deviations of the treated values; l yy Indicates indicator data Y e The sum of squares of the mean deviations of the treated values; l xy Indicates indicator data Y i Processing value and indicator data Y e The sum of the products of the mean deviations of the treatment values.

[0124] S442: Correlation coefficient Compare with the correlation coefficient threshold. If the correlation coefficient If it is greater than the correlation coefficient threshold, the indicator data Y is calculated. i and indicator data Y e The actual weight value after correction:

[0125]

[0126]

[0127]

[0128] Where: Y = A, B, C, i≤4, e≤4; Indicates indicator data Y i The actual weight value of Indicates indicator data Y e The actual weight value of Indicates indicator data Y i The modified weight value of Indicates indicator data Y e The modified weight value of Indicates indicator data Y i and indicator data Y e Correct the overlap of weight values;

[0129] S443: The remaining index data Y that have not been independently corrected in step S442 h Make corresponding weighted adjustments so that the sum of the actual weight values of all indicator data is 1. The adjustment formula is as follows:

[0130]

[0131] Where: Indicates indicator data Y h The actual weight value of Indicates indicator data Y h The modified weight value of Indicates indicator data Y i The actual weight value of Indicates indicator data Y e The actual weight value of Indicates indicator data Y i The modified weight value of Indicates indicator data Y e The modified weight value of .

[0132] Step S5: Calculate the comprehensive index of river water ecological quality, and classify water ecological quality levels according to the comprehensive index.

[0133] Step S51: Calculate the sub-item evaluation index of the river water ecological quality. The sub-item evaluation index includes the water quality sub-item evaluation index, the water ecology sub-item evaluation index, and the sediment sub-item evaluation index. The calculation formula is as follows:

[0134]

[0135]

[0136]

[0137] Where: T Ax T is the water quality sub-item evaluation index of the x-th monitoring section of the river; Bx T is the water ecological sub-item evaluation index of the x-th monitoring section of the river; Cx is the sediment sub-item evaluation index of the x-th monitoring section of the river; Water quality factor A i Processing value results of indicator data; Water ecological element B of the standard layer i Indicator data processing value; C is the sediment element of the standard layer i Indicator data processing value; Water quality factor A i The actual weight value of the indicator data; Water ecological element B i The actual weight value of the indicator data; Sediment element C i The actual weight value of the indicator data.

[0138] Step S52: The formula for calculating the comprehensive index of river water ecological quality evaluation is as follows:

[0139] T S =∑(T Ax +T Bx +T Cx )×(c x / c)

[0140] Where: T S is the comprehensive index for river water ecological quality evaluation; c represents the total length of the river; c x It indicates the length of the river section from the xth monitoring section to the x-1th monitoring section. When x-1 is 0, it represents the starting point of the river. The c of the xth monitoring section is x represents the length from the x-1th monitoring section to the end of the river. If the river has only one monitoring section, then c x =c;

[0141] Step S53: Based on the river water ecological quality grade evaluation comprehensive index, the river water ecological quality grade evaluation comprehensive index is divided into five levels according to different threshold intervals: level I, level II, level III, level IV, and level V;

[0142] Step S54: Evaluate the quality level of the river water ecology, where level I is the best and level V is the worst. The threshold range is 0-1, divided according to the normal distribution, and the specific range is as follows.

[0143] Table 2 Grade classification table

[0144] grade Level I Level II Level III Level IV V-level interval (0.964，1] (0.726，0.964] (0.274，0.726] (0.036，0.274] [0，0.036)

[0145] Experimental example:

[0146] Two monitoring sections are set up on River 1 and River 2 respectively. After obtaining the index data, the index data is standardized. The standardized processing values are shown in the following table:

[0147] Table 3 Corresponding table of index data standardization processing values

[0148]

[0149] The theoretical weight values of the indicator data are obtained using the hierarchical analysis method. The theoretical weight value results are shown in the following table:

[0150] Table 4 Theoretical weight value table of indicator data

[0151]

[0152]

[0153] The above theoretical weight values are optimized and distributed, and the corrected weight values and actual weight values are calculated in turn. The results are as follows:

[0154] Table 5 Calculation results of the revised weight values and actual weight values of indicator data

[0155]

[0156]

[0157] The actual weights of chemical oxygen demand (A1), ammonia nitrogen (A2), total phosphorus (A3), benthic animal BMWP index (B1), benthic animal BPI index (B2), zooplankton Shannon-Wiener diversity index (B3), zooplankton richness index (B4), sediment total nitrogen (C1), sediment total phosphorus (C2), and sediment heavy metal potential risk ecological index (C3) were calculated to be 5.0%, 6.6%, 26.1%, 19.2%, 14.0%, 10.7%, 8.7%, 2.1%, 3.5%, and 4.1%, respectively.

[0158] Based on the actual weight values of the above indicators, the evaluation results of the water ecological quality sub-item of river 1T are calculated. A11 is 0.15, T B11 is 0.24, T C11 0.06; section 2T A12 is 0.33, T B12 is 0.21, T C12 0.05; River 2 water ecological quality sub-item evaluation results section 3T A21 is 0.35, T B21 is 0.21, Y C21 0.06; section 4T A22 is 0.32, T B22 is 0.16, T C22 is 0.04.

[0159] Carry out river water ecological quality evaluation. The length of the river controlled by section 1 of river 1 is 14 kilometers, and the length controlled by section 2 is 24 kilometers. The comprehensive index of river water ecological quality evaluation T is calculated. S1 The river length controlled by section 3 of river 2 is 7 kilometers, and the river length controlled by section 4 is 26.7 kilometers. The comprehensive index of water ecological quality evaluation of river 2 is T. S2 It is 0.54 and the grade is III.

[0160] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A river water ecological quality evaluation method based on indicator weight optimization, characterized by: The method includes the following steps: Step S1, collecting various indicator data of the river, the indicator data including water quality element A, water ecological element B and sediment element C, the water quality element A including chemical oxygen demand index A1, ammonia nitrogen index A2 and total phosphorus index A3, the water ecological element B including benthic zoowp index B1, benthic zoowp index B2, zooplankton Shannon-Wiener diversity index B3 and zooplankton richness index B4, the sediment element C including sediment total nitrogen index C1, sediment total phosphorus index C2 and sediment heavy metal potential risk ecological index C3; Step S2: dividing and normalizing each indicator data according to the forward indicator processing sequence and the reverse indicator processing sequence to obtain the forward indicator processing value and the reverse indicator processing value; Step S3: Constructing a comprehensive evaluation model for river water ecological quality including a target layer, a criterion layer, and an indicator layer, wherein the target layer, the criterion layer, and the indicator layer are arranged in a hierarchical manner and in descending order from high to low. The target layer includes the river water ecological quality element S; the criterion layer includes the water quality element A, the water ecological element B, and the sediment element C; and dividing the positive indicator processing values and the negative indicator processing values corresponding to the water quality element A, the water ecological element B, and the sediment element C into each indicator layer; Step S4: Calculate the theoretical weights of various indicator data in combination with the comprehensive evaluation model of river water ecological quality, optimize the theoretical weights using the improved analytic hierarchy process, and modify the comprehensive evaluation model of river water ecological quality to obtain actual weights; S41: Establish an evaluation model, compare the index data of the same layer with the corresponding factors of the upper layer, and obtain multiple evaluation values according to the set AHP scale; S42: Establish a judgment matrix for the evaluation value, use MATLAB to perform hierarchical single sorting on the judgment matrix and calculate the test coefficient CR, perform test coefficient threshold judgment on the test coefficient CR, and calculate the theoretical weight value corresponding to the test coefficient CR that meets the test coefficient threshold condition; S43: According to the corresponding relationship between each evaluation value and each element of the criterion layer, the theoretical weight value of the evaluation model is optimized and modified. The modified weight value is calculated using the SL395-2007 technical standard combined with the classification statistics method. The formula is as follows: Where: Indicates indicator data Y i Correction weight value, Y = A, B, C, i ≤ 4; Indicates the index data Y calculated from m evaluation values i The theoretical weight value, m∈(1,n); The index data Y calculated from nm evaluation values i The theoretical weight value of n represents the total number of evaluation values, and m represents the mth evaluation value; S44: Based on the correlation coefficients between the indicator data, the indicator data corresponding to the correlation coefficients exceeding the set correlation coefficient threshold are selected, and the weight values of the modified weight values of the indicator data are redistributed to eliminate the overlapping parts. After the weight values of all the indicator data corresponding to the correlation coefficients exceeding the set correlation coefficient threshold are redistributed, the remaining indicator data corresponding to the correlation coefficients not exceeding the set correlation coefficient threshold are adjusted accordingly so that the sum of the actual weight values of all the indicator data is 1; Step S5: Calculate the comprehensive index of river water ecological quality based on the positive indicator processing value and the reverse indicator processing value, combined with the actual weight value corresponding to each indicator data, and divide the water ecological quality level according to the comprehensive index of river water ecological quality.

2. A river water ecological quality evaluation method based on indicator weight optimization according to claim 1, characterized in that: The specific calculation process of the positive indicator processing value and the reverse indicator processing value in step S2 is: S21: Divide the data of each indicator to obtain the original value of the positive indicator and the original value of the reverse indicator; S22: normalize the original value of the positive indicator and the original value of the reverse indicator according to the positive indicator processing sequence and the reverse indicator processing sequence to obtain the positive indicator processing value and the reverse indicator processing value. Forward indicator processing sequence: Reverse indicator processing sequence: Where: y′ Zk Represents the kth positive indicator processing value, y′ Zk ∈(0,1);y Zk represents the original value of the kth positive indicator; y Zksu Indicates the upper limit of the kth positive indicator standard; y′ Nj Represents the j-th reverse indicator processing value, y′ Nj ∈(0,1);y Nj represents the original value of the j-th inverse indicator; y Njsu represents the upper limit of the j-th reverse indicator standard; y Njsd Indicates the lower limit of the original value of the j-th inverse indicator.

3. A river water ecological quality evaluation method based on indicator weight optimization according to claim 2, characterized in that: In step S42, a judgment matrix for evaluation values is established, and MATLAB is used to perform hierarchical single sorting on the evaluation values and calculate the test coefficient CR. The specific process of threshold judgment on the test coefficient CR is as follows: S421: Calculate the consistency index CI of the judgment matrix: Where λ max Indicates the maximum eigenvalue of the matrix, n indicates the order of the judgment matrix, and to measure the size of CI, the random consistency index RI is introduced, CI m represents the mth consistency index CI, where m = n; S422: Compare the consistency index CI and the random consistency index RI to calculate the test coefficient CR. The formula is as follows: S423: Compare the test coefficient CR with the test coefficient threshold. If the test coefficient CR is less than 0.1, the judgment matrix passes the consistency test and the theoretical weight value of each indicator data is collected. If the test coefficient CR is greater than 0.1, the judgment matrix is adjusted to make the test coefficient CR less than 0.1, and then the theoretical weight value of each indicator data is calculated.

4. A river water ecological quality evaluation method based on indicator weight optimization according to claim 3, characterized in that: The calculation method of the theoretical weight value in step S423 is as follows: According to the hierarchical distribution of the comprehensive evaluation model of river water ecological quality, the weight value corresponding to each element in the criterion layer is used as the first weight value, and the weight value of each indicator in the indicator layer is used as the second weight value. The first weight value and the second weight value are multiplied to obtain the theoretical weight value.

5. A river water ecological quality evaluation method based on indicator weight optimization according to claim 4, characterized in that: The specific method of redistributing the weight values of the modified weight values of the indicator data in step S44 is: S441: Calculate indicator data Y i and indicator data Y e Correlation coefficient The calculation method is as follows: Where, l xx Indicates indicator data Y i The sum of squares of the mean deviations of the treated values; l yy Indicates indicator data Y e The sum of squares of the mean deviations of the treated values; l xy Indicates indicator data Y i Processing value and indicator data Y e The sum of the products of the mean deviations of the treatment values; S442: Correlation coefficient Compare with the correlation coefficient threshold. If the correlation coefficient If it is greater than the correlation coefficient threshold, the indicator data Y is calculated. i and indicator data Y e The actual weight value after correction: Where: Y = A, B, C, i ≤ 4, e ≤ 4; Indicates indicator data Y i The actual weight value of Indicates indicator data Y e The actual weight value of Indicates indicator data Y i The modified weight value of Indicates indicator data Y e The modified weight value of Indicates indicator data Y i and indicator data Y e Correct the overlap of weight values; S443: The remaining index data Y that have not been independently corrected in step S442 h Make corresponding weighted adjustments so that the sum of the actual weight values of all indicator data is 1. The adjustment formula is as follows: Where: Indicates indicator data Y h The actual weight value of Indicates indicator data Y h The modified weight value of Indicates indicator data Y i The actual weight value of Indicates indicator data Y e The actual weight value of Indicates indicator data Y i The modified weight value of Indicates indicator data Y e The modified weight value of .

6. A river water ecological quality evaluation method based on indicator weight optimization according to claim 5, characterized in that: The calculation formula for the comprehensive index of river water ecological quality evaluation is as follows: S51: Calculate the sub-item evaluation index of the river water ecological quality, which includes the water quality sub-item evaluation index, the water ecology sub-item evaluation index and the sediment sub-item evaluation index. The calculation formula is as follows: Where: T Ax is the water quality sub-item evaluation index of the x-th monitoring section of the river; T Bx is the water ecological sub-item evaluation index of the xth monitoring section of the river; T Cx is the sediment sub-item evaluation index of the x-th monitoring section of the river; Water quality factor A i Indicator data processing value; Water ecological element B of the standard layer i Indicator data processing value; C is the sediment element of the standard layer i Indicator data processing value; Water quality factor A i The actual weight value of the indicator data; Water ecological element B i The actual weight value of the indicator data; Sediment element C i The actual weight value of the indicator data; S52: Calculate the comprehensive index of river water ecological quality evaluation: T S =∑(T Ax +T Bx +T Cx )×(c x / c) Where: T S is the comprehensive index for river water ecological quality evaluation; c represents the total length of the river; c x It indicates the length of the river section from the xth monitoring section to the x-1th monitoring section. When x-1 is 0, it represents the starting point of the river. The c of the xth monitoring section is x represents the length from the x-1th monitoring section to the end of the river. If the river has only one monitoring section, then c x =c; S53: Based on the comprehensive index of river water ecological quality evaluation, the comprehensive index of river water ecological quality evaluation is divided into five levels according to different threshold intervals: Level I, Level II, Level III, Level IV, and Level V; S54: Evaluate the quality level of river water ecology.

Images