CN111784034A - Screening and detecting technology of key environmental factors influencing Chilo squid fishery in Chili sea area - Google Patents
Screening and detecting technology of key environmental factors influencing Chilo squid fishery in Chili sea area Download PDFInfo
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Abstract
The invention discloses a screening and detecting technology for key environmental factors influencing Chilo squid fishery in Chili sea area, which comprises the following steps: s1, processing fishery fishing data of Chile sea area Chilo squid, wherein the data comprise operation time, operation positions, fishing yield and fishing Nu force, S2, verifying simulation effect, S3, comparing and analyzing contribution conditions of different environment variables of each month to Chilo squid fishery distribution, S4, matching actual fishery data of each month with key environment factor data of the actual fishery data, S5, drawing key environment variables as horizontal coordinates, S6, reestablishing a MaxEnt model to simulate potential distribution of the Chilo squid fishery, and selecting key environment factors of each month to evaluate and predict the Chile sea area Chilo squid fishery. According to the method, the sensitivity difference of the biological characteristics of the squid liver to the environment is considered, so that the environment variables selected when the squid liver fishery in Chile sea area is predicted in future are more reasonable and scientific, and the reliability of a forecasting model is enhanced.
Description
Technical Field
The invention relates to environmental impact assessment and fishery prediction methods of spatial-temporal distribution of squid liver, in particular to a method for screening and detecting key environmental factors of squid liver in Chile sea area based on a maximum entropy model.
Background
Dosidicus gigas (Dosidicus gigas) is a commercially developed cephalopod variety, is widely distributed in the southeast Pacific ocean area, has extremely high fishing yield and occupies a high proportion of the total fishing yield of cephalopods, and currently developed fisheries have Chile, Peru, equator and the like, while Chile fisheries are one of the most important fisheries for catching Dosidicus gigas in China. The squid liver is a short life cycle species of one year, the population of the squid liver is very sensitive to environmental change, so when the environment in the habitat of the squid liver changes, the population of the squid liver can rapidly react, the abundance and the spatial distribution of resources can rapidly change in a short time, and therefore the fishing yield of the squid liver is obviously influenced by the environment, the yield fluctuation is obvious, and the difference between the year and the month is obvious. In the research of resource abundance and fishery distribution of the squid liver responding to environmental changes, a plurality of analysis results ignore environmental change in months, and actually environmental factors in different months have different influence degrees on the squid liver, namely, a key environmental factor with higher influence degree and a non-key environmental factor with lower influence degree exist. Therefore, a relation model for screening key environmental factors of Chile sea area squid liver fisheries is established, and the evaluation and prediction of the squid liver fisheries in the sea area are carried out, so that the method has important guiding significance for the ocean squid fishing industry in the southeast Pacific sea area of China.
At present, several methods and models for detecting Chile sea area squid liver fisheries exist, and whether the selected environment variables are key environment variables in the research time or not is not considered in the methods and models, so that the precision of the model or method for detecting the fishery is not high. According to the method, based on 12 environmental data including water temperatures of different water layers (including 0m,25m, 50m, 100m, 150m, 200m, 300m, 400m and 500m), sea surface heights, sea surface salinity, mixed layer depth and the like, key environmental factors are screened on the premise that influence differences of the factors in different time periods are fully considered, the prediction performance of the Chilodochus praecox in Chilolisse sea area is improved, and the model method can be used for accurately detecting the Chilodochus praecox in Chilodochus dorsalis in Dongnan Pacific sea area in China by using a Toyota ocellator.
Disclosure of Invention
In order to solve the problems, the invention researches the influence degree of 12 environmental data such as water temperature of different water layers (including 0m,25m, 50m, 100m, 150m, 200m, 300m, 400m and 500m), sea surface height, sea surface salinity, mixed layer depth and the like on the potential fishery distribution of the squid liver under different time conditions, and provides a technical method for screening and detecting key environmental factors influencing the squid liver fishery in Chili sea area.
The invention is realized by the following technical scheme:
a screening and detecting technology for key environmental factors influencing a Chile sea area squid liver fishery comprises the following steps:
s1, processing fishery fishing data of Chilean sea area Chilo-squid, wherein the data comprises operation time (year and month), operation position (longitude and latitude), fishing yield (unit: ton) and fishing Nu force (counted by operation times), processing all environment data into graph layer data by ArcGis10.2 software, simulating potential fishery distribution of Chilean sea area Chilo-squid by combining the fishery data and the environment data after processing by using a MaxEnt model,
s2, visualizing and classifying the existing probability distribution results of the squid liver in each month by ArcGis10.2 software, classifying the existing probability distribution results by different color regions, defining the existing probability of the squid liver as Habitat Suitability Index (HSI), representing potential fishery distribution, superposing the potential fishery distribution indexes with actual fishery distribution data, verifying simulation effect, taking the size of AUC value in model simulation results as an index for measuring model precision,
s3, comparing and analyzing the contribution condition of different environment variables of each month to the distribution of the Dosidicus gigas fishery, selecting the variables with the contribution ratio of three before ranking according to the contribution ratio to be regarded as the key environment factors influencing the space-time distribution of the Dosidicus gigas fishery in the month,
s4, matching the actual fishery data of each month with key environment factor data of the actual fishery data, defining the operation times as the catching effort force, drawing a frequency distribution graph with the key environment variable as a horizontal coordinate and the catching effort force as a vertical coordinate by using a frequency distribution method, calculating the appropriate range of the key environment variable when the Dosidicus gigas is actually distributed,
s5, drawing a response curve graph with the key environment variable as an abscissa and the adaptive probability of the Dosidicus gigas under the condition of single environment variable as an ordinate, calculating the appropriate range of the corresponding key environment variable when the adaptive probability is more than 0.4 under the simulation condition, comparing the appropriate range with the appropriate range of the corresponding environment variable when the adaptive probability is actually distributed,
and S6, selecting fishery data and environment data of different years, reestablishing a MaxEnt model to simulate potential distribution of the Dosidicus gigas fishery, and selecting key environment factors of each month to evaluate and predict the Dosidicus gigas fishery in Chile sea areas.
Preferably, the step S2 defines the possible existence probability of the squid liver as a Habitat Suitability Index (HSI).
Preferably, the existence probability of the squid liver in the step S2 is that the model combines all environment variables and screens out key factors according to the contribution rate of each variable to perform fishery simulation and verification.
Preferably, in the step S2, an area value (area ≦ undercut, AUC) under a receiver operating characteristic Curve (ROC) automatically generated in the model running process is used as an index for measuring the accuracy of the model, specifically: when the potential distribution of the squid does not coincide with the actual distribution of the squid, the AUC value is 0; when the potential distribution of the model is completely consistent with the actual distribution, namely under an ideal state, the AUC value is 1; and judging the accuracy of the model according to the AUC value automatically generated by the model.
Preferably, the number of the environmental variables is not limited in the step S3, and the first three variables with the highest contribution rate are selected as the key environmental factors of the month according to the contribution situation of each environmental variable to the species distribution in different time periods.
Preferably, the selecting of the key environmental factors for each month in step S3 is to select the three top-ranked variables as the key environmental factors for the month in turn only according to the descending order of the contribution rate of each environmental variable for each month, and the key environmental factors for each month have differences.
Preferably, the fishing effort in the step S4 is defined as the number of operations.
Preferably, in the step S5, the corresponding environmental variable range when the adaptive probability of the squid is greater than 0.4 is regarded as the appropriate environmental range of the squid.
Preferably, when the response curve of the possible existence probability of the squid liver to the key environment variable is drawn in the step S5, in order to avoid the influence of other environment variables, a single environment variable is selected, and the rationality of the selection of the key environment factor is verified by combining the frequency distribution graph of the key environment variable and the fishing effort in the step S4.
The principle of the invention is as follows: the method includes the steps of simulating potential fishery distribution of the Dosidicus gigas by combining fishery fishing data of the Dosidicus gigas with environment data such as different water layer water temperatures, sea surface heights, sea surface salinities and mixed layer depths of the Dosidicus gigas through a maximum entropy model, superposing the distribution result of the simulated fishery with actual fishery data, selecting key environment factors influencing the Dosidicus gigas fishery distribution through different environmental variable contribution rates of months, comparing and analyzing the appropriate range of the key environment factors under the simulated Dosidicus gigas and actual distribution conditions, and selecting fishery data and environment data of different years to conduct fishery prediction analysis based on the selected key environment factors.
The invention has the beneficial effects that:
(1) aiming at the traditional research of the correlation between the space-time distribution and the environment of the squid liver, 2-3 persons are mostly assumed to be set environment factors for analysis, the differences and the contribution rates of 12 different environment variables to the distribution of the squid liver at different time periods are fully considered, the response rules and the monthly differences of the squid liver to the different environment factors are explored, the key environment factors are screened out, and the preference range of the squid liver is evaluated.
(2) The method provided by the invention is used for screening out key environmental factors influencing the distribution of the squid liver fishery to carry out modeling and prediction based on the factors with little influence neglected by the contribution rate, and fully considers the biological characteristics of the squid liver and the sensitivity difference of the squid liver to the environment, so that the environmental variables selected when the squid liver fishery in Chile sea area is predicted in future are more reasonable and scientific, and the reliability of a prediction model is enhanced.
Drawings
Fig. 1 is a latest MaxEnt software 3.4.1 operation interface in an embodiment of the present invention.
Fig. 2 is a superposition distribution diagram of existence probabilities and actual operation positions of the MaxEnt model simulation 2011-2017 summer (12 months, 1 month and 2 months) and autumn (3 months, 4 months and 5 months) squid duchebeth in the embodiment of the invention.
Fig. 3 is a frequency distribution diagram of actual catching efforts of squid liver under the condition of key environmental variables in each month in summer in an embodiment of the invention.
Fig. 4 is a frequency distribution diagram of actual catching efforts of squid liver under key environmental variables in each month of autumn in an embodiment of the present invention.
Fig. 5 is a diagram illustrating a reflection curve of the survival probability of the squid in summer when only a single key environmental variable is modeled in an embodiment of the present invention.
Fig. 6 is a diagram showing a reflection curve of the survival probability of squid liver when only a single key environmental variable is modeled in autumn in an embodiment of the present invention.
Fig. 7 is a diagram illustrating the probability of existence (or HSI) of squid liver in 12-5 months in 2011 predicted by the MaxEnt model and an actual distribution diagram thereof in an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.
A screening and detecting technology for key environmental factors influencing a Chile sea area squid liver fishery comprises the following steps:
s1, processing fishery fishing data of Chilean sea area Chilo-squid, wherein the data comprises operation time (year and month), operation position (longitude and latitude), fishing yield (unit: ton) and fishing Nu force (counted by operation times), processing all environment data into graph layer data by ArcGis10.2 software, simulating potential fishery distribution of Chilean sea area Chilo-squid by combining the fishery data and the environment data after processing by using a MaxEnt model,
s2, visualizing and classifying the existing probability distribution results of the squid liver in each month by ArcGis10.2 software, classifying the existing probability distribution results by different color regions, defining the existing probability of the squid liver as Habitat Suitability Index (HSI), representing potential fishery distribution, superposing the potential fishery distribution indexes with actual fishery distribution data, verifying simulation effect, taking the size of AUC value in model simulation results as an index for measuring model precision,
s3, comparing and analyzing the contribution condition of different environment variables of each month to the distribution of the Dosidicus gigas fishery, selecting the variables with the contribution ratio of three before ranking according to the contribution ratio to be regarded as the key environment factors influencing the space-time distribution of the Dosidicus gigas fishery in the month,
s4, matching the actual fishery data of each month with key environment factor data of the actual fishery data, defining the operation times as the catching effort force, drawing a frequency distribution graph with the key environment variable as a horizontal coordinate and the catching effort force as a vertical coordinate by using a frequency distribution method, calculating the appropriate range of the key environment variable when the Dosidicus gigas is actually distributed,
s5, drawing a response curve graph with the key environment variable as an abscissa and the adaptive probability of the Dosidicus gigas under the condition of single environment variable as an ordinate, calculating the appropriate range of the corresponding key environment variable when the adaptive probability is more than 0.4 under the simulation condition, comparing the appropriate range with the appropriate range of the corresponding environment variable when the adaptive probability is actually distributed,
and S6, selecting fishery data and environment data of different years, reestablishing a MaxEnt model to simulate potential distribution of the Dosidicus gigas fishery, and selecting key environment factors of each month to evaluate and predict the Dosidicus gigas fishery in Chile sea areas.
The step S2 defines the existence probability of squid liver as a Habitat Suitability Index (HSI). The existence probability of the squid liver in the step S2 is that the model combines all environment variables and screens out key factors according to the contribution rate of each variable to simulate and verify the fishing ground. In step S2, an area value (area ≦ undercut, AUC) under a receiver operating characteristic Curve (ROC) automatically generated in the model operation process is used as an index for measuring the model accuracy, specifically: when the potential distribution of the squid does not coincide with the actual distribution of the squid, the AUC value is 0; when the potential distribution of the model is completely consistent with the actual distribution, namely under an ideal state, the AUC value is 1; and judging the accuracy of the model according to the AUC value automatically generated by the model.
The number of the environmental variables is not limited in the step S3, the first three variables with the highest contribution rate are selected as the key environmental factors of the month according to the contribution of each environmental variable to species distribution in different time periods, and different from the previous research, the method fully considers the influence degree of each environmental variable on the space-time distribution of the red squid in different time periods, embodies the high migration and the biological characteristics sensitive to the environment of the red squid, and enables the research result to be more scientific. The selection of the key environmental factors of each month in the step S3 is to select the three top ranked variables as the key environmental factors of the month in turn only according to the order of the contribution rate of each environmental variable of each month from large to small, and the key environmental factors of each month have differences. The fishing effort in the step S4 is defined as the number of jobs.
In step S5, the corresponding environmental variable range when the adaptive probability of the squid is greater than 0.4 is regarded as the appropriate environmental range of the squid. When the response curve of the possible existence probability of the squid liver to the key environment variable is drawn in the step S5, in order to avoid the influence of other environment variables, a single environment variable is selected, and the reasonability of the selection of the key environment factor is verified by combining the frequency distribution graph of the key environment variable and the fishing effort force in the step S4.
Example (b): as shown in FIGS. 1-7, evaluation and prediction of Chile squid fishery in summer (12-2 months) and autumn (3-5 months) in Chili sea area 2011-2017 are selected as implementation cases, the spatial resolution is 0.5 degrees multiplied by 0.5 degrees, the coverage range is 70-97 degrees W, and 20-47 degrees S.
1. Model construction
The Maximum Entropy model (maxment) in the present application is based on species presence data and environmental data of the entire research area, and selects a distribution with the Maximum species presence probability, i.e., Entropy, as an optimal distribution of potential habitats thereof in compliance with a constraint. Assuming that the environmental region of species distribution is M, M is composed of a finite number of spatial grids xiForming; y represents the presence of the species in a grid, i.e., y is1 when the species is present and 0 when the species is absent. Defining the distribution probability of the target species in each grid to be pi (x) based on the existence condition of the species, and then
π(x)＝Pr(x∣y＝1) (1)
And Σ pi (x) ═ 1; the distribution probability H (pi) of the species under the limitation of environmental conditions is calculated by the following formula:
model operation uses the latest MaxEnt software
3.4.1(http://biodiversityinformatics.amnh.org/open_source/maxent/) The operation interface is shown in figure 1, species existence data of a sample input layer (Samples) is data for fishing each fishing boat in the current month at daily operation positions and removing fishing catches as 0, the data is from a China ocean fishery data center of Shanghai ocean university, the data is obtained by selecting 2011-2017 Chili sea area Chihua-squid fishery fishing catch data, the time resolution is month, the spatial resolution is 0.5 degrees × 0.5.5 degrees, the input form is 'species name, longitude and latitude', and the data is stored in a csv format, the data of an environment input layer (Environmental layer) is the mean value of 12 Environmental data such as different water layer water temperatures (including 0m,25m, 50m, 100m, 150m, 200m, 300m, 400m, 500m), sea surface height, sea surface salinity and mixed layer depth in a research area of each month, and the data is obtained from a Atlantic data research center (Satilde research center)http://apdrc.soest.hawaii.edu/las_ofes/v6/dataset？catitem＝71) The time resolution is month, the space resolution is 0.5 degree × 0.5.5 degree, and the ArcGis10.2 software converts the space resolution into ASCII format for storage, 75% of species distribution data is used as training data before the MaxEnt model is run, and the rest isAnd the rest 25 percent of the test data is test data, in order to eliminate randomness and repetition number, the repeated operation frequency of the model is set to 10 times, namely, the sample data is equally divided into 10 parts, in the operation process, the 10 equal parts of species distribution data are operated in a cross verification mode, the regularization multiplier and the iteration frequency default to the automatic optimal setting of the software, and the operation result is output in a Logistic form.
2. Result verification
The experimental performance of the evaluation model is evaluated by using a Receiver operating characteristic Curve (ROC) automatically generated by a MaxEnt model. The judgment of the model on the prediction result threshold value can generate different classification modes such as correct estimation, overestimation and underestimation. Correctly estimating the number of grids actually existing in the M region space grid by the model, namely, correctly predicting the number of the grids actually existing in the species, namely, true positive, wherein the true positive rate is the rate of correctly predicting the existence condition of the species; overestimating that the species do not exist in the M region space grid actually, but the number of the grids in which the model predicts the species exists is also called false positive, and the false positive rate is the rate of the species which do not exist but are predicted to exist; the over-underestimation is that the species actually exists in the M region space grid, but the number of grids which the model predicts does not exist is also false negative. The ROC curve is drawn by taking a false positive rate as an abscissa and a true positive rate as an ordinate, the size of a curve area value (AUC) enclosed by the ROC curve and the abscissa is taken as a measurement index of the model precision, and a value range is [0, 1%]That is, when the potential distribution of the model simulation species is completely inconsistent with the actual distribution, the AUC value is 0; when the two completely matched, the AUC value was 1. Will be provided with
Defined as model prediction failure, poor, general, good, and excellent. The simulation sample size, precision and standard deviation of each month model are summarized in table 1, and the simulation precision of each month is greater than 0.9, which indicates that the simulation result is excellent.
TABLE 1 summary of simulation conditions of summer and autumn squid
The output of the model has the existence probability distribution result format of ASCII, and the result needs to be imported into ArcCis 10.2 software for visual analysis. Firstly, converting ASCII format data into a Raster format (Raster format), loading a world map ship file for extraction and analysis, and further obtaining a distribution map of the Dosidicus gigas in a research area; the probability of presence is defined as the Habitat Suitability Index (HSI) and is in terms of
(unsuitable habitat) of,
(generally suitable habitat),
(preferably a habitat),
Performing 'reclassification' (most suitable habitat), and giving different colors to each class; the practical production statistical data of Chilean sea area Dosidicus gigas and the simulation probability distribution map are superposed, as shown in FIG. 2, most of the actual fishing efforts in 2011-2017 are concentrated in the most suitable area, and the model simulation species distribution and the practical distribution are very high in matching degree.
3. Key environmental factor selection
In the model operation process, the model gain is increased by changing the characteristic coefficient of a certain environment variable, the increment of the model gain is added to the environment variable, and the increment of the gain is converted into percentage at the end of the model operation process, so that the contribution rate of each environment variable in each month can be obtained, as shown in table 2. According to the contribution rate of each environmental variable in each month, the first three variables with higher contribution rates are selected as the key environmental factors in the month from the large contribution rate to the small contribution rate (the bold environmental variables in table 2 are the key environmental factors screened in each month).
Table 22011-2017 environmental factor contribution rate in summer and autumn
Matching the selected key environmental factor data of each month with fishery data, drawing a frequency distribution graph with the key environmental variable as a horizontal coordinate and the fishing effort as a vertical coordinate by using a frequency step method, and estimating the appropriate range of each key environmental variable, as shown in fig. 3 and 4; and drawing a response curve graph with the key environment variable as an abscissa and the survival probability of the squid liver output when modeling is carried out by using a single key environment variable as an ordinate, as shown in fig. 5 and 6, calculating the appropriate range of the key environment variable under the condition that the survival probability is greater than 0.4, and comparing the appropriate range with the appropriate range of the corresponding key environment variable in the frequency distribution diagram to verify the rationality of the selected key environment factor, as shown in table 3, the appropriate range of the key environment variable of the squid liver under the simulation condition is consistent with the appropriate range of the key environment variable when the squid liver is actually distributed.
TABLE 3 suitable range of key environmental factors for each month in summer and autumn
4. Predictive analysis
According to the key environmental factors for screening Chile sea area Liquidambar nuda habitats and the fishing ground detection technical method thereof, the fishery data in summer (12-2 months) and autumn (3-5 months) and the environmental data of research areas after treatment in 2012-2017 are selected, the time resolution is months, the spatial resolution is 0.5 degrees multiplied by 0.5 degrees, the key environmental factors in each month are selected again by using a MaxEnt model, as shown in table 4, the squid liver potential fishery in the month corresponding to 2011 is predicted according to the key environmental variable data selected in each month and the processed environmental variable data corresponding to the month corresponding to 2011, and the actual fishery distribution data are overlapped, as shown in fig. 7, most of the fishing effort in 2011 predicted according to the key environment factors selected in 2012-2017 simulated by the model is concentrated in the area with the high HSI value, and the fact that the key environment factors selected by the method can better evaluate and predict the spatial position of the Chilean sea area Chilo-squid fishery is shown.
Table 42012-2017 environmental factor contribution rates in summer and autumn
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. A technology for screening and detecting key environmental factors influencing a Chilo-squid fishery in Chili sea areas is characterized by comprising the following steps:
s1, processing fishery fishing data of Chilean sea area Chilo-squid, wherein the data comprises operation time, operation position, fishing yield and fishing Nu force, processing all environment data into graph layer data by ArcKis 10.2 software, simulating potential fishery distribution of Chilean sea area Chilo-squid by combining the fishery data and the environment data after processing by using a MaxEnt model,
s2, visualizing and classifying the existing probability distribution results of the squid liver in each month by ArcGis10.2 software, classifying the existing probability distribution results by different color regions, defining the existing probability of the squid liver as a habitat suitability index, representing potential fishery distribution, superposing the potential fishery distribution index with actual fishery distribution data, verifying simulation effect, taking the size of AUC value in model simulation results as an index for measuring model precision,
s3, comparing and analyzing the contribution condition of different environment variables of each month to the distribution of the Dosidicus gigas fishery, selecting the variables with the contribution ratio of three before ranking according to the contribution ratio to be regarded as the key environment factors influencing the space-time distribution of the Dosidicus gigas fishery in the month,
s4, matching the actual fishery data of each month with key environment factor data of the actual fishery data, defining the operation times as the catching effort force, drawing a frequency distribution graph with the key environment variable as a horizontal coordinate and the catching effort force as a vertical coordinate by using a frequency distribution method, calculating the appropriate range of the key environment variable when the Dosidicus gigas is actually distributed,
s5, drawing a response curve graph with the key environment variable as an abscissa and the adaptive probability of the Dosidicus gigas under the condition of single environment variable as an ordinate, calculating the appropriate range of the corresponding key environment variable when the adaptive probability is more than 0.4 under the simulation condition, comparing the appropriate range with the appropriate range of the corresponding environment variable when the adaptive probability is actually distributed,
and S6, selecting fishery data and environment data of different years, reestablishing a MaxEnt model to simulate potential distribution of the Dosidicus gigas fishery, and selecting key environment factors of each month to evaluate and predict the Dosidicus gigas fishery in Chile sea areas.
2. The screening and detection technique for key environmental factors affecting Chilodochus praecox in Chilean sea area of claim 1, wherein the step S2 defines the existence probability of Chilodochus praecox as habitat suitability index.
3. The technology of claim 1, wherein the Squidus americana presence probability in step S2 is that the model combines all environmental variables and screens out the key environmental factors according to the contribution rate of each variable for fishery simulation and verification.
4. The technology of claim 1, wherein the step S2 uses an area value under a working characteristic curve of a subject automatically generated during a model operation process as an index for measuring model accuracy, specifically: when the potential distribution of the squid does not coincide with the actual distribution of the squid, the AUC value is 0; when the potential distribution of the model is completely consistent with the actual distribution, namely under an ideal state, the AUC value is 1; and judging the accuracy of the model according to the AUC value automatically generated by the model.
5. The technology of claim 1, wherein the number of environmental variables is not limited in step S3, and the first three variables with the highest contribution rate are selected as the key environmental factors of the month according to the contribution of each environmental variable to species distribution in different time periods.
6. The technology of claim 1, wherein the selection of the key environmental factors in each month in step S3 is performed by sequentially selecting the first three variables as the key environmental factors in the month according to the descending order of the contribution rate of the environmental variables in each month, and the key environmental factors in each month have differences.
7. The technology of claim 1, wherein the catching effort in step S4 is defined as the number of operations.
8. The technology of claim 1, wherein in step S5, the environmental variable range corresponding to the squid liver survival probability greater than 0.4 is regarded as the suitable environmental range of squid liver.
9. The technology of claim 1, wherein when a response curve of the possible occurrence probability of the squid liver to the key environment variables is plotted in step S5, a single environment variable is selected to avoid the influence of other environment variables, and a frequency distribution diagram of the key environment variables and the fishing effort in step S4 is combined to verify the rationality of selecting the key environment factors.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112686465A (en) * | 2021-01-08 | 2021-04-20 | 中国海洋大学 | Method, system, equipment and application for predicting set model of scomberomorus niphonius fishery |
| CN112784180A (en) * | 2021-02-03 | 2021-05-11 | 中国水产科学研究院东海水产研究所 | Method for extracting catching strength spatial information of tuna seine fishing boat |
| CN114444819A (en) * | 2022-04-11 | 2022-05-06 | 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) | Fishery resource prediction method and device, storage medium and electronic equipment |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106204313A (en) * | 2016-07-22 | 2016-12-07 | 上海海洋大学 | A kind of method predicting jumbo flying squid resources spatial distribution |
| CN106651616A (en) * | 2016-12-26 | 2017-05-10 | 上海海洋大学 | Method for predicting access fishing of western and central pacific ocean katsuwonuspelamis purse seine fishery |
| CN106845699A (en) * | 2017-01-05 | 2017-06-13 | 南昌大学 | A kind of method for predicting oil tea normal region |
| CN107944590A (en) * | 2016-10-13 | 2018-04-20 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of fishing condition analysis and forecasting |
| CN109584098A (en) * | 2018-12-20 | 2019-04-05 | 上海海洋大学 | A kind of prediction technique and device of fishing ground catch per unit effort |
| CN109767040A (en) * | 2019-01-15 | 2019-05-17 | 上海海洋大学 | Saury cental fishing ground prediction technique based on habitat suitability index |
| CN109902761A (en) * | 2019-03-18 | 2019-06-18 | 上海海洋大学 | A kind of fishing feelings prediction technique based on marine environment Factor Fusion and deep learning |
| US20190230913A1 (en) * | 2016-07-22 | 2019-08-01 | Shanghai Ocean University | Method of Predicting Central Fishing Ground of Flying Squid Family Ommastrephidae |
| US20190272598A1 (en) * | 2017-08-29 | 2019-09-05 | Shanghai Ocean University | Cephalopod fishery forecasting method in northwest african waters based on environmental factors |
| WO2020088615A1 (en) * | 2018-11-02 | 2020-05-07 | 上海海洋大学 | Todarodes pacificus resource abundance prediction method and application based on pacific decadal oscillation |
| CN111222748A (en) * | 2019-11-27 | 2020-06-02 | 上海海洋大学 | Tuna habitat quantitative evaluation method |
-
2020
- 2020-06-22 CN CN202010576595.0A patent/CN111784034B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106204313A (en) * | 2016-07-22 | 2016-12-07 | 上海海洋大学 | A kind of method predicting jumbo flying squid resources spatial distribution |
| US20190230913A1 (en) * | 2016-07-22 | 2019-08-01 | Shanghai Ocean University | Method of Predicting Central Fishing Ground of Flying Squid Family Ommastrephidae |
| CN107944590A (en) * | 2016-10-13 | 2018-04-20 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of fishing condition analysis and forecasting |
| CN106651616A (en) * | 2016-12-26 | 2017-05-10 | 上海海洋大学 | Method for predicting access fishing of western and central pacific ocean katsuwonuspelamis purse seine fishery |
| CN106845699A (en) * | 2017-01-05 | 2017-06-13 | 南昌大学 | A kind of method for predicting oil tea normal region |
| US20190272598A1 (en) * | 2017-08-29 | 2019-09-05 | Shanghai Ocean University | Cephalopod fishery forecasting method in northwest african waters based on environmental factors |
| WO2020088615A1 (en) * | 2018-11-02 | 2020-05-07 | 上海海洋大学 | Todarodes pacificus resource abundance prediction method and application based on pacific decadal oscillation |
| CN109584098A (en) * | 2018-12-20 | 2019-04-05 | 上海海洋大学 | A kind of prediction technique and device of fishing ground catch per unit effort |
| CN109767040A (en) * | 2019-01-15 | 2019-05-17 | 上海海洋大学 | Saury cental fishing ground prediction technique based on habitat suitability index |
| CN109902761A (en) * | 2019-03-18 | 2019-06-18 | 上海海洋大学 | A kind of fishing feelings prediction technique based on marine environment Factor Fusion and deep learning |
| CN111222748A (en) * | 2019-11-27 | 2020-06-02 | 上海海洋大学 | Tuna habitat quantitative evaluation method |
Non-Patent Citations (3)
| Title |
|---|
| 张嘉容;杨晓明;田思泉;: "基于最大熵模型的南太平洋长鳍金枪鱼栖息地预测", 中国水产科学, no. 10, 1 April 2020 (2020-04-01) * |
| 张孝民;石永闯;李凡;朱明明;魏振华;: "基于MAXENT模型预测西北太平洋秋刀鱼潜在渔场", 上海海洋大学学报, no. 02, 11 September 2019 (2019-09-11) * |
| 龚彩霞;陈新军;高峰;: "基于最大熵模型模拟西北太平洋柔鱼潜在栖息地分布", 中国水产科学, no. 03, 13 March 2020 (2020-03-13) * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112686465A (en) * | 2021-01-08 | 2021-04-20 | 中国海洋大学 | Method, system, equipment and application for predicting set model of scomberomorus niphonius fishery |
| CN112784180A (en) * | 2021-02-03 | 2021-05-11 | 中国水产科学研究院东海水产研究所 | Method for extracting catching strength spatial information of tuna seine fishing boat |
| CN114444819A (en) * | 2022-04-11 | 2022-05-06 | 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) | Fishery resource prediction method and device, storage medium and electronic equipment |
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