CN109657200A - A kind of method of determining lake and reservoir cyanobacterial bloom outburst probability - Google Patents
A kind of method of determining lake and reservoir cyanobacterial bloom outburst probability Download PDFInfo
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- CN109657200A CN109657200A CN201811479966.2A CN201811479966A CN109657200A CN 109657200 A CN109657200 A CN 109657200A CN 201811479966 A CN201811479966 A CN 201811479966A CN 109657200 A CN109657200 A CN 109657200A
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Abstract
The invention discloses a kind of method of determining lake and reservoir cyanobacterial bloom outburst probability, include the following steps: the data for obtaining all algae in water body with indoor identification by sampling on the spot；Water quality data is obtained by apparatus measures；The density and biomass of various algae are obtained according to sampled data；Determine dominant species Main Driving Factors: blue algae bloom probability prediction.Algae and water quality data of the present invention using actual measurement, by identification dominant species, determine Main Driving Factors, finally predict blue algae bloom probability according to blue algae bloom probability prediction model.The present invention does not depend on the measured data of blue algae bloom, has a wide range of application, and plays a significant role for the prevention and treatment of cyanobacterial bloom.
Description
Technical field
The present invention relates to a kind of method more particularly to a kind of methods of determining lake and reservoir cyanobacterial bloom outburst probability.
Background technique
With the development of industrial or agricultural, a large number of nutrients flows into rivers and lakes, and water body eutrophication degree constantly aggravates,
Under the promotion of global warming, blue algae bloom event frequently occurs.The mass propagation of cyanobacteria and rot, influence Dissolved Oxygen in Water and contain
Amount, leads to aquatic animal mortality, aquatic ecosystem is made to be seriously damaged.Cyanobacterial bloom can also generate various lifes
Object toxin, especially Microcystin are most commonly seen and harm is maximum, can damage the devices such as liver, kidney, heart, the sexual gland of vertebrate
Official's tissue and nervous system.The outburst of cyanobacterial bloom not only endangers the health of aquatic ecosystem, more seriously endangers the drinking-water of the mankind
Safety and life and health.
Presently, there are numerous blue algae bloom prediction models, are mostly with the uncertain data-driven model such as neural network
Main, this class model pays attention to the critical judgement of blue algae bloom, cannot analyze it for the environmental aspect that wawter bloom is not broken out and break out wind
Danger and Main Driving Factors.And it by territory restriction, cannot be used in the region that cyanobacteria is not broken out, therefore limit theirs
Use scope cannot effectively forecast blue algae bloom range and explosion time, weaken anticipation and control of the mankind to wawter bloom algae
Ability.Therefore, the analysis of statistical data cyanobacteria dominant species based on cyanobacteria species, based on the main driving of dominant species screening because
Son establishes blue algae bloom probability prediction model without geographical restrictions, determines prevention and treatment of the blue algae bloom risk for cyanobacterial bloom
It is of great significance.
Summary of the invention
In order to solve shortcoming present in above-mentioned technology, the present invention provides a kind of determining lake and reservoir cyanobacterial blooms
Break out the method for probability.
In order to solve the above technical problems, the technical solution adopted by the present invention is that: a kind of determining lake and reservoir cyanobacterial bloom
The method for breaking out probability, includes the following steps:
1) data of all algae in water body are obtained with indoor identification by sampling on the spot；
2) water quality data, including water temperature, pH, conductivity, dissolved oxygen, total nitrogen, total phosphorus are obtained by apparatus measures；
3) density and biomass of various algae are obtained according to sampled data；
4) dominant species Main Driving Factors are determined:
5) blue algae bloom probability is predicted.
Further, in step 3), density is indicated with D, and biomass is indicated with B, calculates each species by formula one
Dominance I；
In formula, i represents i-th kind of species, and a, b are weight.
Further, step 4) determines weight a, b according to formula two.
Further, step 5) is determined using the relationship of Canonical correspondence analysis method analysis dominant species and environmental factor
The Main Driving Factors of dominant species, specifically:
A, blue algae bloom probability is calculated according to formula three；
In formula, P represents blue algae bloom probability, and c is weight；S is Main Driving Factors, SbFor the most suitable of Main Driving Factors
Value；
B, the monitoring range of water quality factor is divided into Pyatyi, calculates the habitat suitability index of each classification, determined
Main Driving Factors most just when range, using the median of the range as Main Driving Factors most just when；
C, Canonical correspondence analysis is done with Main Driving Factors and species data, species-environmental concerns accumulative perception becomes
Change when the value of the first sequence axis is greater than 50%, normalizes to obtain with the absolute value of the Biplot scores value of the first sequence axis
The weight of each driven factor, the variation of species-environmental concerns accumulative perception is when the value of the first sequence axis is less than 50%, with the
The root mean square of the Biplot scores value of one sequence axis and the second sequence axis normalizes to obtain the weight of each driven factor.
To sum up, the outburst probability of cyanobacteria in water body can be determined according to sampled data according to above step.
The beneficial effects of the present invention are:
The present invention is a kind of method of the outburst probability of cyanobacteria in determining water body, and compared with prior art, the present invention passes through
Algae and water quality sampling data analysis dominant species and Main Driving Factors, calculate cyanobacteria according to blue algae bloom probability prediction model
Probability is broken out, it is applied widely, do not depend on the measured data of blue algae bloom.
Specific embodiment
The present invention will be further described in detail with reference to the specific embodiments.
A kind of method of determining lake and reservoir cyanobacterial bloom outburst probability, comprising the following steps:
Step 1: data acquisition:
(1) data of all algae in water body are obtained with indoor identification by sampling on the spot；
(2) water quality datas such as water temperature, pH, conductivity, dissolved oxygen, total nitrogen, total phosphorus are obtained by apparatus measures.
Step 2: dominant species determine:
(1) density and biomass of the various algae obtained according to sampled data, density are indicated with D, biomass B table
Show, the dominance of each species is calculated by formula one, is indicated with I.
Formula one:
In formula, i represents i-th kind of species, and a, b are weight.
(2) weight a, b is determined according to formula two.
Formula two:
Step 3: dominant species Main Driving Factors determine:
Using the relationship of Canonical correspondence analysis method analysis dominant species and environmental factor, the main drive of dominant species is determined
Reason.
Step 4: the prediction of blue algae bloom probability:
(1) blue algae bloom probability is calculated according to formula three.
Formula three:
In formula, P represents blue algae bloom probability, and c is weight.S is Main Driving Factors, SbMost for Main Driving Factors
Just when.
(2) monitoring range of water quality factor is divided into Pyatyi, calculates the habitat suitability index of each classification, determined
Main Driving Factors most just when range, using the median of the range as Main Driving Factors most just when.
(3) Canonical correspondence analysis is done with Main Driving Factors and species data, species-environmental concerns accumulative perception becomes
Change when the value of the first sequence axis is greater than 50%, normalizes to obtain with the absolute value of the Biplot scores value of the first sequence axis
The weight of each driven factor, the variation of species-environmental concerns accumulative perception is when the value of the first sequence axis is less than 50%, with the
The root mean square of the Biplot scores value of one sequence axis and the second sequence axis normalizes to obtain the weight of each driven factor.
To sum up, the outburst probability of cyanobacteria in water body can be determined according to sampled data according to above step.
The algae and water quality data that the present invention makes full use of sampling to obtain, identification dominant species represent algal community with determination
Main Driving Factors calculate blue algae bloom probability according to blue algae bloom probability prediction model, can instruct cyanobacteria preventing and controlling.This
The measured data that invention does not need blue algae bloom can determine the blue algae bloom probability of research water body, and application range is not by research water
Whether body cyanobacteria breaks out limitation.
Basic principles and main features and advantages of the present invention of the invention have been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (4)
1. a kind of method of determining lake and reservoir cyanobacterial bloom outburst probability, it is characterised in that: described method includes following steps:
1) data of all algae in water body are obtained with indoor identification by sampling on the spot；
2) water quality data, including water temperature, pH, conductivity, dissolved oxygen, total nitrogen, total phosphorus are obtained by apparatus measures；
3) density and biomass of various algae are obtained according to sampled data；
4) dominant species Main Driving Factors are determined:
5) blue algae bloom probability is predicted.
2. the method for determining lake and reservoir cyanobacterial bloom outburst probability according to claim 1, it is characterised in that: the step
It is rapid 3) in, density is indicated with D, and biomass is indicated with B, and the dominance I of each species is calculated by formula one；
In formula, i represents i-th kind of species, and a, b are weight.
3. the method for determining lake and reservoir cyanobacterial bloom outburst probability according to claim 2, it is characterised in that: the step
It is rapid that weight a, b 4) is determined according to formula two.
4. the method for determining lake and reservoir cyanobacterial bloom outburst probability according to claim 3, it is characterised in that: the step
It is rapid 5) using the relationship of Canonical correspondence analysis method analysis dominant species and environmental factor, determine the main drivings of dominant species because
Son, specifically:
A, blue algae bloom probability is calculated according to formula three；
In formula, P represents blue algae bloom probability, and c is weight；S is Main Driving Factors, SbFor Main Driving Factors most just when；
B, the monitoring range of water quality factor is divided into Pyatyi, calculates the habitat suitability index of each classification, determined main
Driven factor most just when range, using the median of the range as Main Driving Factors most just when；
C, Canonical correspondence analysis is done with Main Driving Factors and species data, the variation of species-environmental concerns accumulative perception exists
When the value of first sequence axis is greater than 50%, normalize to obtain each drive with the absolute value of the Biplot scores value of the first sequence axis
The weight of reason, the variation of species-environmental concerns accumulative perception is when the value of the first sequence axis is less than 50%, with first row
The root mean square of the Biplot scores value of sequence axis and the second sequence axis normalizes to obtain the weight of each driven factor.
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Cited By (4)
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| CN110414051A (en) * | 2019-06-27 | 2019-11-05 | 长江水资源保护科学研究所 | A kind of water demand for natural service accounting method inhibiting river wawter bloom |
| CN110991047A (en) * | 2019-12-04 | 2020-04-10 | 珠江水利委员会珠江水利科学研究院 | Rapid early warning method for cyanobacterial bloom of water reservoir in water source area |
| CN112345473A (en) * | 2020-10-23 | 2021-02-09 | 中国水利水电科学研究院 | Method for identifying dissolved oxygen control factors of thermal stratification reservoir |
| CN115795367A (en) * | 2023-01-29 | 2023-03-14 | 湖南大学 | Algal bloom outbreak prediction method based on machine learning and application |
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| CN112345473A (en) * | 2020-10-23 | 2021-02-09 | 中国水利水电科学研究院 | Method for identifying dissolved oxygen control factors of thermal stratification reservoir |
| CN112345473B (en) * | 2020-10-23 | 2021-08-24 | 中国水利水电科学研究院 | Method for identifying dissolved oxygen control factors of thermal stratification reservoir |
| CN115795367A (en) * | 2023-01-29 | 2023-03-14 | 湖南大学 | Algal bloom outbreak prediction method based on machine learning and application |
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