Regional nutritive salt standard setting method for high-sediment-content lakes and reservoirs

Abstract

The invention discloses a high sediment content lake and reservoir-oriented regional nutritive salt benchmark making method, which comprises the following steps of S1, collecting regional lake and reservoir nutritive salt, total suspended particulate matters and monitoring data related to a target index, and constructing a regional lake and reservoir nutritive salt benchmark making database for establishing a quantitative response relationship between the nutritive salt and the target index; s2, selecting a specific target index standard value and a regression quantile according to the risk preference of a decision maker or expert experience; s3, adopting a quantile regression neural network model to establish a nonlinear pressure-response relationship between the target index and the total nitrogen, total phosphorus and total suspended particulate matters, and adopting a Bayesian optimization method to perform hyper-parameter optimization; and S4, for a specific lake and reservoir, according to the average total suspended particle concentration of the lake and reservoir, giving a specific target index standard value and a combined nutritive salt standard of total nitrogen and total phosphorus under the regression quantile condition.

Description

Technical Field

[0001] The present invention belongs to the technical field of water quality monitoring and water body management, and in particular relates to a method for formulating a regional nutrient salt benchmark for lakes and reservoirs with high sediment content. Background Art

[0002] Currently, there are multiple methods for establishing nutrient baselines for lakes and reservoirs, including statistical analysis, model inference, and pressure-response models. The pressure-response model, among others, infers nutrient baselines by constructing a relationship between pressure variables and response variables. This method is particularly applicable when dealing with lakes and reservoirs severely impacted by human activities. It can construct an appropriate mathematical model to describe the relationship between pressure variables and response variables, based on the specific characteristics of the study area. Therefore, the pressure-response model is of great significance for establishing nutrient baselines for the unique aquatic environments of lakes and reservoirs with high sediment content.

[0003] Commonly used mathematical models in pressure-response modeling include linear regression and Bayesian hierarchical regression. Linear regression models primarily reveal linear relationships between pressure variables and response variables, while Bayesian hierarchical regression models can explore potential nonlinear relationships. However, in lakes and reservoirs with high sediment loads, due to the unique characteristics of the water environment, the relationship between pressure variables and response variables often exhibits complex nonlinear characteristics, which limits the application of these two classic models. Furthermore, traditional pressure-response modeling typically calculates baseline values ​​for total nitrogen and total phosphorus separately, ignoring the combined effects of different pressure variables. Furthermore, previous methods often use the mean as the evaluation criterion for water quality compliance, an approach that fails to effectively mitigate the impact of extreme values ​​on the results. Therefore, there is an urgent need to develop a method that can handle nonlinear nutrient data and use quantiles as water quality evaluation criteria to construct a combined nutrient baseline suitable for lakes and reservoirs with high sediment loads. Summary of the Invention

[0004] To solve the above problems, the present invention proposes a method for formulating regional nutrient salt benchmarks for lakes and reservoirs with high sediment content. This method formulates quantile evaluation standards for the special water environment of lakes and reservoirs with high sediment content, and can jointly establish nutrient salt benchmarks, which can provide strong technical support for water quality evaluation and ecological management of lakes and reservoirs with high sediment content.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A method for establishing a regional nutrient benchmark for lakes and reservoirs with high sediment content includes the following steps: S1. Collect monitoring data related to nutrients, total suspended particulate matter, and target indicators from regional lakes and reservoirs, and construct a regional lake and reservoir nutrient benchmark database to establish a quantitative response relationship between nutrients and target indicators; S2. Select specific target indicator values ​​and regression quantiles based on the decision maker's risk preference or expert experience; S3. A quantile regression neural network model was used to establish a nonlinear pressure-response relationship between the target indicators and total nitrogen, total phosphorus, and total suspended particulate matter, and the Bayesian optimization method was used for hyperparameter optimization. The specific process of constructing the quantile regression neural network model in step S3 is: S31. Divide the dataset of the regional lake and reservoir nutrient benchmark database into a training set, a validation set, and a test set; S32. Design the neural network architecture, including input layer, hidden layer and output layer, based on the actual situation and data scale; S33. Determine the hyperparameters that need to be optimized and their ranges. Hyperparameters include learning rate, batch size, number of network layers, and number of neurons. Use Bayesian optimization to construct a surrogate model to approximate the true objective function, and use the acquisition function to efficiently search in the hyperparameter space to find the optimal hyperparameter combination. During the optimization process, continuously evaluate the performance of the surrogate model using the validation set, and update the surrogate model based on the evaluation results. S34. Using the training set to train the quantile regression neural network model, during the training process, using the validation set data to input the quantile regression neural network model, adjusting the hyperparameters of the quantile regression neural network model based on the performance on the validation set, and recording the hyperparameter combination at that time when the performance on the validation set reaches the best; S35. Apply the optimal hyperparameter combination obtained in step S34, use the test set to evaluate the performance of the trained quantile regression neural network model, and analyze the impact of environmental parameters such as total nitrogen, total phosphorus, and total suspended particulate matter on the target indicators based on the prediction results of the quantile regression neural network model; S4. For specific lakes and reservoirs, provide a specific target indicator compliance value and a combined nutrient benchmark for total nitrogen and total phosphorus under regression quantile conditions based on the average total suspended particulate concentration of the lake or reservoir; The specific process of step S4 is: S41. Calculate the average total suspended particle concentration of lakes and reservoirs with high sediment content based on the target indicator compliance value and regression quantile finally determined in step S2; S42. Using the trained quantile regression neural network model, an iterative search method is used to find the total nitrogen and total phosphorus values ​​of a specific lake reservoir under given target indicator standards and regression quantiles; S43. Generate contour maps of the total nitrogen and total phosphorus concentration values ​​to obtain a combined nutrient salt benchmark for total nitrogen and total phosphorus, which is used to visually display the areas where the target indicators reach the target values ​​at different total nitrogen and total phosphorus levels in specific high sediment content lakes and reservoirs.

[0006] Preferably, the target indicator includes chlorophyll a concentration; in step S2, the target indicator reaching the standard value is a chlorophyll a concentration equal to 0.010 mg / L, and the regression quantile is 90%.

[0007] Preferably, the specific process of step S1 is: S11. Collect data on nutrient concentrations in lakes and reservoirs, including total nitrogen and total phosphorus; S12. Collect ecological response data related to nutrient concentrations and use the ecological response data as target indicators; S13. Collect total suspended particulate matter concentrations in lakes and reservoirs and other relevant water quality data; S14. Perform quality control on monitoring data, including removing abnormal data, removing duplicate data, and resampling data; S15. Process missing values ​​in monitoring data and use Kalman filtering to fill in missing values. By fusing measurement values ​​and state estimation, the accuracy of state estimation is improved, missing value filling is completed, and a regional lake and reservoir nutrient benchmark database is constructed.

[0008] Preferably, the elimination of abnormal data in step S14 is to eliminate non-numeric data, wherein non-numeric data includes characters and null values; the elimination of duplicate data is to eliminate duplicate data in the time series; and the data resampling is to unify the data frequency to the daily frequency by taking the median method.

[0009] Preferably, the specific process of step S32 is: S321. Determine the input layer, where the input features include total nitrogen, total phosphorus, and total suspended particulate matter; S322. Design hidden layers. The number of hidden layers is obtained through cross-validation, and the hyperbolic tangent function is used as the activation function to handle nonlinear relationships. S323. Determine the output layer, where the output features are chlorophyll a concentration and its quantile estimation. At the same time, set the quantile loss function in the output layer, and adjust the weights and bias of the quantile regression neural network model through the gradient descent optimization algorithm to minimize the error between the predicted value and the actual value of the quantile regression neural network model at a specific quantile.

[0010] Preferably, the specific steps of optimizing the hyperparameters using the Bayesian optimization method in step S33 are: S331. Define the parameter space and determine the hyperparameters that need to be optimized. The hyperparameters include the number of layers of the neural network, the number of neurons in each layer, and the learning rate. S332, using Gaussian processes as surrogate models for true approximate objective functions; S333, select the next potential hyperparameter combination for evaluation by obtaining the function; S334. Train a quantile regression neural network on the selected hyperparameter combination and evaluate the performance on the validation set. S335, updating the agent model using the new evaluation results; S336. Repeat steps S333-S335 until a stopping condition is met, where the stopping condition is the iteration upper limit or performance convergence.

[0011] After adopting the above technical solution, the present invention has the following beneficial effects: the regional nutrient salt benchmark formulation method of the present invention is aimed at lakes and reservoirs with high sediment content. By constructing a quantile regression neural network model to output the target index and its quantile estimated value, the quantile target value is used as the water quality compliance evaluation standard, avoiding the extreme value problem of using the mean target value as the water quality compliance evaluation standard. At the same time, the quantile regression neural network, as a neural network model, performs better than the traditional machine learning model when processing complex nonlinear high-dimensional water quality data. When inferring the nutrient salt benchmark for a specific lake and reservoir with high sediment content, a set of total nitrogen and total phosphorus concentrations that meet specific conditions are generated, and a joint nutrient salt benchmark for the specific lake and reservoir can be established, thereby fully considering the joint relationship between total nitrogen and total phosphorus. Therefore, the present invention not only provides a more scientific water quality compliance evaluation standard, but also formulates a more targeted and accurate joint nutrient salt benchmark, providing effective support for lake and reservoir water environment management, and has significant advantages and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 Flowchart of the present invention; Figure 2 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0014] like Figure 1 and Figure 2 As shown in FIG, a method for establishing a regional nutrient benchmark for lakes and reservoirs with high sediment content includes the following steps: S1. Collect monitoring data related to nutrients, total suspended particulate matter, and target indicators from regional lakes and reservoirs, and construct a regional lake and reservoir nutrient benchmark database for establishing a quantitative response relationship between nutrients and target indicators; the target indicator includes chlorophyll a concentration; The specific process of step S1 is: S11. Collect data on nutrient concentrations in lakes and reservoirs, including total nitrogen and total phosphorus; S12. Collect ecological response data related to nutrient concentrations and use the ecological response data as target indicators; S13. Collect total suspended particulate matter concentrations in lakes and reservoirs and other relevant water quality data; S14. Perform quality control on monitoring data, including removing abnormal data, removing duplicate data, and resampling data; In step S14, the elimination of abnormal data is to eliminate non-numeric data, wherein non-numeric data includes characters and null values; the elimination of duplicate data is to eliminate duplicate data in the time series; the data resampling is to unify the data frequency to the daily frequency by taking the median method; S15. Process missing values ​​in the monitoring data and fill them with Kalman filtering. By fusing the measured values ​​and state estimation, the accuracy of state estimation is improved, the missing values ​​are filled, and a regional lake and reservoir nutrient benchmark database is constructed. S2. Select specific target indicator values ​​and regression quantiles based on the decision maker's risk preference or expert experience; In step S2, the target indicator standard value is chlorophyll a concentration equal to 0.010 mg / L, and the regression quantile is 90%; S3. A quantile regression neural network model (QRNN model) was used to establish a nonlinear pressure-response relationship between the target indicators and total nitrogen, total phosphorus, and total suspended particulate matter, and the Bayesian optimization method was used for hyperparameter optimization. The specific process of constructing the quantile regression neural network model in step S3 is: S31. Divide the dataset of the regional lake and reservoir nutrient benchmark database into a training set, a validation set, and a test set; S32. Design the neural network architecture, including input layer, hidden layer and output layer, based on the actual situation and data scale; The specific process of step S32 is: S321. Determine the input layer, where the input features include total nitrogen, total phosphorus, and total suspended particulate matter; S322. Design hidden layers. The number of hidden layers is obtained through cross-validation, and the hyperbolic tangent function is used as the activation function to handle nonlinear relationships. S323. Determine an output layer, where the output features are chlorophyll a concentration and its quantile estimation. Set a quantile loss function in the output layer and adjust the weights and biases of the quantile regression neural network model using a gradient descent optimization algorithm to minimize the error between the predicted value and the actual value of the quantile regression neural network model at a specific quantile. S33. Determine the hyperparameters that need to be optimized and their ranges. Hyperparameters include learning rate, batch size, number of network layers, and number of neurons. Use Bayesian optimization to construct a surrogate model to approximate the true objective function, and use the acquisition function to efficiently search in the hyperparameter space to find the optimal hyperparameter combination. During the optimization process, continuously evaluate the performance of the surrogate model using the validation set, and update the surrogate model based on the evaluation results. The specific steps of optimizing hyperparameters using the Bayesian optimization method in step S33 are: S331. Define the parameter space and determine the hyperparameters that need to be optimized. The hyperparameters include the number of layers of the neural network, the number of neurons in each layer, and the learning rate. S332, using Gaussian processes as surrogate models for true approximate objective functions; S333, select the next potential hyperparameter combination for evaluation by obtaining the function; S334. Train a quantile regression neural network on the selected hyperparameter combination and evaluate the performance on the validation set. S335, updating the agent model using the new evaluation results; S336, repeat steps S333-S335 until a stopping condition is met, where the stopping condition is an iteration upper limit or performance convergence; S34. Using the training set to train the quantile regression neural network model, during the training process, using the validation set data to input the quantile regression neural network model, adjusting the hyperparameters of the quantile regression neural network model based on the performance on the validation set, and recording the hyperparameter combination at that time when the performance on the validation set reaches the best; S35. Apply the optimal hyperparameter combination obtained in step S34, use the test set to evaluate the performance of the trained quantile regression neural network model, and analyze the impact of environmental parameters such as total nitrogen, total phosphorus, and total suspended particulate matter on the target indicators based on the prediction results of the quantile regression neural network model; S4. For specific lakes and reservoirs, provide a joint nutrient benchmark for total nitrogen (TN) and total phosphorus (TP) under specific target indicator attainment values ​​and regression quantile conditions based on the average total suspended particulate concentration of the lake or reservoir; The specific process of step S4 is: S41. Calculate the average total suspended particle concentration of lakes and reservoirs with high sediment content based on the target indicator compliance value and regression quantile finally determined in step S2; S42. Using the trained quantile regression neural network model, an iterative search method is used to find the total nitrogen and total phosphorus values ​​of a specific lake reservoir under given target indicator standards and regression quantiles; S43. Generate contour maps of the total nitrogen and total phosphorus concentration values ​​to obtain a combined nutrient salt benchmark for total nitrogen and total phosphorus, which is used to visually display the areas where the target indicators reach the target values ​​at different total nitrogen and total phosphorus levels in specific high sediment content lakes and reservoirs.

[0015] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for establishing a regional nutrient salt benchmark for lakes and reservoirs with high sediment content, characterized in that: The following steps are involved: S1. Collect monitoring data related to nutrients, total suspended particulate matter and target indicators of regional lakes and reservoirs, and build a regional lake and reservoir nutrient benchmark database to establish a quantitative response relationship between nutrients and target indicators; S2. Select specific target indicator values ​​and regression quantiles based on the decision maker’s risk preference or expert experience; S3. The quantile regression neural network model was used to establish the nonlinear pressure-response relationship between the target index and total nitrogen, total phosphorus and total suspended particulate matter, and the Bayesian optimization method was used for hyperparameter optimization; The specific process of constructing the quantile regression neural network model in step S3 is: S31. Divide the data set of the regional lake nutrient benchmark database into a training set, a validation set and a test set; S32. Design the neural network architecture, including input layer, hidden layer and output layer, according to the actual situation and data scale; S33, determining the hyperparameters to be optimized and the value ranges of the hyperparameters, the hyperparameters including the learning rate, the batch size, the number of network layers, and the number of neurons; The Bayesian optimization method is used to build a proxy model to approximate the true objective function, and the acquisition function is used to efficiently search in the hyperparameter space to find the best hyperparameter combination. During the optimization process, the performance of the proxy model is continuously evaluated using the validation set, and the proxy model is updated based on the evaluation results. S34, using the training set to train the quantile regression neural network model, during the training process, using the validation set data to input the quantile regression neural network model, adjusting the hyperparameters of the quantile regression neural network model according to the performance on the validation set, and recording the hyperparameter combination at this time when the performance on the validation set reaches the best; S35, applying the best hyperparameter combination obtained in step S34, using the test set to evaluate the performance of the trained quantile regression neural network model, and analyzing the influence of environmental parameters such as total nitrogen, total phosphorus and total suspended particulate matter on the target indicators based on the prediction results of the quantile regression neural network model; S4. For specific lakes and reservoirs, based on the average total suspended particle concentration of the lakes and reservoirs, provide specific target indicator compliance values ​​and a combined nutrient benchmark of total nitrogen and total phosphorus under regression quantile conditions; The specific process of step S4 is: S41, calculating the average total suspended particle concentration of lakes and reservoirs with high sediment content according to the target indicator standard value and regression quantile finally determined in step S2; S42. Using the trained quantile regression neural network model, the total nitrogen and total phosphorus values ​​of a specific lake reservoir are found under given target index attainment values ​​and regression quantiles through an iterative search method; S43. Generate contour maps of total nitrogen and total phosphorus concentration values ​​to obtain a joint nutrient benchmark of total nitrogen and total phosphorus, which is used to visually display the areas where target indicators reach target values ​​at different total nitrogen and total phosphorus levels in specific high sediment content lakes and reservoirs.

2. A method for establishing a regional nutrient salt benchmark for lakes and reservoirs with high sediment content as claimed in claim 1, characterized in that: The target indicator includes chlorophyll a concentration; in step S2, the target indicator reaching the standard value is chlorophyll a concentration equal to 0.010 mg / L, and the regression quantile is 90%.

3. A method for establishing a regional nutrient salt benchmark for lakes and reservoirs with high sediment content as claimed in claim 1, characterized in that: The specific process of step S1 is: S11. Collect the concentration data of nutrients in lakes and reservoirs, including total nitrogen and total phosphorus; S12. Collect ecological response data related to nutrient concentration and use the ecological response data as target indicators; S13. Collect total suspended particulate matter concentration in lakes and reservoirs and other relevant water quality data; S14. Conduct quality control on monitoring data, including eliminating abnormal data, eliminating duplicate data, and resampling data; S15. Process the missing values ​​of the monitoring data and use the Kalman filter method to fill in the missing values. By fusing the measured values ​​and state estimation, the accuracy of the state estimation is improved, the missing values ​​are filled in, and a regional lake and reservoir nutrient benchmark database is constructed.

4. A method for establishing a regional nutrient salt benchmark for lakes and reservoirs with high sediment content as claimed in claim 3, characterized in that: The elimination of abnormal data in step S14 is to eliminate non-numeric data, wherein non-numeric data includes characters and null values; the elimination of duplicate data is to eliminate duplicate data in the time series; and the data resampling is to unify the data frequency to the daily frequency by taking the median method.

5. A method for establishing a regional nutrient salt benchmark for lakes and reservoirs with high sediment content as claimed in claim 1, characterized in that: The specific process of step S32 is: S321, determine the input layer, the input characteristics include total nitrogen, total phosphorus and total suspended particulate matter; S322, designing hidden layers, the number of hidden layers is obtained through cross validation, and a hyperbolic tangent function is used as an activation function to process nonlinear relationships; S323. Determine the output layer, the output features are chlorophyll a concentration and its quantile estimation, and set the quantile loss function in the output layer. Adjust the weights and bias of the quantile regression neural network model through the gradient descent optimization algorithm to minimize the error between the predicted value and the actual value of the quantile regression neural network model at a specific quantile.

6. A method for establishing a regional nutrient salt benchmark for lakes and reservoirs with high sediment content as claimed in claim 1, characterized in that: The specific steps of optimizing the hyperparameters using the Bayesian optimization method in step S33 are: S331, define the parameter space and determine the hyperparameters that need to be optimized, the hyperparameters including the number of layers of the neural network, the number of neurons in each layer, and the learning rate; S332, using Gaussian process as a proxy model for the true approximate objective function; S333, selecting the next potential hyperparameter combination for evaluation by obtaining a function; S334. Train a quantile regression neural network on the selected hyperparameter combination and evaluate the performance on the validation set. S335, updating the proxy model using the new evaluation result; S336. Repeat steps S333-S335 until a stopping condition is met, where the stopping condition is an iteration upper limit or performance convergence.

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