CN109272246A - A kind of river clustering method based on algal bloom - Google Patents
A kind of river clustering method based on algal bloom Download PDFInfo
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Abstract
The invention discloses a kind of river clustering method based on algal bloom, comprising the following steps: establish river algal bloom index system；Determine system index weights；Parameter score；River cluster.The present invention comprehensively considers river algal bloom index, including river form, water body power, water body physical and chemical factor, water body optics, water nutrition index, establish river algal bloom index system, and each index weights are determined using subjective and objective combination weights method, finally river cluster is carried out using hierarchical clustering method, to which the river of different zones, different characteristic be sorted out, the universal law for understanding river algal bloom, provides a kind of effective technical management tool for wawter bloom risk profile and wawter bloom prevention and control.
Description
Technical field
The present invention relates to a kind of clustering methods, and in particular to a kind of river clustering method based on algal bloom.
Background technique
With social development, mankind's activity is frequent, and great variety occurs for river channel ecology environment, and different degrees of wawter bloom causes society
It can pay close attention to.Algal bloom is response of the aquatic ecosystem to water eutrophication, and algal bloom directly destroys ecology of water landscape,
The survival and development for inhabiting biology in water area ecological environment are endangered, or even threaten human health.It is quick-fried for research river wawter bloom
Hair mechanism, environment scholar carry out from river form, river water body hydrodynamic characterisitic, water temperature, nutritive salt characteristic, sociales etc.
Research finds algal bloom key factor are as follows: slow-flowing stream fluidised form；It is suitable for illumination, water temperature condition；Sufficient nitrogen and phosphorous nutrient.
Currently, for river algal bloom Mechanism Study mainly for single river, for different zones different characteristic
River, if there is cluster, if with similar wawter bloom risk and mechanism even prevention and control measure, lack research.
For the universal law for understanding the river algal bloom of different zones different characteristic, the present invention proposes a kind of quick-fried based on wawter bloom
The river New Clustering of hair.
Summary of the invention
To solve the deficiencies in the prior art, it is an object of that present invention to provide one kind comprehensively consider river form, water body power,
Water body physical and chemical factor, water body optics, water nutrition index, using the entitled mode of subjective and objective combination weights method, science, reasonably
The method for carrying out river cluster.
The technical solution adopted by the present invention are as follows:
A kind of river clustering method based on algal bloom, comprising the following steps:
S1, river algal bloom index system, including first class index layer and two-level index layer are established；
The first class index layer is made of several first class index, including river morphological index, water body dynamic index, water body reason
Change level of factor, water body optical index, water nutrition index；
The two-level index layer is made of several two-level index,
River morphological index includes length index, area index, wriggles and spend index,
Water body dynamic index includes water level index, velocity parameters, the vertical coefficient of stability index of water body,
Water body physical and chemical factor index includes temperature index, pH value index, conductivity indices, oxidation-reduction potential index, DO
Index,
Water body optical index includes transparency index, turbidity index, light attenuation coefficient index, euphotic zone depth index, mixes
Layer depth index is closed,
Water nutrition index includes total nitrogen (TN) index, total phosphorus (TP) index, dissolved silicon hydrochlorate (D-Si) index, nitrogen phosphorus
(TN/TP) than index, silicon nitrogen ratio (D-Si/TN) index, silicon phosphorus ratio (D-Si/TP) index, chlorophyll (Chla) index；
S2, using subjective and objective combination weights method, determine each two-level index weight in each river；
S3, each first class index score in each river is determined using weighted sum method based on each two-level index weight in each river；
S4, using hierarchical clustering method, carry out river cluster.
Subjective and objective combination weights method in above-mentioned steps S2 includes subjective weighting method, objective weighted model；
Determine system index weights, specifically:
Assuming that two-level index number is J:
wj=α aj+(1-α)bjJ=1,2 ..., J, 0≤α≤1 (1)
In formula (1), wjFor the comprehensive weight of j-th of index, ajAnd bjThe subjective weight of respectively j-th index and objective
Weight, α is preference coefficient, according to policymaker to the preference of different enabling legislations；
The subjective weighting method uses expert survey, and n experts understand river algal bloom index system, and anonymity is finger
It marks j and assigns power, ajIt is averaged；
The objective weighted model uses Information Entropy, and steps are as follows:
A1: nondimensionalization processing
Nondimensionalization processing is carried out to index system data using Maximum Approach, it is assumed that there is N river to participate in clustering,
Assuming that two-level index number is J, matrix [x is constitutedij']N×J, matrix [x is obtained after nondimensionalization processingij]N×J:
In formula (2), max xj' it is maximum value under same index；
A2: parameter entropy
K=(lnN)-1 (4)
In formula (3), fijThe specific gravity of the index is accounted for for lower i-th river of j-th of index；
In formula (4), K is constant；
In formula (5), HjFor the entropy of j-th of index；
A3: parameter entropy weight
Weighted sum method is used in above-mentioned steps S3, determines each first class index score in each river, specifically:
Assuming that first class index number is P,
In formula (7), SipFor i-th p-th of river first class index score, l is that the 1st second level refers under p-th of first class index
Corresponding j value is marked, m is two-level index number under p-th of first class index.
River cluster is carried out using hierarchical clustering method in above-mentioned steps S4, steps are as follows:
B1: distance calculates
Assuming that there is N river to participate in clustering, N class is established,It calculates, is obtained apart from square through distance
Battle array D(0),(0)To cluster original state；
The method that the distance calculates, including knearest neighbour method, longest distance method, group average distance method；
The knearest neighbour method, it is assumed that N1 and N2 is two rivers, N1 the and N2 shortest distance are as follows:
DN1,N2=min (du,v) (8)
In formula (8), DN1,N2The shortest distance between all first class index in all first class index in the river N1 and the river N2,
U is p-th of the river N1 first class index score, and v is p-th of the river N2 first class index score, du,vFor the distance between u and v；
The longest distance method, N1 and N2 longest distance are as follows:
DN1,N2=max (du,v) (9)
The group average distance method, N1 and N2 group average distance are as follows:
In formula (10), P is first class index number；
B2: new classification is established
Assuming that min0For Distance matrix D(0)Minimum value:
min0=min (D(0)) (11)
Assuming that min0It isWithThe distance between two classes,WithMerge into one kindOther classes not merged establish new classification:
B3: new classifying distance calculates
It calculatesWithThe distance between, obtain Distance matrix D(1)；
B4: it computes repeatedly
Step B2-B3 is repeated, until N river cluster is 1, then stops clustering；
B5: cluster
Dendrogram is drawn, determines clusters number.
Above-mentioned sinuous degree is river bending degree:
In formula (12), β is the degree that wriggles, L1For Talweg length, L2For river upstream and downstream point-to-point transmission linear distance.
The above-mentioned vertical coefficient of stability of water body:
In formula (13), χ is the vertical coefficient of stability of water body, and g is acceleration of gravity, ρHFor bottom water body density, ρ0For superficial water
Volume density, ρavgFor the vertical averag density of water body, H is the river depth of water；
The water body density is calculated by the corresponding water body density of water temperature and water body silt content:
In formula (14), ρ is water body density, ρTFor the corresponding water body density of water temperature, ρsFor silt bulk density, δ is water body containing sand
Amount；
The corresponding water body density of the water temperature:
In formula (15), T is water temperature.
Above-mentioned light attenuation coefficient:
In formula (16), ε is light attenuation coefficient, and z is from the river water surface to measured place depth, and E (z) is to utilize underwater light quantum
Instrument measures the irradiation level of depth z, and E (0) is water surface irradiation level；
The euphotic zone depth:
In formula (17), zeuFor euphotic zone depth；
The layer depth takes river surface temperature to decline 0.5 DEG C of corresponding depth of water.
The invention has the beneficial effects that:
A kind of river clustering method based on algal bloom of the invention, comprehensively considers river algal bloom important indicator,
Including river form, water body power, water body physical and chemical factor, water body optics, water nutrition index, river algal bloom index is established
System, and each index weights are determined using subjective and objective combination weights method, river cluster finally is carried out using hierarchical clustering method, thus
The river of different zones, different characteristic is sorted out, understand river algal bloom universal law, be wawter bloom risk profile and
Wawter bloom prevention and control provide a kind of effective technical management tool.
Detailed description of the invention
Fig. 1 is a kind of flow chart of river clustering method based on algal bloom of the invention；
Fig. 2 is the structural schematic diagram of algal bloom index system in river of the invention；
Fig. 3 is river cluster result of the invention.
Specific embodiment
Specific introduce is made to the present invention below in conjunction with the drawings and specific embodiments.
As shown in Figure 1, the invention discloses a kind of river clustering method based on algal bloom, comprising the following steps:
S1, river algal bloom index system is established；
S2, system index weights are determined；
S3, parameter score；
S4, river cluster.
Algal bloom index system in river in step S1, including first class index layer and two-level index layer；
First class index layer includes river morphological index, water body dynamic index, water body physical and chemical factor index, water body optical index
With water nutrition index；
Two-level index layer is made of several two-level index；River morphological index includes length index, area index and wriggles
Index is spent, water body dynamic index includes that water level index, velocity parameters and the vertical coefficient of stability index of water body, water body physical and chemical factor refer to
Mark includes temperature index, pH value index, conductivity indices, oxidation-reduction potential index and DO index, and water body optical index includes
Transparency index, turbidity index, light attenuation coefficient index, euphotic zone depth index and layer depth index, water nutrition refer to
Mark includes total nitrogen (TN) index, total phosphorus (TP) index, dissolved silicon hydrochlorate (D-Si) index, nitrogen phosphorus (TN/TP) than index, silicon nitrogen
Than (D-Si/TN) index, silicon phosphorus ratio (D-Si/TP) index and chlorophyll (Chla) index；
The degree that wriggles is river bending degree:
In formula (12), β is the degree that wriggles, L1For Talweg length, L2For river upstream and downstream point-to-point transmission linear distance；
The vertical coefficient of stability of water body:
In formula (13), χ is the vertical coefficient of stability of water body, and g is acceleration of gravity, ρHFor bottom water body density, ρ0For superficial water
Volume density, ρavgFor the vertical averag density of water body, H is the river depth of water；
Water body density is calculated by the corresponding water body density of water temperature and water body silt content:
In formula (14), ρ is water body density, ρTFor the corresponding water body density of water temperature, ρsFor silt bulk density, δ is water body containing sand
Amount；
The corresponding water body density of water temperature:
In formula (15), T is water temperature；
Light attenuation coefficient:
In formula (16), ε is light attenuation coefficient, and z is from the river water surface to measured place depth, and E (z) is to utilize underwater light quantum
Instrument measures the irradiation level of depth z, and E (0) is water surface irradiation level；
Euphotic zone depth:
In formula (17), zeuFor euphotic zone depth；
Layer depth takes river surface temperature to decline 0.5 DEG C of corresponding depth of water.
System index weights are determined in step S2, it is assumed that two-level index number is J, using subjective and objective combination weights method:
wj=α aj+(1-α)bjJ=1,2 ..., J, 0≤α≤1 (1)
In formula (1), wjFor the comprehensive weight of j-th of index, ajAnd bjThe subjective weight of respectively j-th index and objective
Weight, α is preference coefficient, according to policymaker to the preference of different enabling legislations；
Subjective weighting method uses expert survey, and n experts understand river algal bloom index system, and anonymity is index j
Assign power, ajIt is averaged；
Objective weighted model uses Information Entropy, and steps are as follows:
A1: nondimensionalization processing
Nondimensionalization processing is carried out to index system data using Maximum Approach, it is assumed that there is N river to participate in clustering,
Assuming that two-level index number is J, matrix [x is constitutedij']N×J, matrix [x is obtained after nondimensionalization processingij]N×J:
In formula (2), maxxj' it is maximum value under same index；
A2: parameter entropy
K=(ln N)-1 (4)
In formula (3), fijThe specific gravity of the index is accounted for for lower i-th river of j-th of index；
In formula (4), K is constant；
In formula (5), HjFor the entropy of j-th of index；
A3: parameter entropy weight
Parameter score in step S3, it is assumed that first class index number is P, determines that each river is each using weighted sum method
First class index score:
In formula (7), SipFor i-th p-th of river first class index score, l is that the 1st second level refers under p-th of first class index
Corresponding j value is marked, m is two-level index number under p-th of first class index.
River clusters in step S4, carries out river cluster using hierarchical clustering method, steps are as follows:
B1: distance calculates
Assuming that there is N river to participate in clustering, N class is established,It calculates, is obtained apart from square through distance
Battle array D(0),(0)To cluster original state；Knearest neighbour method, longest distance method, group average distance may be selected in the method that distance calculates
Method；
Knearest neighbour method, it is assumed that N1 and N2 is two rivers, N1 the and N2 shortest distance are as follows:
DN1,N2=min (du,v) (8)
In formula (8), DN1,N2The shortest distance between all first class index in all first class index in the river N1 and the river N2,
U is p-th of the river N1 first class index score, and v is p-th of the river N2 first class index score, du,vFor the distance between u and v；
Longest distance method, N1 and N2 longest distance are as follows:
DN1,N2=max (du,v) (9)
Group average distance method, N1 and N2 group average distance are as follows:
In formula (10), P is first class index number；
B2: new classification is established
Assuming that min0For Distance matrix D(0)Minimum value:
min0=min (D(0)) (11)
Assuming that min0It isWithThe distance between two classes,WithMerge into one kindOther classes not merged establish new classification:
B3: new classifying distance calculates
It calculatesWithThe distance between, obtain Distance matrix D(1)；
B4: it computes repeatedly
Return step B2, computes repeatedly, until N river cluster is 1, then stops clustering；
B5: cluster
Dendrogram is drawn, determines clusters number.
Embodiment 1:
By taking the numerous tributaries of a certain large-scale river-like reservoir as an example, the river cluster based on algal bloom is carried out, steps are as follows:
1. establishing river algal bloom index system
Algal bloom index system in river is as shown in Fig. 2, include first class index layer and two-level index layer；
First class index layer includes river morphological index, water body dynamic index, water body physical and chemical factor index, water body optical index
With water nutrition index, amount to 5 first class index；
Two-level index layer is made of two-level index, and river morphological index includes that length index, area index and the degree that wriggles refer to
Mark, water body dynamic index includes water level index, velocity parameters and the vertical coefficient of stability index of water body, water body physical and chemical factor index packet
Temperature index, pH value index, conductivity indices, oxidation-reduction potential index and DO index are included, water body optical index includes transparent
Spend index, turbidity index, light attenuation coefficient index, euphotic zone depth index and layer depth index, water nutrition index packet
Total nitrogen (TN) index, total phosphorus (TP) index, dissolved silicon hydrochlorate (D-Si) index, nitrogen phosphorus (TN/TP) are included than index, silicon nitrogen ratio
(D-Si/TN) index, silicon phosphorus ratio (D-Si/TP) index and chlorophyll (Chla) index amount to 23 two-level index.
2. determining system index weights
It shares 10 rivers and participates in clustering, by taking the 1st article of river as an example, river algal bloom index value such as 1 institute of table
Show；
Each index weights of expert survey, each index weights of Information Entropy and each index weights of subjective and objective combination weights method such as table
Shown in 2, preference factor alpha takes 0.5.
1 river algal bloom index value of table (by taking the 1st article of river as an example)
2 index weights of table
3. parameter score
It shares 10 rivers and participates in clustering, 23 two-level index, river algal bloom first class index score such as 3 institute of table
Show.
3 river algal bloom first class index score of table
4. river clusters
It is carried out using knearest neighbour method apart from calculating, Distance matrix D(0)As shown in table 4.
4 Distance matrix D of table(0)
Distance matrix D(0)In, 0.02 is minimum value, therefore one kind is merged into river 2, river 5 and river 6, it is assumed that close
And 1, other classes not merged establish new classification, Distance matrix D(1)As shown in table 5.
It is carried out using knearest neighbour method apart from calculating, by river 1 for merging 1 (river 2, river 5 and river 6), point
Not Ji Suan river 1 and river 2, river 5 and river 6 difference, be minimized.
5 Distance matrix D of table(1)
Distance matrix D(1)In, 0.03 is minimum value, therefore one kind is merged into river 2, river 5, river 6 and river 10, false
It is set as merging 2, other classes not merged establish new classification, Distance matrix D(2)As shown in table 6.
6 Distance matrix D of table(2)
Distance matrix D(2)In, 0.05 is minimum value, therefore river 2, river 5, river 6, river 7 and river 10 are merged into
One kind, other classes not merged establish new classification.And so on, when 10 rivers finally being merged into 1 (cluster is 1),
End of clustering.
As shown in figure 3, the river cluster result based on algal bloom,
When river is divided into two classes, river 3, river 8 are one kind, remaining river is one kind；
When river is divided into three classes, river 1 is one kind, and river 3, river 8 are one kind, remaining river is one kind.
Claims (7)
1. a kind of river clustering method based on algal bloom, which comprises the following steps:
S1, river algal bloom index system, including first class index layer and two-level index layer are established；
The first class index layer is made of several first class index, including river morphological index, water body dynamic index, water body physics and chemistry because
Sub- index, water body optical index, water nutrition index；
The two-level index layer is made of several two-level index,
River morphological index includes length index, area index, wriggles and spend index,
Water body dynamic index includes water level index, velocity parameters, the vertical coefficient of stability index of water body,
Water body physical and chemical factor index includes temperature index, pH value index, conductivity indices, oxidation-reduction potential index, DO index,
Water body optical index includes transparency index, turbidity index, light attenuation coefficient index, euphotic zone depth index, mixed layer
Depth index,
Water nutrition index includes total nitrogen (TN) index, total phosphorus (TP) index, dissolved silicon hydrochlorate (D-Si) index, nitrogen phosphorus (TN/
TP) than index, silicon nitrogen ratio (D-Si/TN) index, silicon phosphorus ratio (D-Si/TP) index, chlorophyll (Chla) index；
S2, using subjective and objective combination weights method, determine each two-level index weight in each river；
S3, each first class index score in each river is determined using weighted sum method based on each two-level index weight in each river；
S4, using hierarchical clustering method, carry out river cluster.
2. a kind of river clustering method based on algal bloom according to claim 1, which is characterized in that the step S2
In subjective and objective combination weights method include subjective weighting method, objective weighted model；
Determine system index weights, specifically:
Assuming that two-level index number is J:
wj=α aj+(1-α)bjJ=1,2 ..., J, 0≤α≤1 (1)
In formula (1), wjFor the comprehensive weight of j-th of index, ajAnd bjThe subjective weight and objective weight of respectively j-th index,
α is preference coefficient, according to policymaker to the preference of different enabling legislations；
The subjective weighting method uses expert survey, and n experts understand river algal bloom index system, and anonymity is index j
Assign power, ajIt is averaged；
The objective weighted model uses Information Entropy, and steps are as follows:
A1: nondimensionalization processing
Nondimensionalization processing is carried out to index system data using Maximum Approach, it is assumed that there is N river to participate in clustering, it is assumed that
Two-level index number is J, constitutes matrix [xij']N×J, matrix [x is obtained after nondimensionalization processingij]N×J:
In formula (2), maxxj' it is maximum value under same index；
A2: parameter entropy
K=(lnN)-1 (4)
In formula (3), fijThe specific gravity of the index is accounted for for lower i-th river of j-th of index；
In formula (4), K is constant；
In formula (5), HjFor the entropy of j-th of index；
A3: parameter entropy weight
3. a kind of river clustering method based on algal bloom according to claim 1, which is characterized in that the step S3
It is middle to use weighted sum method, determine each first class index score in each river, specifically:
Assuming that first class index number is P,
In formula (7), SipFor i-th p-th of river first class index score, l is the 1st two-level index pair under p-th of first class index
The j value answered, m are two-level index number under p-th of first class index.
4. a kind of river clustering method based on algal bloom according to claim 1, which is characterized in that the step S4
Middle to carry out river cluster using hierarchical clustering method, steps are as follows:
B1: distance calculates
Assuming that there is N river to participate in clustering, N class is established,It is calculated through distance, obtains Distance matrix D(0), (0) is cluster original state；
The distance calculates, including knearest neighbour method, longest distance method, group average distance method；
The knearest neighbour method, it is assumed that N1 and N2 is two rivers, N1 the and N2 shortest distance are as follows:
DN1,N2=min (du,v) (8)
In formula (8), DN1,N2The shortest distance between all first class index in all first class index in the river N1 and the river N2, u are
P-th of the river N1 first class index score, v are p-th of the river N2 first class index score, du,vFor the distance between u and v；
The longest distance method, N1 and N2 longest distance are as follows:
DN1,N2=max (du,v) (9)
The group average distance method, N1 and N2 group average distance are as follows:
In formula (10), P is first class index number；
B2: new classification is established
Assuming that min0For Distance matrix D(0)Minimum value:
min0=min (D(0)) (11)
Assuming that min0 isWithThe distance between (i=1,2 ..., N, r=1,2 ..., N, i ≠ r) two classes,With
Merge into one kindOther classes not merged establish new classification:Q=N-2；
B3: new classifying distance calculates
It calculatesWithThe distance between, obtain Distance matrix D(1)；
B4: it computes repeatedly
Step B2-B3 is repeated, until N river cluster is 1, then stops clustering；
B5: cluster
Dendrogram is drawn, determines clusters number.
5. a kind of river clustering method based on algal bloom according to claim 1, which is characterized in that described to wriggle
Degree is river bending degree:
In formula (12), β is the degree that wriggles, L1For Talweg length, L2For river upstream and downstream point-to-point transmission linear distance.
6. a kind of river clustering method based on algal bloom according to claim 1, which is characterized in that the water body hangs down
To the coefficient of stability:
In formula (13), χ is the vertical coefficient of stability of water body, and g is acceleration of gravity, ρHFor bottom water body density, ρ0It is close for surface water
Degree, ρavgFor the vertical averag density of water body, H is the river depth of water；
The water body density is calculated by the corresponding water body density of water temperature and water body silt content:
In formula (14), ρ is water body density, ρTFor the corresponding water body density of water temperature, ρsFor silt bulk density, δ is water body silt content；
The corresponding water body density of the water temperature:
In formula (15), T is water temperature.
7. a kind of river clustering method based on algal bloom according to claim 1, which is characterized in that the optical attenuation
Coefficient:
In formula (16), ε is light attenuation coefficient, and z is from the river water surface to measured place depth, and E (z) is to be surveyed using underwater quanta meter
The irradiation level of depth z is obtained, E (0) is water surface irradiation level；
The euphotic zone depth:
In formula (17), zeuFor euphotic zone depth；
The layer depth takes river surface temperature to decline 0.5 DEG C of corresponding depth of water.
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| CN106202960A (en) * | 2016-07-21 | 2016-12-07 | 沈阳环境科学研究院 | A kind of health assessment method based on Lake Water ecosystem |
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| CN112802069B (en) * | 2020-12-30 | 2023-07-04 | 北京师范大学 | Determination method for water-blocking dike cofferdam of shallow lake |
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