Estimation method for risk of algal bloom in estuary based on optimization and fusion of multiple water environment indexes

Abstract

The embodiment of the present application provides a kind of estuary algal bloom risk assessment method based on the optimization fusion of multiple water environment indexes, comprising: first determining the inversion algorithm of various water environment key indicators of target estuary area, then according to the water environment characteristics of target estuary area, combined with the historical algal bloom event of target estuary area, local standard and the growth characteristics of dominant algae, respectively determine the influence function of various water environment key indicators on algal bloom, and establish the algal bloom risk assessment model of the fusion of multiple water environment key indicators, then the inversion algorithm of various water environment key indicators is used to the inversion of various water environment key indicators of target estuary area when algal bloom event occurs, and based on the inversion result, the structure form and weight parameter optimization of algal bloom risk assessment model is carried out using genetic algorithm, to obtain target algal bloom risk assessment model, and then based on the model, the accurate evaluation of algal bloom risk of target estuary area is carried out, effectively improve the effect of algal bloom risk assessment.

Description

Technical Field

[0001] This invention relates to the field of environmental assessment technology, and in particular to a method for risk assessment of algal blooms in estuaries based on the optimized integration of multiple water environment indicators. Background Technology

[0002] Excessive algal blooms lead to a decrease in dissolved oxygen concentration in water bodies, thereby reducing biodiversity. As a typical manifestation of eutrophication, the frequency and intensity of algal blooms are increasing globally each year, seriously threatening human health and aquatic ecosystem security. Therefore, assessing the risk of algal blooms is of great significance for water resource management and pollution control.

[0003] Estuaries, as one of the most productive ecosystems, are characterized by the confluence of fresh and salt water, complex hydrodynamic conditions, and high and unevenly distributed pollution loads, leading to frequent algal blooms. Furthermore, influenced by these factors, the formation process of algal blooms is complex and highly heterogeneous in time and space, posing challenges to their monitoring and risk assessment. Remote sensing technology facilitates large-scale monitoring of algal blooms; however, most existing remote sensing-based algal bloom risk assessment methods assess risk using single indicators (such as algal biomass or chlorophyll-a concentration). However, algal risk is comprehensively influenced by multiple factors (including temperature, salinity, and nutrient concentration). While some current methods consider multiple indicators, insufficient research has been conducted on the interactions between these indicators, and the response of algal bloom risk to various indicators has not been quantified. The complex and dynamic relationships between these indicators make it difficult to accurately assess the risk of algal blooms under the combined influence of multiple indicators. Summary of the Invention

[0004] In a first aspect, embodiments of the present invention provide a method for risk assessment of estuarine algal blooms based on the optimized fusion of multiple water environment indicators, the method comprising:

[0005] Acquire historical remote sensing image data of the target estuary area;

[0006] Historical remote sensing image data is preprocessed to obtain historical water body remote sensing image data of the target estuary area;

[0007] For any key water environment indicator affecting algal blooms in the target estuary area, if a known inversion algorithm exists for the key water environment indicator, then the inversion algorithm for the key water environment indicator is obtained; otherwise, inversion is performed based on historical water body remote sensing image data and the corresponding historical measured data of the key water environment indicator to obtain the inversion algorithm for the key water environment indicator.

[0008] Based on the water environment characteristics of the target estuary area, and combined with the historical algal bloom events, local standards, and growth characteristics of dominant algae in the target estuary area, the influence functions of various key water environment indicators on algal blooms were determined respectively.

[0009] Based on the influence functions of various key water environment indicators on algal blooms, an algal bloom risk assessment model integrating multiple key water environment indicators is established.

[0010] The inversion algorithms of various key water environment indicators were used to invert various key water environment indicators of the target estuary area when an algal bloom event occurred. Based on the inversion results, the structure and weight parameters of the algal bloom risk assessment model were optimized using a genetic algorithm to obtain the target algal bloom risk assessment model.

[0011] The target algal bloom risk assessment model was used to conduct an algal bloom risk assessment of the target estuary area.

[0012] Among the possible implementations of the first aspect, historical remote sensing imagery data of the target estuary area is acquired, including:

[0013] Historical remote sensing image data covering the target estuary area are cropped from the geographic information vector file of the target estuary area to obtain the historical remote sensing image data of the target estuary area.

[0014] In some feasible ways of implementing the first aspect, key water environment indicators are determined through the following steps:

[0015] Correlation analysis was conducted between various water environment indicators of the target estuary area and algal bloom events in the target estuary area to obtain the correlation coefficients between various water environment indicators and algal bloom events.

[0016] Water environment indicators with correlation coefficients greater than or equal to a preset threshold are selected from a variety of water environment indicators as key water environment indicators.

[0017] Among the possible implementation methods of the first aspect, the key water environment indicators are chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, water surface temperature, and salinity.

[0018] In some feasible ways of implementing the first aspect, the effect function of chlorophyll-a concentration on algal bloom is determined by the following steps:

[0019] The minimum chlorophyll-a concentration under local standard mild algal bloom in the target estuary area was used as the maximum value of the influence function, and the maximum chlorophyll-a concentration under no algal bloom in the target estuary area was used as the minimum value of the influence function. The influence function value was defined between [0, 1] by linear interpolation, thus obtaining the influence function of chlorophyll-a concentration on algal bloom.

[0020] In some feasible ways of the first aspect, the effect function of dissolved inorganic nitrogen concentration on algal bloom is determined by the following steps:

[0021] The average dissolved inorganic nitrogen concentration in the target estuary region during a typical algal bloom event was used as the minimum value of the influence function, and the maximum dissolved inorganic nitrogen concentration in the target estuary region during a typical algal bloom event was used as the maximum value of the influence function. A linear interpolation method was used to define the influence function value between [0, 1], thus obtaining the influence function of dissolved inorganic nitrogen concentration on algal bloom.

[0022] In some feasible ways of the first aspect, the effect function of dissolved inorganic phosphorus concentration on algal bloom is determined by the following steps:

[0023] The average dissolved inorganic phosphorus concentration in the target estuary region during a typical algal bloom event was used as the minimum value of the influence function, and the maximum dissolved inorganic phosphorus concentration in the target estuary region during a typical algal bloom event was used as the maximum value of the influence function. A linear interpolation method was used to define the influence function value between [0, 1], thus obtaining the influence function of dissolved inorganic phosphorus concentration on algal bloom.

[0024] In some feasible implementations of the first aspect, the influence function of water surface temperature on algal blooms is determined through the following steps:

[0025] Based on the growth rates of dominant algae in the target estuary area at different water surface temperatures during past algal blooms, growth curves of dominant algae were fitted and normalized to obtain the influence function of water surface temperature on algal blooms.

[0026] In some feasible implementations of the first aspect, the effect function of salinity on algal bloom is determined through the following steps:

[0027] Based on the growth rates of dominant algae in the target estuary area at different salinities during past algal blooms, growth curves of dominant algae were fitted and normalized to obtain the effect function of salinity on algal blooms.

[0028] Among some possible implementations of the first aspect, the method also includes:

[0029] The spatiotemporal distribution of algal bloom risk assessment results is visualized.

[0030] Secondly, embodiments of the present invention provide an estuarine algal bloom risk assessment device based on the optimized fusion of multiple water environment indicators, the device comprising:

[0031] The acquisition module is used to acquire historical remote sensing image data of the target estuary area;

[0032] The preprocessing module is used to preprocess historical remote sensing image data to obtain historical water body remote sensing image data of the target estuary area.

[0033] The inversion module is used to target any key water environment indicator that affects algal blooms in the target estuary area. If a known inversion algorithm exists for the key water environment indicator, the inversion algorithm for the key water environment indicator is obtained. Otherwise, the inversion algorithm for the key water environment indicator is obtained by inverting based on historical water body remote sensing image data and the corresponding historical measured data of the key water environment indicator.

[0034] The determination module is used to determine the influence functions of various key water environment indicators on algal blooms based on the water environment characteristics of the target estuary area, combined with historical algal bloom events, local standards, and growth characteristics of dominant algae in the target estuary area.

[0035] A module is established to build an algal bloom risk assessment model that integrates multiple key water environment indicators based on the influence functions of various key water environment indicators on algal blooms.

[0036] The optimization module is used to invert various key water environment indicators in the target estuary area when an algal bloom event occurs using inversion algorithms of various key water environment indicators. Based on the inversion results, a genetic algorithm is used to optimize the structure and weight parameters of the algal bloom risk assessment model to obtain the target algal bloom risk assessment model.

[0037] The assessment module is used to conduct algal bloom risk assessments on target estuarine areas using the target algal bloom risk assessment model.

[0038] Thirdly, embodiments of the present invention provide an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.

[0039] Fourthly, embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method described above.

[0040] In this embodiment of the invention, firstly, the inversion algorithms for various key water environment indicators of the target estuary area are obtained. Then, based on the water environment characteristics of the target estuary area, combined with historical algal bloom events, local standards, and the growth characteristics of dominant algae, the influence functions of various key water environment indicators on algal blooms are determined. Based on this, an algal bloom risk assessment model integrating multiple key water environment indicators is established. Next, the inversion algorithms for various key water environment indicators are used to invert various key water environment indicators of the target estuary area when algal bloom events occur. Based on the inversion results, a genetic algorithm is used to optimize the structure and weight parameters of the algal bloom risk assessment model, resulting in a target algal bloom risk assessment model with outstanding assessment capabilities. Then, based on this model, the algal bloom risk of the target estuary area is accurately assessed, effectively improving the algal bloom risk assessment effect.

[0041] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0042] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the invention. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0043] Figure 1 A flowchart of a risk assessment method for estuarine algal blooms based on the optimized fusion of multiple water environment indicators provided in this embodiment of the invention;

[0044] Figure 2 A schematic diagram of the spatial distribution of algal bloom risk in estuary region A in different years, provided for an embodiment of the present invention;

[0045] Figure 3 A structural diagram of an estuarine algal bloom risk assessment device based on the optimized fusion of multiple water environment indicators provided in an embodiment of the present invention;

[0046] Figure 4 This is a structural diagram of an exemplary electronic device capable of implementing embodiments of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0049] To address the technical problems mentioned in the background art, this invention provides a method, apparatus, device, and storage medium for estuarine algal bloom risk assessment based on the optimized fusion of multiple water environment indicators. Specifically, firstly, inversion algorithms for various key water environment indicators of the target estuarine area are obtained. Then, based on the water environment characteristics of the target estuarine area, combined with historical algal bloom events, local standards, and the growth characteristics of dominant algae, the influence functions of various key water environment indicators on algal blooms are determined, and an algal bloom risk assessment model integrating multiple key water environment indicators is established. Next, the inversion algorithms for various key water environment indicators are used to invert various key water environment indicators in the target estuarine area during algal bloom events. Based on the inversion results, a genetic algorithm is used to optimize the structure and weight parameters of the algal bloom risk assessment model, resulting in a target algal bloom risk assessment model with outstanding assessment capabilities. Finally, based on this model, the algal bloom risk of the target estuarine area is accurately assessed, effectively improving the algal bloom risk assessment effect.

[0050] The following detailed description, in conjunction with the accompanying drawings, of a method, apparatus, equipment, and storage medium for risk assessment of estuarine algal blooms based on the optimized fusion of multiple water environment indicators, provided by embodiments of the present invention, will be provided in detail.

[0051] Figure 1 A flowchart illustrating a risk assessment method for estuarine algal blooms based on the optimized fusion of multiple water environment indicators, as provided in this embodiment of the invention, is shown below. Figure 1 As shown, the estuarine algal bloom risk assessment method 100 may include:

[0052] S110: Acquire historical remote sensing image data of the target estuary area.

[0053] In some embodiments, historical remote sensing image data covering the target estuary area can be obtained and cropped based on the geographic information vector file of the target estuary area (i.e., the study area), and the image portion of the target estuary area can be extracted to obtain the historical remote sensing image data of the target estuary area.

[0054] S120 involves preprocessing historical remote sensing image data to obtain historical water body remote sensing image data for the target estuary area.

[0055] In some embodiments, the purpose of preprocessing is to improve the quality of image data and ensure its suitability for water body analysis. This preprocessing specifically includes: eliminating atmospheric interference through atmospheric correction; removing cloud-covered areas using declouding techniques; and separating water body regions using water extraction techniques. Finally, based on the above preprocessing, historical remote sensing image data of the target estuary area can be generated.

[0056] As an example, remote sensing image data from satellite sensors is acquired in the GEE platform and atmospheric correction is performed to obtain the pixel brightness value (DN) of the band. The surface reflectance can be obtained by applying a scaling factor to the DN of the surface reflectance band; the water surface temperature can be obtained by applying a scaling factor to the DN of the surface temperature band of the Landsat series satellites, as shown in formulas (1)-(2).

[0057] (1)

[0058] (2)

[0059] In the formula, For satellite sensors in Surface reflectance in nm; For satellite sensors in The pixel brightness value of the remote sensing image in nm; , These are scaling factors, used when using Landsat remote sensing image data. , The values ​​are 0.0000275 and -0.2 respectively; when using MODIS remote sensing image data, , They are 0.0001 and 0 respectively; Water surface temperature (°C); These are the pixel brightness values ​​for the surface temperature band of the Landsat series satellites.

[0060] To ensure data quality, historical remote sensing image data needs to be processed to remove clouds and extract water bodies, resulting in historical water body remote sensing image data. Cloud removal uses Landsat and MODIS quality bands; pixels identified as clouds are removed. Water body extraction involves calculating the Normalized Difference Water Index (NDWI), which extracts areas containing water. When a pixel's NDWI is greater than -0.2, the area it covers is considered to be water. For example, the calculation of NDWI can be seen in formula (3).

[0061] (3)

[0062] In the formula, and These represent the surface reflectance of Landsat series satellite sensors in green and near-infrared light, respectively.

[0063] S130: For any key water environment indicator affecting algal blooms in the target estuary area, if a known inversion algorithm exists for the key water environment indicator, then obtain the inversion algorithm for the key water environment indicator; otherwise, perform inversion based on historical remote sensing image data of water bodies and the corresponding historical measured data of the key water environment indicator to obtain the inversion algorithm for the key water environment indicator.

[0064] In some embodiments, the above-mentioned key water environment indicators can be determined in advance through the following steps:

[0065] Correlation analysis was conducted between various water environment indicators of the target estuary area and algal bloom events in the target estuary area to obtain the correlation coefficients between various water environment indicators and algal bloom events.

[0066] Water environment indicators with correlation coefficients greater than or equal to a preset threshold are selected from a variety of water environment indicators as key water environment indicators.

[0067] As an example, correlation analysis can be performed between water environment indicators such as chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, total suspended solids, water transparency, salinity, water surface temperature, and photosynthetically active radiation and algal bloom events. Five water environment indicators obtainable from remote sensing image data can be selected as key indicators: chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, water surface temperature, and salinity. For example, it is assumed here that there is a known inversion algorithm for chlorophyll-a concentration and water surface temperature, while there is no known inversion algorithm for dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, and salinity.

[0068] The inversion algorithm for chlorophyll-a concentration can be represented by formulas (4)-(5).

[0069] (4)

[0070] (5)

[0071] In the formula, Chlorophyll-a concentration (mg / m³) 3 ); and These are the surface reflectivities of Landsat series satellite sensors in blue and green light, respectively.

[0072] The algorithm for retrieving water surface temperature can be expressed by formula (2), which will not be elaborated here.

[0073] The algorithm for retrieving dissolved inorganic nitrogen concentration can be obtained by performing multivariate linear fitting on historical water remote sensing image data (i.e., historical satellite sensor surface reflectance data of water areas) and corresponding historical measured data of dissolved inorganic nitrogen concentration. By comprehensively comparing the fitting performance indicators of different combinations (including coefficient of determination, root mean square error, relative percentage difference, etc.), the best combination can be found as the algorithm for retrieving dissolved inorganic nitrogen concentration. Specifically, it can be expressed by formula (6).

[0074] (6)

[0075] In the formula, The concentration of dissolved inorganic nitrogen (mg / L); and The surface reflectance of MODIS at 645 nm and 469 nm are respectively. , The constant is to be determined and is obtained by fitting historical satellite sensor surface reflectivity data with measured data.

[0076] The inversion algorithm for dissolved inorganic phosphorus concentration can be obtained using machine learning. Specifically, latitude and longitude, and the surface reflectance of MODIS at 645 nm, 859 nm, 469 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm are used as independent variables, and the measured dissolved inorganic phosphorus concentration is used as the dependent variable for machine learning. Preferably, different machine learning algorithms are compared, and the optimal machine learning model for inverting dissolved inorganic phosphorus concentration is found by comparing the performance indicators of the models and then applied.

[0077] The salinity inversion algorithm can be obtained by performing multivariate linear fitting on historical water body remote sensing image data (i.e., historical satellite sensor surface reflectance data of water body area) and its corresponding historical salinity measurement data. By comprehensively comparing the fitting performance indicators of different combinations (including coefficient of determination, root mean square error, relative percentage difference, etc.), the best combination can be found as the salinity inversion algorithm, which can be expressed by formula (7).

[0078] (7)

[0079] In the formula, S is salinity (PSU); , and The surface reflectance of MODIS at 667 nm, 555 nm and 488 nm are respectively. , , , The constant is to be determined and is obtained by fitting historical satellite sensor surface reflectivity data with measured data.

[0080] It is important to note that if certain key water environment indicators affecting algal blooms in the target estuary area have known inversion algorithms, but these algorithms do not meet current requirements, inversion can be performed using historical remote sensing imagery of the water body and corresponding historical measured data for that key water environment indicator to obtain the inversion algorithm. For example, if a known inversion algorithm exists for dissolved inorganic nitrogen concentration in the target estuary area, but it does not meet current requirements, inversion can be performed using historical remote sensing imagery of the water body and corresponding historical measured data for dissolved inorganic nitrogen concentration to obtain the inversion algorithm for dissolved inorganic nitrogen concentration.

[0081] S140. Based on the water environment characteristics of the target estuary area, and combined with the historical algal bloom events, local standards, and growth characteristics of dominant algae in the target estuary area, determine the influence functions of various key water environment indicators on algal blooms.

[0082] Among them, when the influence function value of the key water environment indicator is 1, it indicates that the algal bloom is promoted under this condition; when the influence function value is 0, it indicates that the algal bloom is not promoted under this condition.

[0083] Referring to S130, taking key water environment indicators such as chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, water surface temperature, and salinity as examples, the influence functions of chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, water surface temperature, and salinity on algal blooms can be determined through the following steps:

[0084] Effect function of chlorophyll-a concentration on algal bloom:

[0085] The minimum chlorophyll-a concentration under local standard mild algal bloom in the target estuary area was used as the maximum value of the influence function, and the maximum chlorophyll-a concentration under no algal bloom in the target estuary area was used as the minimum value of the influence function. Linear interpolation was used to define the influence function value between [0, 1], thus obtaining the influence function of chlorophyll-a concentration on algal bloom. See formulas (8)-(10) for details.

[0086] (8)

[0087] (9)

[0088] (10)

[0089] In the formula, Chlorophyll-a concentration (mg / m³) 3 ); The effect of chlorophyll-a concentration on algal bloom is a functional value. The minimum chlorophyll-a concentration (mg / m³) for mild algal bloom in the target estuary area according to local standards. 3 ); The maximum chlorophyll-a concentration (mg / m³) in the target estuary area during the absence of algal blooms. 3 ); , is an undetermined constant.

[0090] The effect function of dissolved inorganic nitrogen concentration on algal bloom:

[0091] The average dissolved inorganic nitrogen concentration in the target estuary region during a typical algal bloom event is used as the minimum value of the influence function, and the maximum dissolved inorganic nitrogen concentration in the target estuary region during a typical algal bloom event is used as the maximum value of the influence function. Linear interpolation is used to define the influence function value between [0, 1], thus obtaining the influence function of dissolved inorganic nitrogen concentration on algal bloom. See formulas (11)-(13) for details.

[0092] (11)

[0093] (12)

[0094] (13)

[0095] In the formula, The concentration of dissolved inorganic nitrogen (mg / L); The function value representing the effect of dissolved inorganic nitrogen concentration on algal bloom; The average dissolved inorganic nitrogen concentration (mg / L) in the target estuarine area during a typical algal bloom event. The maximum dissolved inorganic nitrogen concentration (mg / L) in the target estuarine area during a typical algal bloom event. , is an undetermined constant.

[0096] Effect function of dissolved inorganic phosphorus concentration on algal bloom:

[0097] The average dissolved inorganic phosphorus concentration in the target estuary region during a typical algal bloom event is used as the minimum value of the influence function, and the maximum dissolved inorganic phosphorus concentration in the target estuary region during a typical algal bloom event is used as the maximum value of the influence function. Linear interpolation is used to define the influence function value between [0, 1], thus obtaining the influence function of dissolved inorganic phosphorus concentration on algal bloom. See formulas (14)-(16) for details.

[0098] (14)

[0099] (15)

[0100] (16)

[0101] In the formula, The concentration of dissolved inorganic phosphorus (mg / L); The function value representing the effect of dissolved inorganic phosphorus concentration on algal bloom; The average dissolved inorganic phosphorus concentration (mg / L) in the target estuarine area during a typical algal bloom event. The maximum dissolved inorganic phosphorus concentration (mg / L) in the target estuarine area during a typical algal bloom event. , is an undetermined constant.

[0102] The effect function of water surface temperature on algal blooms:

[0103] Based on the growth rates of dominant algae in the target estuary area at different water surface temperatures during past algal blooms, growth curves of the dominant algae were fitted and normalized to obtain the influence function of water surface temperature on algal blooms. See formulas (17)-(18) for details.

[0104] (17)

[0105] (18)

[0106] In the formula, Water surface temperature (°C); This represents the function value of the effect of water surface temperature on algal blooms. , The constant is to be determined, and is obtained by fitting the growth curves of dominant algae at different water surface temperatures using polynomial fitting.

[0107] The effect function of salinity on algal blooms was determined through the following steps:

[0108] Based on the growth rates of dominant algae in the target estuary area at different salinities during past algal blooms, growth curves of dominant algae were fitted and normalized to obtain the effect function of salinity on algal blooms. See formulas (19)-(21) for details.

[0109] (19)

[0110] (20)

[0111] (twenty one)

[0112] In the formula, Salinity (PSU); This represents the effect of salinity on algal blooms. , , , , The constant is to be determined, and is obtained by fitting the growth curves of dominant algae at different salinities using polynomials.

[0113] In this way, the linear or nonlinear characteristics of the impact of each key water environment indicator on algal growth can be preserved, and the differences in units and scales between different indicators can be eliminated, making the algal bloom risk assessment model built on this basis applicable to algal bloom risk assessment under various environmental conditions.

[0114] S150, based on the influence functions of various key water environment indicators on algal blooms, establishes an algal bloom risk assessment model that integrates multiple key water environment indicators.

[0115] Referring to S140, taking the influence function of key water environment indicators on algal bloom as the influence function of chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, water surface temperature, and salinity on algal bloom as an example, the algal bloom risk assessment model integrating chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, water surface temperature, and salinity established here can be shown as formula (22).

[0116] (twenty two)

[0117] In the formula, This is the risk assessment value for algal blooms; It is a function that includes the effects of chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, salinity, and water surface temperature on algal blooms.

[0118] Furthermore, the algae risk assessment model can be in various weighted linear combination forms, such as formulas (23)-(24). The specific structural form and weight coefficients can be obtained through the subsequent optimization process of the model.

[0119] (twenty three)

[0120] (twenty four)

[0121] In the formula, This is the risk assessment value for algal blooms. , , , , These are the weight coefficients to be optimized.

[0122] S160 uses inversion algorithms for various key water environment indicators to invert various key water environment indicators in the target estuary area when an algal bloom event occurs. Based on the inversion results, a genetic algorithm is used to optimize the structure and weight parameters of the algal bloom risk assessment model to obtain the target algal bloom risk assessment model.

[0123] Specifically, the area of ​​the algal bloom event can be used to construct an optimized objective function to quantify the error between the predicted algal bloom area and the actual observed area. That is, the optimization objective of the genetic algorithm is to minimize the error between the predicted algal bloom area and the measured algal bloom area, where the predicted algal bloom area is determined based on the algal bloom risk assessment results. On this basis, the genetic algorithm iterates continuously to minimize the objective function, thereby obtaining the optimal structural form and weight parameters, thus obtaining the target algal bloom risk assessment model. For example, the objective function can be seen in formula (25), and the genetic algorithm uses the sum of the absolute values ​​of the relative errors in formula (25) to evaluate the performance under the current weight parameters.

[0124] (25)

[0125] In the formula, Represents the objective function value; Let be the predicted area (km²) of the algal bloom at the time of the i-th actual algal bloom event. 2 ); The measured algal bloom area (km²) at the time of the i-th actual algal bloom event. 2 ); , , , , , where are the weight coefficients to be optimized; N is the number of algal bloom events used for optimization by the genetic algorithm.

[0126] S170, Use the target algal bloom risk assessment model to conduct an algal bloom risk assessment of the target estuary area.

[0127] Specifically, various inversion algorithms for key water environment indicators can be used to invert various key water environment indicators of the target estuary area at a specified time, and the inversion results can be input into the target algal bloom risk assessment model to obtain the algal bloom risk assessment results. Then, the spatiotemporal distribution of the algal bloom risk assessment results can be visualized.

[0128] For example, an algal bloom risk visualization toolkit can be developed based on the target algal bloom risk assessment model. By inputting the key water environment indicator data obtained by inversion algorithms based on various key water environment indicators into the toolkit, the toolkit can automatically calculate the algal bloom risk assessment results and visualize their spatiotemporal distribution.

[0129] According to embodiments of the present invention, at least the following technical effects are achieved:

[0130] In this embodiment of the invention, firstly, the inversion algorithms for various key water environment indicators of the target estuary area are obtained. Then, based on the water environment characteristics of the target estuary area, combined with historical algal bloom events, local standards, and the growth characteristics of dominant algae, the influence functions of various key water environment indicators on algal blooms are determined. Based on this, an algal bloom risk assessment model integrating multiple key water environment indicators is established. Next, the inversion algorithms for various key water environment indicators are used to invert various key water environment indicators of the target estuary area when algal bloom events occur. Based on the inversion results, a genetic algorithm is used to optimize the structure and weight parameters of the algal bloom risk assessment model, resulting in a target algal bloom risk assessment model with outstanding assessment capabilities. Then, based on this model, the algal bloom risk of the target estuary area is accurately assessed, effectively improving the algal bloom risk assessment effect.

[0131] The following section uses a specific estuary region A as an example to provide a detailed description of the estuarine algal bloom risk assessment method 100 provided by this invention. The key water environment indicators for estuary region A are chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, water surface temperature, and salinity. Furthermore, there are known inversion algorithms for chlorophyll-a concentration and water surface temperature, and these algorithms meet the current requirements, so they can be directly obtained and used. While there are known inversion algorithms for dissolved inorganic nitrogen concentration and dissolved inorganic phosphorus concentration, they do not meet the current requirements. There is no known inversion algorithm for salinity; therefore, it is necessary to establish inversion algorithms for dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, and salinity.

[0132] (1) Use the GEE platform to acquire Landsat series satellite remote sensing image data and MODIS remote sensing image data covering the estuary area A on a monthly basis from 2003 to 2022.

[0133] (2) Based on the geographic information vector file of the estuary area A, the above-mentioned historical remote sensing image data is cropped to obtain the historical remote sensing image data of the estuary area A.

[0134] (3) Perform atmospheric correction, cloud removal, and water body extraction on historical remote sensing image data to obtain historical water body remote sensing image data.

[0135] (4) Obtain the inversion algorithm for chlorophyll-a concentration and water surface temperature in estuary region A; use historical water remote sensing image data (i.e., historical satellite sensor surface reflectance data of water body region) and the measured dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration and salinity data of estuary region A to perform inversion and obtain the inversion algorithms for dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration and salinity.

[0136] Among them, the inversion algorithm for chlorophyll-a concentration can be seen in formulas (4)-(5); the inversion algorithm for water surface temperature can be seen in formula (2); the inversion algorithm for dissolved inorganic nitrogen concentration can be seen in formula (26); and the inversion algorithm for salinity can be seen in formula (27).

[0137] (26)

[0138] In the formula, The concentration of dissolved inorganic nitrogen (mg / L); and The values ​​represent the surface reflectance of MODIS at 645 nm and 469 nm, respectively.

[0139]

[0140] (27)

[0141] In the formula, S is salinity (PSU); , and The values ​​represent the surface reflectance of MODIS at 667 nm, 555 nm, and 488 nm, respectively.

[0142] It is worth noting that this study compares eight common machine learning algorithms: Random Forest, XGBoosting, K-Nearest Neighbors, Kernel Ridge Regression, Gradient Boosting Decision Tree, Support Vector Machine, and Multiple Linear Regression. The results show that Kernel Ridge Regression performs best in retrieving dissolved inorganic phosphorus concentration. Therefore, this study employs Kernel Ridge Regression, using latitude and longitude, and MODIS surface reflectance at 645 nm, 859 nm, 469 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm as independent variables, and the measured dissolved inorganic phosphorus concentration as the dependent variable. Machine learning algorithms are then applied to invert the dissolved inorganic phosphorus concentration, resulting in the inversion algorithm for dissolved inorganic phosphorus concentration.

[0143] (5) Based on the water environment conditions when algal bloom events occurred in the estuary area A in the past, determine the influence functions of chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, water surface temperature, and salinity on algal blooms.

[0144] The effect function of chlorophyll-a concentration on algal bloom can be seen in formulas (28)-(30).

[0145] (28)

[0146] (29)

[0147] (30)

[0148] In the formula, Chlorophyll-a concentration (mg / m³) 3 ); The value represents the effect of chlorophyll-a concentration on algal bloom.

[0149] The effect function of dissolved inorganic nitrogen concentration on algal bloom can be seen in formulas (31)-(33).

[0150] (31)

[0151] (32)

[0152] (33)

[0153] In the formula, The concentration of dissolved inorganic nitrogen (mg / L); The value represents the effect of dissolved inorganic nitrogen concentration on algal bloom.

[0154] The effect of dissolved inorganic phosphorus concentration on algal bloom can be seen in formulas (34)-(36).

[0155] (34)

[0156] (35)

[0157] (36)

[0158] In the formula, The concentration of dissolved inorganic phosphorus (mg / L); The value represents the effect of dissolved inorganic phosphorus concentration on algal bloom.

[0159] The effect function of water surface temperature on algal bloom can be seen in formulas (37)-(38).

[0160] (37)

[0161] (38)

[0162] In the formula, Water surface temperature (°C); This represents the function value of the effect of water surface temperature on algal blooms.

[0163] The effect function of salinity on algal bloom can be seen in formulas (39)-(41).

[0164] (39)

[0165] (40)

[0166] (41)

[0167] In the formula, Salinity (PSU); The value represents the effect of salinity on algal blooms. Since the highest salinity in the estuary region generally does not exceed 35 PSU, areas with salinity greater than 35 PSU are not the estuary regions studied in this scheme. Therefore, it is used here. The effect of salinity on algal blooms was set to 0.

[0168] (6) Based on the influence functions of various key water environment indicators on algal blooms, establish an algal bloom risk assessment model that integrates multiple key water environment indicators.

[0169] (7) Various key water environment indicators were inverted using inversion algorithms to retrieve various key water environment indicators in estuary region A during an algal bloom event. Based on the inversion results, a genetic algorithm was used to optimize the structure and weight parameters of the algal bloom risk assessment model to obtain the target algal bloom risk assessment model. The termination condition of the genetic algorithm was 500 iterations.

[0170] (8) Based on the target algal bloom risk assessment model, develop an algal bloom risk visualization toolkit. By inputting the water environment key indicator data obtained by inversion algorithm based on various water environment key indicators into the toolkit, the toolkit can automatically calculate the algal bloom risk assessment results and visualize the spatiotemporal distribution.

[0171] Specifically, the toolkit input includes: key water environment indicator data exported from the GEE platform, and a specified time and regional range, which allows the spatiotemporal distribution of algal bloom risk in estuary region A to be exported. For example, it can be used as follows: Figure 2 As shown.

[0172] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0173] The above is an introduction to the method embodiments. The following describes the solution of the present invention further through device embodiments.

[0174] Figure 3 A structural diagram of an estuarine algal bloom risk assessment device based on the optimized fusion of multiple water environment indicators is provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the estuarine algal bloom risk assessment device 300 may include:

[0175] The acquisition module 310 is used to acquire historical remote sensing image data of the target estuary area.

[0176] The preprocessing module 320 is used to preprocess historical remote sensing image data to obtain historical water remote sensing image data of the target estuary area.

[0177] The inversion module 330 is used to obtain the inversion algorithm of any key water environment indicator that affects algal blooms in the target estuary area if a known inversion algorithm exists for the key water environment indicator. Otherwise, the inversion algorithm of the key water environment indicator is obtained by inverting based on historical water body remote sensing image data and the corresponding historical measured data of the key water environment indicator.

[0178] Module 340 is used to determine the influence functions of various key water environment indicators on algal blooms based on the water environment characteristics of the target estuary area, combined with historical algal bloom events, local standards, and growth characteristics of dominant algae in the target estuary area.

[0179] Module 350 is established to create an algal bloom risk assessment model that integrates multiple key water environment indicators based on the influence functions of various key water environment indicators on algal blooms.

[0180] The optimization module 360 ​​is used to invert various key water environment indicators in the target estuary area when an algal bloom event occurs using inversion algorithms of various key water environment indicators. Based on the inversion results, a genetic algorithm is used to optimize the structure and weight parameters of the algal bloom risk assessment model to obtain the target algal bloom risk assessment model.

[0181] Assessment module 370 is used to conduct algal bloom risk assessment on target estuarine areas using the target algal bloom risk assessment model.

[0182] Understandable, Figure 3 Each module / unit in the estuarine algal bloom risk assessment device 300 shown has the ability to realize Figure 1 The functions of each step in the estuarine algal bloom risk assessment method 100 shown, and its corresponding technical effects, will not be elaborated here for the sake of brevity.

[0183] Figure 4 This is a structural diagram of an exemplary electronic device capable of implementing embodiments of the present invention. Electronic device 400 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 400 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown in this invention, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0184] like Figure 4 As shown, the electronic device 400 may include a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0185] Multiple components in electronic device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of displays, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0186] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer program product, including a computer program tangibly contained in a computer-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0187] The various embodiments described above in this invention can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), payload programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0188] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0189] In the context of this invention, a computer-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0190] It should be noted that the present invention also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute method 100 and achieve the corresponding technical effects achieved by the embodiments of the present invention in executing the method. For the sake of brevity, they will not be described in detail here.

[0191] In addition, the present invention also provides a computer program product, which includes a computer program that implements method 100 when executed by a processor.

[0192] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this invention does not impose any limitations on them.

[0193] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for risk assessment of estuarine algal blooms based on the optimized fusion of multiple water environment indicators, characterized in that, The method includes: Acquire historical remote sensing image data of the target estuary area; The historical remote sensing image data is preprocessed to obtain historical water body remote sensing image data of the target estuary area. For any key water environment indicator affecting algal blooms in the target estuary area, if a known inversion algorithm exists for the key water environment indicator, then the inversion algorithm for the key water environment indicator is obtained; otherwise, inversion is performed based on the historical water body remote sensing image data and the corresponding historical measured data of the key water environment indicator to obtain the inversion algorithm for the key water environment indicator. Based on the water environment characteristics of the target estuary area, and combined with the historical algal bloom events, local standards, and growth characteristics of dominant algae in the target estuary area, the influence functions of various key water environment indicators on algal blooms are determined respectively. Based on the influence functions of various key water environment indicators on algal blooms, an algal bloom risk assessment model integrating multiple key water environment indicators is established. Various key water environment indicators were inverted using inversion algorithms to retrieve key water environment indicators for the target estuary region during an algal bloom event. Based on the inversion results, a genetic algorithm was used to optimize the structure and weight parameters of the algal bloom risk assessment model, resulting in the target algal bloom risk assessment model. The objective function used in the optimization was constructed using the area of ​​the algal bloom event to quantify the error between the predicted algal bloom area and the actual observed area. In other words, the optimization objective of the genetic algorithm was to minimize the error between the predicted algal bloom area and the measured algal bloom area. The target algal bloom risk assessment model was used to conduct an algal bloom risk assessment of the target estuary area. The key water environment indicators are chlorophyll-a concentration, dissolved inorganic nitrogen concentration, dissolved inorganic phosphorus concentration, water surface temperature, and salinity. The effect function of chlorophyll-a concentration on algal bloom was determined through the following steps: The minimum chlorophyll-a concentration under local standard mild algal bloom conditions in the target estuary area was used as the maximum value of the influence function, and the maximum chlorophyll-a concentration under no algal bloom conditions in the target estuary area was used as the minimum value of the influence function. A linear interpolation method was used to define the influence function value between [0,1], thus obtaining the influence function of chlorophyll-a concentration on algal bloom; specifically, as shown in the following formula: C i =c2×C+c3(C min ≤C≤C max ) C i =1(C>C max ) C i =0(C<C min ) In the formula, C is the chlorophyll-a concentration; C i C represents the effect of chlorophyll-a concentration on algal bloom. min The minimum chlorophyll-a concentration for mild algal bloom at the local standard in the target estuary area; C max c1 represents the maximum chlorophyll-a concentration in the target estuary region without algal blooms; c2 and c3 are constants to be determined. The effect function of dissolved inorganic nitrogen concentration on algal bloom was determined through the following steps: The average dissolved inorganic nitrogen concentration in the target estuary region during a typical algal bloom event is used as the minimum value of the influence function, and the maximum dissolved inorganic nitrogen concentration in the target estuary region during a typical algal bloom event is used as the maximum value of the influence function. A linear interpolation method is used to define the influence function value between [0,1], thus obtaining the influence function of dissolved inorganic nitrogen concentration on algal blooms; specifically, as shown in the following formula: D i < d3×D+d4(D avg ≤D≤D max ) D i =1(D>D max ) D i =0(D<D avg ) In the formula, D is the concentration of dissolved inorganic nitrogen; i D represents the effect of dissolved inorganic nitrogen concentration on algal blooms. avg D represents the average dissolved inorganic nitrogen concentration in the target estuarine region during a typical algal bloom event. max d3 represents the maximum dissolved inorganic nitrogen concentration in the target estuary region during a typical algal bloom event; d4 and d3 are undetermined constants. The effect function of dissolved inorganic phosphorus concentration on algal bloom was determined through the following steps: The average dissolved inorganic phosphorus concentration in the target estuary region during a typical algal bloom event is used as the minimum value of the influence function, and the maximum dissolved inorganic phosphorus concentration in the target estuary region during a typical algal bloom event is used as the maximum value of the influence function. A linear interpolation method is used to define the influence function value between [0,1], thus obtaining the influence function of dissolved inorganic phosphorus concentration on algal blooms; specifically, as shown in the following formula: P i =p1×P+p2(P avg ≤P≤P max ) P i =1(P>P max ) P i =0(P<P avg ) In the formula, P is the concentration of dissolved inorganic phosphorus; P i P represents the effect of dissolved inorganic phosphorus concentration on algal bloom. avg P represents the average dissolved inorganic phosphorus concentration in the target estuary region during a typical algal bloom event; max p1 represents the maximum dissolved inorganic phosphorus concentration in the target estuary region during a typical algal bloom event; p2 and p1 are constants to be determined. The influence function of water surface temperature on algal blooms is determined through the following steps: Based on the growth rates of dominant algae in the target estuary area during past algal blooms at different water surface temperatures, growth curves of the dominant algae were fitted and normalized to obtain the influence function of water surface temperature on algal blooms; specifically, as shown in the following formula: T i =0 (T<0℃ or T>40℃) In the formula, T is the water surface temperature; i t1 and t2 are the function values ​​of the effect of water surface temperature on algal bloom; t1 and t2 are undetermined constants, obtained by fitting the growth curves of dominant algae at different water surface temperatures using polynomial fitting. The effect function of salinity on algal blooms is determined through the following steps: Based on the growth rates of dominant algae in the target estuary area at different salinities during past algal blooms, growth curves of the dominant algae were fitted and normalized to obtain the effect function of salinity on algal blooms; specifically, as shown in the following formula: S i =s5×S 2 +s6×S+s7(6PSU≤S<15PSU) S i =s8×S+s9(15PSU≤S≤35PSU) S i =0 (S < 6 PSU or S > 35 PSU) In the formula, S represents salinity; S i s5 represents the effect of salinity on algal blooms; s6, s7, s8, and s9 are undetermined constants, obtained by fitting the growth curves of dominant algae at different salinities using polynomial fitting.

2. The method according to claim 1, characterized in that, The acquisition of historical remote sensing image data of the target estuary area includes: Based on the geographic information vector file of the target estuary area, the historical remote sensing image data covering the target estuary area is cropped to obtain the historical remote sensing image data of the target estuary area.

3. The method according to claim 1, characterized in that, The key water environment indicators were determined through the following steps: Correlation analysis was conducted between various water environment indicators of the target estuary area and algal bloom events in the target estuary area to obtain the correlation coefficients between various water environment indicators and algal bloom events. Water environment indicators with correlation coefficients greater than or equal to a preset threshold are selected from the various water environment indicators as key water environment indicators.

4. The method according to claim 1, characterized in that, The method further includes: The spatiotemporal distribution of algal bloom risk assessment results is visualized.

Images