CN113887635A - Basin similarity classification method and classification device - Google Patents

Basin similarity classification method and classification device Download PDF

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CN113887635A
CN113887635A CN202111170738.9A CN202111170738A CN113887635A CN 113887635 A CN113887635 A CN 113887635A CN 202111170738 A CN202111170738 A CN 202111170738A CN 113887635 A CN113887635 A CN 113887635A
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张珂
牛杰帆
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Abstract

The invention discloses a basin similarity classification method and a classification device, wherein the classification method comprises the following steps: collecting hydrological, meteorological and underlying surface data of a preset basin, extracting meteorological and underlying surface factors, and constructing characteristic indexes; setting a correlation threshold value to determine a weather and underlying surface similarity index; performing grid scale hydrological meteorological zoning in a preset basin based on a SOM and FCM fusion algorithm; constructing a sub-basin scale clustering set in a meteorological homogeneous region, and classifying the sub-basins by using a fusion model; according to the weather subareas where the drainage basins are located and the sub-drainage basin types contained in the drainage basins, fuzzy membership degrees of the sub-drainage basins are utilized to construct drainage basin similarity comprehensive measurement, the similarity degree between the drainage basins is evaluated by using a maximum and minimum closeness method, and the similarity between the drainage basins is identified. The invention provides theoretical basis for parameter transplantation and parameter selection of the data-free basin, and promotes the deep development of the work of regionalization of model parameters, hydrological early warning and forecasting and the like of the data-free area.

Description

Basin similarity classification method and classification device
Technical Field
The invention belongs to the technical field of hydrology, and particularly relates to a basin similarity classification method and a basin similarity classification device, which are mainly used for similar basin discrimination and hydrology early warning and forecasting and the like in data-free areas.
Background
In recent years, under the influence of climate change and human activities, extreme weather events show an increasing and increasing trend, and serious water damage disasters such as torrential floods, debris flows and the like are frequent. Particularly for the regions with deficient hydrologic data, due to the lack of effective hydrologic monitoring means, the detection of disasters is insufficient, the disasters happen rapidly, the damage is serious, and the sustainable development of the economy and the society is severely restricted. Aiming at the hydrologic forecast problem of the data-free area, the international hydrologic scientific association (IAHS) started the PUB international hydrologic program in 2002, and the program aims to explore a new method for converting the hydrologic work of the data-free area from a traditional method for calibrating observed data into a mechanism analysis. The method for determining model parameters of a data-free area is a regionalization method, namely, a data basin similar to a target basin is selected, and the model parameters of the data-free basin are calculated by utilizing the calibrated model parameters of the data basin, so that hydrologic prediction of the target basin is realized.
With the development of technologies such as remote sensing and GIS, climate and underlying surface data are easy to obtain, the hydrologic similarity is calculated in an approximate mode by mining effective information of the structure characteristics of the non-data basin and utilizing the similarity of the climate and the basin characteristics, and the possibility is provided for runoff prediction of the non-data areas. In recent years, computer technology is rapidly developed, pattern recognition, clustering algorithm and fuzzy mathematics are greatly developed from research theory to practical application, and technical support is provided for hydrologic similarity research. Therefore, the comprehensive criterion for establishing the similarity between the basin weather and the underlying surface and the method for establishing the high-dimensional heterogeneous characteristic space hydrologic similarity area identification have certain theoretical basis and practical significance.
The selection of the traditional similar drainage basin usually depends on the experience of a decision maker, has no uniform quantization standard and has stronger uncertainty in practical application. Meanwhile, a general similarity index combination is not formed at present, a specific similarity index may not capture comprehensive hydrologic rules, and how to select the similarity index and construct a similar basin evaluation system becomes a key step in hydrologic similarity and parameter regional research. Therefore, the present invention provides a general watershed similarity classification method and a classification apparatus.
Disclosure of Invention
The invention aims to provide a watershed similarity classification method and a watershed similarity classification device, which provide a theoretical basis for selection and parameter transplantation of similar watersheds in a data-free area.
In order to achieve the purpose, the invention adopts the following technical scheme: a watershed similarity classification method is characterized by comprising the following steps:
step 1, collecting hydrological, meteorological and underlying surface data of a preset basin, extracting meteorological and underlying surface factors, and constructing characteristic indexes;
step 2, selecting characteristic indexes by using distance correlation coefficients of the hydrological characteristics of the preset basin and weather and underlying surface factors, and setting a correlation threshold value to determine weather and underlying surface similarity indexes;
step 3, constructing a grid scale clustering set based on a SOM and FCM fusion algorithm, and carrying out hydrological meteorological zoning in a preset basin;
step 4, constructing a sub-basin scale clustering set in a meteorological homogeneous area based on the hydrometeorology partition result, and classifying the sub-basins by using a fusion model;
and 5, constructing a basin similarity comprehensive measurement by utilizing the fuzzy membership degree of the sub-basins according to the weather subareas where the basins are located and the included sub-basin categories. And (4) evaluating the similarity degree between the flow domains by using a maximum and minimum closeness method, and identifying the similarity between the flow domains.
The step 1 comprises the following steps:
step 11, collecting hydrological, meteorological and underlying surface data of a preset watershed, wherein the hydrological data comprise runoff coefficients and multi-year average daily runoff of the preset watershed, the meteorological data comprise multi-year average monthly precipitation, multi-year average monthly potential evaporation and multi-year average monthly temperature data of the preset watershed, and the underlying surface data comprise topographic characteristics, vegetation types, soil types and land utilization types of the preset watershed;
step 12, according to the meteorological data of the preset basin, calculating the following meteorological factors as characteristic indexes: the average annual wetting index, the maximum difference of annual monthly wetting indexes, the snow falling ratio, the average annual temperature, the maximum difference of annual monthly temperature and the snow falling time ratio are as follows:
Figure BDA0003293014020000021
Figure BDA0003293014020000022
Im,r＝max(MI(1,2,...12))-min(MI(1,2,...12))
Figure BDA0003293014020000031
Figure BDA0003293014020000032
Tm,r＝max(T(1,2,...12))-min(T(1,2,...12))
Figure BDA0003293014020000033
wherein MI (t) is the wetting index at month t; i ism，Im,r，fs，Tm，Tm,r，DsRespectively representing the annual average wetting index, the annual monthly wetting index maximum difference, the snow falling ratio, the annual average air temperature, the annual monthly air temperature maximum difference and the snow falling time ratio; p (t), EP(t), t (t) is the annual average precipitation in month tth, the annual average potential evaporation in month tth and the annual average temperature value in month tth, respectively; d (t) days of month t; t is0For a temperature threshold, the form of precipitation below this temperature is snow, which is set by this patent to be 0 ℃.
And step 13, extracting the following underlying surface factors as characteristic indexes including topographic characteristic factors, soil vegetation characteristic factors and shape characteristic factors according to the underlying surface information of the preset watershed. The terrain characterization factors include an average elevation, a maximum elevation difference, an area-elevation integral, an area elevation curve slope, an average terrain index, and an average grade of the predetermined watershed. The method comprises the following steps that (1) area-elevation integration represents the quality of earth surface materials in a preset drainage basin, the slope of an area-elevation curve reflects the topographic relief degree of the preset drainage basin, and the area-elevation integral and the topographic relief degree are calculated through the area-elevation curve; the average terrain index is an arithmetic average of unit grid terrain indexes in a preset basin, and the calculation formula is as follows:
Figure BDA0003293014020000034
Figure BDA0003293014020000035
Figure BDA0003293014020000036
in the formula, HI and ASAnd TI is an area-elevation integral value, an area-elevation curve slope and an average topographic index respectively; the area-elevation curve f (x) is a fitting curve consisting of x ═ a/A and y ═ H/H, and a is the area above a certain contour line in a predetermined flow domain; h is the height difference between the contour line and the lowest point in the preset drainage basin; a is the total area of the preset drainage basin; h is the maximum relative height difference in the preset basin; f (0.2) and f (0.8) represent the corresponding relative height differences on the area-elevation curve at area-to-elevation ratios of 0.2 and 0.8, respectively; a isiThe water collecting area of the ith unit grid in the preset drainage basin is obtained; beta is aiIs the slope of the ith unit grid; n is the total number of grids in the predetermined flow domain;
the soil vegetation characteristic factors comprise the sand grain content, the particle content, the clay grain content and the normalized vegetation index of the watershed soil; the shape characteristic factors comprise the river basin area, the river basin length, the river basin form factor, the river basin elongation ratio and the river basin and river network density.
The step 2 comprises the following steps:
the method for selecting the distance correlation coefficient to select the characteristic indexes of the weather and underlying surface factors and setting the correlation threshold value to determine the similarity indexes of the weather and underlying surface comprises the following steps:
calculating distance correlation coefficients between different factors:
Figure BDA0003293014020000041
in the formula, X is a certain underlying surface or meteorological characteristic factor sequence, and Y is a hydrological characteristic factor sequence; dCor (X, Y) is the distance correlation coefficient between the X and Y sequences; dCov (X, Y) is the distance covariance of X and Y sequences; dVar (X) and dVar (Y) are respectively the standard deviation of the distance between the X sequence and the Y sequence;
and setting a threshold value omega, and clustering by using the underlying surface or meteorological characteristic factor with the distance correlation coefficient dCor (X, Y) > omega as a similarity index. The threshold omega is selected to be 0.5, and similarity indexes determined according to the distance correlation coefficient are respectively as follows:
the annual average precipitation, the annual average wetting index and the annual monthly wetting index maximum difference and other 3 meteorological indexes; and 7 underlay surface indexes such as the sand grain content of the soil in the drainage basin, the clay grain content of the drainage basin, the normalized vegetation index of the drainage basin, the area of the drainage basin, the length of the drainage basin, the form factor of the drainage basin, the elongation ratio of the drainage basin and the like.
The step 3 comprises the following steps:
a hydrological meteorological clustering set is constructed on a grid scale, a SOM and FCM fusion model is used for training and partitioning, and the clustering process is divided into two stages: the first stage, carrying out primary clustering by using an SOM algorithm, and obtaining a competition output layer after training is finished; and in the second stage, the corresponding weight vector of the node of the SOM output layer is used as a clustering sample of the FCM algorithm, and iterative computation is carried out until a convergence condition is reached.
In step 3, the clustering process based on the SOM-FCM fusion algorithm is as follows:
step 31, initializing the SOM neural network: setting the competition layer structure of the SOM neural network, the initial domain radius delta (0) and the initial learning rate
Figure BDA0003293014020000059
Number of iterations k and total number of iterations ks(ii) a Carrying out normalization processing on the N-dimensional similarity index to obtain a training sample G; setting weight vector W corresponding to each neuron in competition layerj(k)＝(Wj,1(k),Wj,2(k),...,Wj,N(k) (j ═ 1, 2.. times, M), initializing a weight vector, where M is the number of contention layer neurons, and k has an initial value of 0;
step 32, inputting training samples: randomly selecting the ith sample Gi＝(G1,G2,...,GN)TTo the input layer;
step 33, search for the winning neuron and calculate GiAnd Wj(k) The neuron with the smallest distance is selected as a winning neuron r;
Figure BDA0003293014020000051
step 34, adjusting the neuron connection weight: connected weight vector W for neurons in neighborhood of winning neuron rjAdjusting;
Figure BDA0003293014020000052
in the formula (I), the compound is shown in the specification,
Figure BDA0003293014020000053
is the learning rate;
Figure BDA0003293014020000054
is the radius of the field of the winning neuron r;
step 35, training an iteration counter k to k + 1; updating
Figure BDA0003293014020000055
And
Figure BDA0003293014020000056
step 36, repeating steps 32-35 until the training iteration count k reaches the preset total number k of iterationssObtaining an SOM output layer neural network;
step 37, using SOM to output the weight W of each neuron of the neural network of the layerjSetting the clustering number c, membership factor m, limited error epsilon and maximum iteration number k of the FCM algorithm as the input vector of the FCM algorithmfInitializing a membership matrix U, and setting the iteration number k as 0;
step 38, calculating a clustering center according to the membership matrix U, wherein the ith clustering center CiComprises the following steps:
Figure BDA0003293014020000057
in the formula (I), the compound is shown in the specification,
Figure BDA0003293014020000058
is an input vector WjFor CiDegree of membership of;
and 39, updating the membership matrix U according to the clustering center:
Figure BDA0003293014020000061
in the formula (d)ijIs an input vector WjFor CiThe Euclidean distance of; dqjIs an input vector WjFor CqEuclidean distance of, q ═ 1, 2.., c;
step 310, training an iteration counter k ═ k + 1;
step 311, repeat steps 39-310 until | U(k)-U(k+1)| < epsilon or the iteration number k reaches the maximum iteration number kf。
In step 3, the SOM network structure is selected according to the principle of minimum error, quantitative error QE and topological error TE are selected for evaluation, and two indexes can express the quality of SOM neural network clustering:
Figure BDA0003293014020000062
TE＝∑v(Gi)
in which QE is the input sample and corresponding gainMean relative distance between victory neurons, Wr(Gi) Is GiA weight vector of the corresponding winning neuron; TE is the degree of proximity of samples adjacent to the input space in the competition layer network, if the samples are GiThe neighboring samples remain adjacent in the output space, v (G)i) Is 1.
In step 3, the clustering number c of the SOM-FCM algorithm is determined according to the Davies-Bouldin index (DBI), and the clustering effect under different clustering numbers is evaluated to optimize the c:
Figure BDA0003293014020000063
Figure BDA0003293014020000064
wherein c is the number of clusters; miThe number of samples belonging to the category i; ciAs the cluster center of class i, CqA cluster center of class q, wherein i 1,2,.., c, q 1,2,.., c;
Figure BDA0003293014020000065
class i and class q sample points to C, respectivelyiAnd CqThe average distance of (c).
And step 5, constructing a basin similarity comprehensive measurement by using the fuzzy membership degree of the sub-basins according to the weather subareas where the basins are located and the included sub-basin categories. The degree of similarity between the domains is evaluated by using the maximum and minimum closeness method, and the domain B1And basin B2The similarity of (a) is expressed as:
Figure BDA0003293014020000071
Figure BDA0003293014020000072
in the formula (I), the compound is shown in the specification,
Figure BDA0003293014020000073
is a drainage basin B1And basin B2The similarity of (2);
Figure BDA0003293014020000074
represents basin B1And B2Weighting the membership degree of the area of the basin class i;
Figure BDA0003293014020000075
and
Figure BDA0003293014020000076
respectively is a drainage basin B1And B2The ratio of the area of the jth sub-basin to the total area of the basin; n is a radical ofB1And NB2Is a drainage basin B1And B2The number of sub-watersheds in (a);
Figure BDA0003293014020000077
and
Figure BDA0003293014020000078
represents basin B1And B2The fuzzy membership degree of the jth sub-basin to the basin class i; the A and V respectively represent the calculation of the minimum value and the maximum value.
The device for classifying the basin similarity is characterized by comprising a processor and a memory; stored in the memory are programs or instructions which are loaded and executed by the processor to implement the steps of the classification method according to any one of claims 1 to 9.
The invention has the following beneficial results: according to the basin similarity classification method and the basin similarity classification device, the basin similarity index system is constructed by calculating the distance correlation coefficient among basin factors; the SOM and FCM fusion model is adopted to divide the hydrometeorology homogeneous region, and on the basis, the fusion model is utilized to realize sub-basin classification; and constructing a basin similarity comprehensive measurement by combining the maximum and minimum closeness method with the fuzzy membership of the sub-basins, and realizing comprehensive evaluation of the similarity degree between the basins. The invention adopts a model combining SOM and FCM to cluster the hydrometeorology partitions and the sub-watersheds, the fusion model has the characteristics of SOM algorithm self-organization and strong nonlinear mapping, and simultaneously, the concept of fuzzy set is fused, and nonlinear and highly heterogeneous hydrological data with difficult boundary definition can be explained. Meanwhile, the optimal clustering number is selected based on the internal indexes of the clusters, so that the high efficiency and the objective stability of the clustering result are ensured. The similarity between the drainage basins is expressed through the fuzzy membership degree, an objective quantitative criterion is provided for the identification of the similar drainage basins, a theoretical basis can be provided for the selection and parameter transplantation of the non-data drainage basin parametrization drainage basins, and the deep development of the work of model parameter regionalization, hydrological early warning and forecasting and the like of the non-data areas is promoted.
Drawings
FIG. 1 is a schematic diagram of a calculation flow of a watershed similarity classification method provided by the present invention;
FIG. 2 is a plot of the correlation coefficients of the hydrological features with the distance of the meteorological and underlying surface features in an exemplary embodiment;
FIG. 3 is a test chart of the SOM neural network structure of the hydrometeorology partition in the embodiment;
FIG. 4 is a diagram of the hydrometeorology partition number-Davies-Bouldin index change relationship in the exemplary embodiment;
FIG. 5 is a diagram of the relationship between the cluster number of the sub-domains-Davies-Bouldin index change in the embodiment;
fig. 6 is a diagram illustrating exemplary inter-basin similarity in an exemplary embodiment.
Detailed Description
The invention is further described with reference to the accompanying drawings and specific examples.
It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the present invention provides a watershed similarity classification method, which includes the following steps:
step 1, collecting hydrological, meteorological and underlying surface data of a preset basin, extracting meteorological and underlying surface factors, and constructing characteristic indexes;
step 2, selecting characteristic indexes by using distance correlation coefficients of the hydrological characteristics of the preset basin and weather and underlying surface factors, and setting a correlation threshold value to determine weather and underlying surface similarity indexes;
step 3, constructing a grid scale clustering set based on a SOM and FCM fusion algorithm, and carrying out hydrological meteorological zoning in a preset basin;
step 4, constructing a sub-basin scale clustering set in a meteorological homogeneous area based on the hydrometeorology partition result, and classifying the sub-basins by using a fusion model;
and 5, constructing a basin similarity comprehensive measurement by utilizing the fuzzy membership degree of the sub-basins according to the weather subareas where the basins are located and the included sub-basin categories. And (4) evaluating the similarity degree between the flow domains by using a maximum and minimum closeness method, and identifying the similarity between the flow domains.
Taking 10 drainage basins of Tunxi, Bishui river, Ma ferry king, Banqiao, big river dam, Chenhe, camp, big pavilion, Shidan and Suilde as typical drainage basins, and carrying out the research on the similarity between the small drainage basin classification and the drainage basins. The method specifically comprises the following steps:
step 1, collecting hydrological, meteorological and underlying surface data of a preset basin, extracting meteorological and underlying surface factors, and constructing characteristic indexes;
the method comprises the following steps:
step 11, collecting hydrological, meteorological and underlying surface data of a preset watershed, wherein the hydrological data comprise runoff coefficients and multi-year average daily runoff of the preset watershed, the meteorological data comprise multi-year average monthly precipitation, multi-year average monthly potential evaporation and multi-year average monthly temperature data of the preset watershed, and the underlying surface data comprise topographic characteristics, vegetation types, soil types and land utilization types of the preset watershed;
step 12, extracting the following meteorological factors as characteristic indexes according to meteorological data of a preset basin: the average annual wetting index, the maximum difference of annual monthly wetting indexes, the snow falling ratio, the average annual temperature, the maximum difference of annual monthly temperature and the snow falling time ratio are as follows:
Figure BDA0003293014020000091
Figure BDA0003293014020000092
Im,r＝max(MI(1,2,...12))-min(MI(1,2,...12)) (3)
Figure BDA0003293014020000093
Figure BDA0003293014020000094
Tm,r＝max(T(1,2,...12))-min(T(1,2,...12)) (6)
Figure BDA0003293014020000095
wherein MI (t) is the wetting index at month t; i ism，Im,r，fs，Tm，Tm,r，DsRespectively representing the annual average wetting index, the annual monthly wetting index maximum difference, the snow falling ratio, the annual average air temperature, the annual monthly air temperature maximum difference and the snow falling time ratio; p (t), EP(t), t (t) is the annual average precipitation in month tth, the annual average potential evaporation in month tth and the annual average temperature value in month tth, respectively; d (t) days of month t; t is0For a temperature threshold, the form of precipitation below this temperature is snow, which is set by this patent to be 0 ℃.
And step 13, extracting the following underlying surface factors as characteristic indexes including topographic characteristic factors, soil vegetation characteristic factors and shape characteristic factors according to the underlying surface information of the preset watershed. The topographic characteristic factor includes an average elevation (H) of the predetermined drainage basinm) Maximum height difference (H)r) Area-elevation integral (HI), area-elevation slope (A)s) Average terrain index (Ti) and average slope (β). Wherein the content of the first and second substances,the area-elevation integral represents the quality of the earth surface in the predetermined drainage basin, the slope of the area-elevation curve reflects the topographic relief degree of the predetermined drainage basin, and the area-elevation curve and the topographic relief degree are calculated through the area-elevation curve; the average terrain index is an arithmetic average of unit grid terrain indexes in a preset basin, and the calculation formula is as follows:
Figure BDA0003293014020000101
Figure BDA0003293014020000102
Figure BDA0003293014020000103
in the formula, HI and ASAnd TI is an area-elevation integral value, an area-elevation curve slope and an average topographic index respectively; the area-elevation curve f (x) is a fitting curve consisting of x ═ a/A and y ═ H/H, and a is the area above a certain contour line in a predetermined flow domain; h is the height difference between the contour line and the lowest point in the preset drainage basin; a is the total area of the preset drainage basin; h is the maximum relative height difference in the preset basin; f (0.2) and f (0.8) represent the corresponding relative height differences on the area-elevation curve at area-to-elevation ratios of 0.2 and 0.8, respectively; a isiThe water collecting area of the ith unit grid in the preset drainage basin is obtained; beta is aiIs the slope of the ith unit grid; n is the total number of grids in the predetermined flow domain;
the soil vegetation characteristic factors comprise drainage basin soil Sand content (Sand), drainage basin powder content (Clay), drainage basin Clay content (Silt) and drainage basin normalized vegetation index (NDVI); the shape feature factors include the watershed area (a), the watershed length (L), the watershed form factor (Rf), the watershed elongation ratio (Re), and the watershed network density (Rd).
Step 2, selecting characteristic indexes by using distance correlation coefficients of the preset watershed hydrological characteristics and meteorological and underlying surface factors, drawing a characteristic factor distance correlation coefficient diagram, constructing a distance correlation matrix of a watershed runoff coefficient (psi) and watershed characteristics as shown in fig. 2, and determining model simulation training indexes;
the method for selecting the distance correlation coefficient to select the characteristic indexes of the weather and underlying surface factors and setting the correlation threshold value to determine the similarity indexes of the weather and underlying surface comprises the following steps:
calculating distance correlation coefficients between different factors:
Figure BDA0003293014020000111
in the formula, X is a certain underlying surface or meteorological characteristic factor sequence, and Y is a hydrological characteristic factor sequence; dCor (X, Y) is the distance correlation coefficient between the X and Y sequences; dCov (X, Y) is the distance covariance of X and Y sequences; dVar (X) and dVar (Y) are standard deviations of distances between the X sequence and the Y sequence, respectively.
And setting a threshold value omega, and clustering by using the underlying surface or meteorological characteristic factor with the distance correlation coefficient dCor (X, Y) > omega as a similarity index. The threshold omega is selected to be 0.5, and similarity indexes determined according to the distance correlation coefficient are respectively as follows:
the annual average precipitation, the annual average wetting index and the annual monthly wetting index maximum difference and other 3 meteorological indexes; and 7 underlay surface indexes such as the sand grain content of the soil in the drainage basin, the clay grain content of the drainage basin, the normalized vegetation index of the drainage basin, the area of the drainage basin, the length of the drainage basin, the form factor of the drainage basin, the elongation ratio of the drainage basin and the like.
Step 3, constructing a grid scale clustering set based on a SOM and FCM fusion algorithm, and carrying out hydrological meteorological zoning in a preset basin;
a hydrological meteorological clustering set is constructed on a grid scale, a SOM and FCM fusion model is used for training and partitioning, and the clustering process is divided into two stages: the first stage, carrying out primary clustering by using an SOM algorithm, and obtaining a competition output layer after training is finished; and in the second stage, the corresponding weight vector of the node of the SOM output layer is used as a clustering sample of the FCM algorithm, and iterative computation is carried out until a convergence condition is reached.
The clustering process based on the SOM-FCM fusion algorithm comprises the following steps:
initialization of the SOM neural network, step 31And (3) conversion: setting the competition layer structure of the SOM neural network, the initial domain radius delta (0) and the initial learning rate
Figure BDA0003293014020000112
Number of iterations k and total number of iterations ks(ii) a Carrying out normalization processing on the N-dimensional similarity index to obtain a training sample G; setting weight vector W corresponding to each neuron in competition layerj(k)＝(Wj,1(k),Wj,2(k),...,Wj,N(k) (j ═ 1, 2.. times, M), initializing a weight vector, where M is the number of contention layer neurons, and k has an initial value of 0;
step 32, inputting training samples: randomly selecting the ith sample Gi＝(G1,G2,...,GN)TTo the input layer;
step 33, search for the winning neuron and calculate GiAnd Wj(k) The neuron with the smallest distance is selected as a winning neuron r;
Figure BDA0003293014020000121
step 34, adjusting the neuron connection weight, and carrying out connection weight vector W on neurons in the neighborhood of the winning neuron rjAdjusting;
Figure BDA0003293014020000122
in the formula (I), the compound is shown in the specification,
Figure BDA0003293014020000125
is the learning rate;
Figure BDA0003293014020000126
is the radius of the field of the winning neuron r;
step 35, training an iteration counter k to k + 1; updating
Figure BDA0003293014020000127
And delta(k)；
Step 36, repeating steps 32-35 until the training iteration count k reaches the preset total number k of iterationssObtaining an SOM output layer neural network;
step 37, using SOM to output the weight W of each neuron of the neural network of the layerjSetting the clustering number c, membership factor m, limited error epsilon and maximum iteration number k of the FCM algorithm as the input vector of the FCM algorithmfInitializing a membership matrix U, and setting the iteration number k as 0;
step 38, calculating a clustering center according to the membership matrix U, wherein the ith clustering center CiComprises the following steps:
Figure BDA0003293014020000123
in the formula (I), the compound is shown in the specification,
Figure BDA0003293014020000128
is an input vector WjFor CiDegree of membership of;
and 39, updating the membership matrix U according to the clustering center:
Figure BDA0003293014020000124
in the formula (d)ijIs an input vector WjFor CiThe Euclidean distance of;
step 310, training an iteration counter k ═ k + 1;
step 311, repeat steps 39-310 until | U(k)-U(k+1)| < epsilon or the iteration number k reaches the maximum iteration number kf。
The SOM network structure in the SOM-FCM algorithm is selected according to the principle of minimum error, quantitative error QE and topological error TE are selected for evaluation, and two indexes can express the quality of SOM neural network clustering:
Figure BDA0003293014020000131
TE＝∑v(Gi) (17)
where QE is the average relative distance between the input sample and the corresponding winning neuron, Wr(Gi) Is GiA weight vector of the corresponding winning neuron; TE is the degree of proximity of samples adjacent to the input space in the competition layer network, if the samples are GiThe neighboring samples remain adjacent in the output space, v (G)i) Is 1.
FIG. 3 shows the test result of the hydrometeorology partition SOM neural network structure, and the number of the finally selected model output nodes is 14 × 22.
The clustering number c of the SOM-FCM algorithm is determined according to the Davies-Bouldin index (DBI), and c is optimized by evaluating clustering effects under different clustering numbers:
Figure BDA0003293014020000132
Figure BDA0003293014020000133
wherein c is the number of clusters; miThe number of samples belonging to the category i; ciAs the cluster center of class i, CqA cluster center of class q, wherein i 1,2,.., c, q 1,2,.., c;
Figure BDA0003293014020000134
class i and class q sample points to C, respectivelyiAnd CqThe average distance of (c).
And (4) optimizing k, wherein when k is 7, DBI reaches the minimum value, and the optimization process of the hydrometeorology partition number k is shown in FIG. 4.
Step 4, constructing a sub-basin scale clustering set in a meteorological homogeneous area based on the hydrometeorology partition result, and classifying the sub-basins by using a fusion model;
and classifying the sub-basin by using an SOM-FCM algorithm, selecting a SOM network structure by selecting a quantization error QE and a topology error TE according to a minimum error principle, evaluating clustering effects under different clustering numbers according to the DBI, and optimizing k. When k is 9, DBI reaches the minimum value, and the preferred process of the sub-basin classification number k is shown in fig. 5.
And 5, constructing a basin similarity comprehensive measurement by utilizing the fuzzy membership degree of the sub-basins according to the weather subareas where the basins are located and the sub-basins contained in the weather subareas. The similarity between the drainage domains is evaluated by using a maximum and minimum closeness method, the similarity between the drainage domains is identified, the drainage domain with a large pavilion is taken as a target drainage domain, and the typical similarity between the drainage domains is shown in fig. 6.
The degree of similarity between the domains is evaluated by using the maximum and minimum closeness method, and the domain B1And basin B2The similarity of (a) is expressed as:
Figure BDA0003293014020000141
Figure BDA0003293014020000142
in the formula (I), the compound is shown in the specification,
Figure BDA0003293014020000143
is a drainage basin B1And basin B2The similarity of (2);
Figure BDA0003293014020000144
represents basin B1And B2Weighting the membership degree of the area of the basin class i;
Figure BDA0003293014020000145
and
Figure BDA0003293014020000146
respectively is a drainage basin B1And B2The ratio of the area of the jth sub-basin to the total area of the basin; n is a radical ofB1And NB2Is a streamDomain B1And B2The number of sub-watersheds in (a);
Figure BDA0003293014020000147
and
Figure BDA0003293014020000148
represents basin B1And B2The fuzzy membership degree of the jth sub-basin to the basin class i; the A and V respectively represent the calculation of the minimum value and the maximum value.
The embodiment of the invention also provides a watershed similarity classification device, which comprises a processor and a memory; the memory has stored therein a program or instructions that is loaded and executed by the processor to implement the steps of the classification method in the above embodiments.

Claims (10)

1. A watershed similarity classification method is characterized by comprising the following steps:
step 1, collecting hydrological, meteorological and underlying surface data of a preset basin, extracting meteorological and underlying surface factors, and constructing characteristic indexes;
step 2, selecting characteristic indexes by using distance correlation coefficients of the hydrological characteristics of the preset basin and weather and underlying surface factors, and setting a correlation threshold value to determine weather and underlying surface similarity indexes;
step 3, constructing a grid scale clustering set based on a SOM and FCM fusion algorithm, and carrying out hydrological meteorological zoning in a preset basin;
step 4, constructing a sub-basin scale clustering set in a meteorological homogeneous area based on the hydrometeorology partition result, and classifying the sub-basins by using a fusion model;
and 5, constructing a basin similarity comprehensive measurement by utilizing the fuzzy membership degree of the sub-basins according to the weather subareas where the basins are located and the included sub-basin categories. And (4) evaluating the similarity degree between the flow domains by using a maximum and minimum closeness method, and identifying the similarity between the flow domains.
2. The method of claim 1, wherein step 1 comprises:
step 11, collecting hydrological, meteorological and underlying surface data of a preset watershed, wherein the hydrological data comprise runoff coefficients and multi-year average daily runoff of the preset watershed, the meteorological data comprise multi-year average monthly precipitation, multi-year average monthly potential evaporation and multi-year average monthly temperature data of the preset watershed, and the underlying surface data comprise topographic characteristics, vegetation types, soil types and land utilization types of the preset watershed;
step 12, according to the meteorological data of the preset basin, calculating the following meteorological factors as characteristic indexes: the average annual wetting index, the maximum difference of annual monthly wetting indexes, the snow falling proportion, the average annual temperature, the maximum difference of annual monthly temperature and the snow falling time proportion;
Figure FDA0003293014010000011
Figure FDA0003293014010000012
Im,r＝max(MI(1,2,...12))-min(MI(1,2,...12))
Figure FDA0003293014010000021
Figure FDA0003293014010000022
Tm,r＝max(T(1,2,...12))-min(T(1,2,...12))
Figure FDA0003293014010000023
wherein MI (t) is the wetting index at month t; i ism，Im,r，fs，Tm，Tm,r，DsRespectively representing the annual average wetting index, the annual monthly wetting index maximum difference, the snow falling ratio, the annual average air temperature, the annual monthly air temperature maximum difference and the snow falling time ratio; p (t), EP(t), t (t) is the annual average precipitation in month tth, the annual average potential evaporation in month tth and the annual average temperature value in month tth, respectively; d (t) days of month t; t is0For a temperature threshold, the form of precipitation below this temperature is snow, which is set by this patent to be 0 ℃.
And step 13, extracting the following underlying surface factors as characteristic indexes including topographic characteristic factors, soil vegetation characteristic factors and shape characteristic factors according to the underlying surface information of the preset watershed. The terrain feature factors include an average elevation, a maximum elevation difference, an area-elevation integral, an area-elevation curve slope, an average terrain index, and an average grade of the predetermined watershed. The method comprises the following steps that (1) area-elevation integration represents the quality of earth surface materials in a preset drainage basin, the slope of an area-elevation curve reflects the topographic relief degree of the preset drainage basin, and the area-elevation integral and the topographic relief degree are calculated through the area-elevation curve; the average terrain index is an arithmetic average of unit grid terrain indexes in a preset basin, and the calculation formula is as follows:
Figure FDA0003293014010000024
Figure FDA0003293014010000025
Figure FDA0003293014010000026
in the formula, HI and ASAnd TI is an area-elevation integral, an area-elevation curve slope and an average topographic index respectively; the area-elevation curve f (x) is a fitting curve consisting of x ═ a/A and y ═ H/H, and a is the area above a certain contour line in a predetermined flow domain; h is the height difference between the contour line and the lowest point in the preset drainage basin; a is the total area of the predetermined drainage basin(ii) a H is the maximum relative height difference in the preset basin; f (0.2) and f (0.8) represent the corresponding relative height differences on the area-elevation curve at area-to-elevation ratios of 0.2 and 0.8, respectively; a isiThe water collecting area of the ith unit grid in the preset drainage basin is obtained; beta is aiIs the slope of the ith unit grid; n is the total number of grids in the predetermined flow domain;
the soil vegetation characteristic factors comprise the sand grain content, the particle content, the clay grain content and the normalized vegetation index of the watershed soil; the shape characteristic factors comprise the river basin area, the river basin length, the river basin form factor, the river basin elongation ratio and the river basin and river network density.
3. The method of claim 1, wherein in step 2, the distance correlation coefficient is selected to select the feature index of the weather and underlying surface factors, and the correlation threshold is set to determine the similarity index of the weather and underlying surface by:
calculating distance correlation coefficients between different factors:
Figure FDA0003293014010000031
in the formula, X is a certain underlying surface or meteorological characteristic factor sequence, and Y is a hydrological characteristic factor sequence; dCor (X, Y) is the distance correlation coefficient between the X and Y sequences; dCov (X, Y) is the distance covariance of X and Y sequences; dVar (X) and dVar (Y) are standard deviations of distances between the X sequence and the Y sequence, respectively.
And setting a threshold value omega, and clustering by using the underlying surface or meteorological characteristic factor with the distance correlation coefficient dCor (X, Y) > omega as a similarity index.
4. The method of claim 3,
the threshold omega is selected to be 0.5, and the similarity index determined according to the distance correlation coefficient is as follows:
the annual average precipitation, the annual average wetting index and the annual monthly wetting index maximum difference and other 3 meteorological indexes; and 7 underlay surface indexes such as the sand grain content of the soil in the drainage basin, the clay grain content of the drainage basin, the normalized vegetation index of the drainage basin, the area of the drainage basin, the length of the drainage basin, the form factor of the drainage basin, the elongation ratio of the drainage basin and the like.
5. The method according to claim 1, wherein in the step 3, a hydrological meteorological clustering set is constructed on a grid scale, a SOM and FCM fusion model is used for training partitioning, and the clustering process is divided into two stages: the first stage, carrying out primary clustering by using an SOM algorithm, and obtaining a competition output layer after training is finished; and in the second stage, the corresponding weight vector of the node of the SOM output layer is used as a clustering sample of the FCM algorithm, and iterative computation is carried out until a convergence condition is reached.
6. The method according to claim 1, wherein in the step 3, the clustering process based on the SOM-FCM fusion algorithm is as follows:
step 31, initializing the SOM neural network: setting the competition layer structure of the SOM neural network, the initial domain radius delta (0) and the initial learning rate
Figure FDA0003293014010000041
Number of iterations k and total number of iterations ks(ii) a Carrying out normalization processing on the N-dimensional similarity index to obtain a training sample G; setting weight vector W corresponding to each neuron in competition layerj(k)＝(Wj,1(k),Wj,2(k),...,Wj,N(k) (j ═ 1, 2.. times, M), initializing a weight vector, where M is the number of contention layer neurons, and k has an initial value of 0;
step 32, inputting training samples: randomly selecting the ith sample Gi＝(G1,G2,...,GN)TTo the input layer;
step 33, finding winning neurons: calculation of GiAnd Wj(k) The neuron with the smallest distance is selected as a winning neuron r;
Figure FDA0003293014010000042
step 34, adjusting the neuron connection weight: connected weight vector W for neurons in neighborhood of winning neuron rjAdjusting;
Figure FDA0003293014010000043
in the formula (I), the compound is shown in the specification,
Figure FDA0003293014010000044
is the learning rate;
Figure FDA0003293014010000045
is the radius of the field of the winning neuron r;
step 35, training an iteration counter k to k + 1; updating
Figure FDA0003293014010000046
And δ (k);
step 36, repeating steps 32-35 until the training iteration number k reaches the preset total iteration number ksObtaining an SOM output layer neural network;
step 37, using SOM to output the weight W of each neuron of the neural network of the layerjSetting the clustering number c, membership factor m, limited error epsilon and maximum iteration number k of the FCM algorithm as the input vector of the FCM algorithmfInitializing a membership matrix U, and setting the iteration number k as 0;
step 38, calculating a clustering center according to the membership matrix U, wherein the ith clustering center CiComprises the following steps:
Figure FDA0003293014010000051
in the formula (I), the compound is shown in the specification,
Figure FDA0003293014010000052
is an input vector WjFor CiDegree of membership of;
and 39, updating the membership matrix U according to the clustering center:
Figure FDA0003293014010000053
in the formula (d)ijIs an input vector WjFor CiThe Euclidean distance of; dqjIs an input vector WjFor CqEuclidean distance of, q ═ 1, 2.., c;
step 310, training an iteration counter k ═ k + 1;
step 311, repeat steps 39-310 until | U(k)-U(k+1)| < epsilon or the iteration number k reaches the maximum iteration number kf。
7. The method according to claim 6, wherein in step 3, the SOM network structure is selected according to the principle of minimum error, and the quantitative error QE and the topological error TE are selected for evaluation, and two indexes can express the quality of SOM neural network clustering:
Figure FDA0003293014010000054
TE＝∑v(Gi)
where QE is the average relative distance between the input sample and the corresponding winning neuron, Wr(Gi) Is GiA weight vector of the corresponding winning neuron; TE is the degree of proximity of samples adjacent to the input space in the competition layer network, if the samples are GiThe neighboring samples remain adjacent in the output space, v (G)i) Is 1.
8. The method according to claim 7, wherein in step 3, the clustering number c of the SOM-FCM algorithm is determined according to the Davies-Bouldin index, and c is optimized by evaluating the clustering effect under different clustering numbers:
Figure FDA0003293014010000061
Figure FDA0003293014010000062
wherein c is the number of clusters; miThe number of samples belonging to the category i; ciAs the cluster center of class i, CqA cluster center of class q, wherein i 1,2,.., c, q 1,2,.., c;
Figure FDA0003293014010000063
class i and class q sample points to C, respectivelyiAnd CqThe average distance of (c).
9. The method according to claim 1, wherein in the step 5, the fuzzy membership of the sub-watersheds is used to construct the comprehensive watershed similarity measure according to the weather partition in which the watershed is located and the included sub-watershed category. The degree of similarity between the domains is evaluated by using the maximum and minimum closeness method, and the domain B1And basin B2The similarity of (a) is expressed as:
Figure FDA0003293014010000064
Figure FDA0003293014010000065
in the formula (I), the compound is shown in the specification,
Figure FDA0003293014010000066
is a drainage basin B1And basin B2The similarity of (2);
Figure FDA0003293014010000067
represents basin B1And B2Weighting the membership degree of the area of the basin class i;
Figure FDA0003293014010000068
and
Figure FDA0003293014010000069
respectively is a drainage basin B1And B2The ratio of the area of the jth sub-basin to the total area of the basin; n is a radical ofB1And NB2Is a drainage basin B1And B2The number of sub-watersheds in (a);
Figure FDA00032930140100000610
and
Figure FDA00032930140100000611
represents basin B1And B2The fuzzy membership degree of the jth sub-basin to the basin class i; the A and V respectively represent the calculation of the minimum value and the maximum value.
10. The device for classifying the basin similarity is characterized by comprising a processor and a memory; stored in the memory are programs or instructions which are loaded and executed by the processor to implement the steps of the classification method according to any one of claims 1 to 9.
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