Method and system for determining AMC grade based on soil moisture content monitoring data

Abstract

A method and a system for determining an AMC grade based on soil moisture content monitoring data relate to the technical field of hydrology and water resource management, and comprise the following steps: (1) soil moisture content data acquisition: monitoring soil volumetric water content by using a soil moisture sensor, and recording environment temperature and humidity data at the same time; (2) data preprocessing: denoising and filtering the collected original data of the relative water content (RWC) of soil; and (3) establishing a corresponding relation between the soil relative water content RWC and the AMC grade. According to the method, a piecewise linear regression model of RWC and AMC levels is established, quantitative relation modeling of the RWC and the AMC is realized, a complete model construction, verification and application technical route is provided, and practical engineering application is supported.

Description

Technical Field

[0001] This invention relates to the field of hydrological and water resources management technology, and in particular to a method and system for determining the level of early soil moisture (AMC) based on soil moisture monitoring data. Background Technology

[0002] In hydrological and water resources management technology research, the antecedent moisture condition (AMC) of soil is a crucial factor influencing runoff formation and soil erosion. Currently, the determination of AMC levels mainly relies on empirical judgment and indirect estimation, which presents the following problems:

[0003] 1. Traditional methods usually determine the AMC level based on the cumulative rainfall in the 5 days before the rainfall. This method fails to directly reflect the actual soil moisture status, especially under different soil types and vegetation cover conditions, the same amount of rainfall may lead to different soil moisture levels.

[0004] 2. In the existing SCS-CN model, the determination of AMC level is relatively crude, divided into three categories: AMC I (dry), AMC II (moderate) and AMC III (humid). This simplified classification fails to fully consider regional differences and the role of real-time monitoring data.

[0005] 3. Existing technologies lack methods for establishing a precise correspondence between soil moisture monitoring data and AMC levels.

[0006] Therefore, it is necessary to develop a method for determining the AMC level based on actual soil moisture monitoring data to improve the accuracy and practicality of hydrological model calculations. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for determining AMC level based on soil moisture monitoring data, thereby solving the problems existing in the prior art.

[0008] The present invention adopts the following technical solution: A method for determining AMC level based on soil moisture monitoring data includes the following steps: (1) Soil moisture data collection: Soil volumetric water content is monitored using a soil moisture sensor, while ambient temperature and humidity data are recorded. Preferably, in step (1), the data acquisition frequency can be set as needed, generally once every 15 minutes to 1 hour.

[0009] (2) Data preprocessing: The raw soil relative water content (RWC) data were denoised and filtered. Preferably, in step (2), the relative soil moisture content (RWC) is obtained directly by front-end soil monitoring equipment.

[0010] (3) Establish the correspondence between soil relative water content (RWC) and AMC grade: When RWC < 51.8%, it is classified as AMC Grade I (dry); When 51.8% ≤ RWC < 81.1%, it is judged as AMC II (medium humidity); When RWC ≥ 81.1%, it is judged as AMC Level III (wet); Preferably, in step (3), the method includes establishing a correspondence between soil relative moisture content and AMC grade using historical data regression analysis; the historical data regression analysis includes the following steps: (3-1) Historical data collection: Collect at least 3 years of soil moisture monitoring data, including relative soil moisture content data under different seasons and climatic conditions, rainfall and evaporation data for the corresponding period, and environmental temperature and humidity data; (3-2) Data Structure Design: Establish a unified data structure containing the following fields: Date and time, relative soil moisture content (RWC), ambient temperature (°C), ambient humidity (%), rainfall (mm), evaporation (mm), and soil type; (3-3) Data cleaning: Remove obvious RWC outliers, process missing data, identify and correct outliers based on adjacent sensor data and historical data, and standardize the data; Preferably, in (3-3), data with RWC values ​​significantly exceeding the range of [0,100]% are removed, and time periods with continuous data missing for more than 24 hours are checked and removed; The criteria for identifying outliers are as follows: outliers are identified based on the 3 sigma principle. When the difference between the monitoring data and the mean of the adjacent sensor data exceeds 3 times the standard deviation, it is considered an outlier; when the difference between the monitoring data and the mean of the historical data for the same period exceeds 3 times the standard deviation, it is considered an outlier. Correction method: Replace with the arithmetic mean of the previous three valid monitoring values ​​for this location.

[0011] (3-4) Regression model establishment: A piecewise linear regression method was used to establish a correspondence between soil relative water content (RWC) data and AMC level; Preferably, in (3-4), based on the AMC grading standard, the following piecewise linear regression model can be established: AMC = β0 + β1 × RWC + β2 × (RWC - a) + β3 × (RWC - b) (1) Where a and b represent the critical values ​​of RWC corresponding to the three levels of AMC, β0-β3 are the coefficients to be estimated, β0 is the intercept term, β1 is the slope of the first segment, β2 is the change in the slope of the second segment, and β3 is the change in the slope of the third segment, which are calculated by regression based on the relationship between AMC and RWC from historical data. (RWC - a) means that the value is (RWC - a) when RWC > a, and 0 otherwise; (RWC - b) means that the value is (RWC - b) when RWC > b, and 0 otherwise.

[0012] (3-5) Parameter optimization: The least squares method is used to optimize the parameters of the regression model and determine the optimal segmentation point (i.e., the values ​​of a and b).

[0013] Preferably, in (3-5), the steps include the following: (3-5-1) Establish the objective function: min J(β0, β1, β2, β3, a, b) = Σ[AMC_obs - ]²(2) in: = β0 + β1 × RWC + β2 × (RWC - a) + + β3 × (RWC - b) (3-5-2) Take the partial derivative with respect to each parameter: Partial derivative with respect to β0: J / β0 = -2Σ[AMC_obs - ] Partial derivative with respect to β1: J / β1 = -2Σ{[AMC_obs - ] × RWC} Partial derivative with respect to β2: J / β2 = -2Σ{[AMC_obs - ] × (RWC - a)} Partial derivative with respect to β3: J / β3 = -2Σ{[AMC_obs - ] × (RWC - b)} Partial derivative with respect to a: J / a = 2Σ{[AMC_obs - ] × β2 × I(RWC>a)} Where I(·) is the indicator function; Partial derivative with respect to b: J / b = 2Σ{[AMC_obs - ] × β3 × I(RWC>b)}; (3-5-3) Parameter initialization: β0 (0) = 0, β1 (0) = 0, β2 (0) = 0, β3 (0) = 0, a (0) = 50%, b (0) = 80% (initial value set based on experience), learning rate α = 0.001; (3-5-4) Iterative update formula: β0 (k+1) = β0 (k) - α × J / β0 β1 (k+1) = β1 (k) - α × J / β1 β2 (k+1) = β2 (k) - α × J / β2 β3 (k+1) = β3 (k) - α × J / β3 a (k+1) = a (k) - α × J / a b (k+1) = b (k) - α × J / b Convergence condition: |J (k+1) - J (k) |<ε,ε = 10 -6 ; or reaching the maximum number of iterations, k max = 10000; (3-5-5) Iteratively solve the parameters β0, β1, β2, β3 and critical values ​​a and b based on historical data samples; find the values ​​of RWC critical values ​​a and b, a=51.8%, b=81.1%, and generate the relationship curve between RWC and AMC level.

[0014] Based on the calculated RWC value, RWC is divided into three categories according to AMC level: When RWC < 51.8%, it is classified as AMC Grade I (dry); When 51.8% ≤ RWC < 81.1%, it is judged as AMC II (medium humidity); When RWC ≥ 81.1%, it is classified as AMC Level III (wet).

[0015] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of a method for determining AMC levels based on soil moisture monitoring data as described above.

[0016] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to perform the steps of the aforementioned method for determining AMC level based on soil moisture monitoring data.

[0017] The beneficial effects of this invention are as follows: This invention determines the AMC level by directly monitoring the relative moisture content of the soil, which is more accurate and intuitive than the traditional empirical judgment method based on rainfall, and can truly reflect the actual moisture status of the soil.

[0018] This invention establishes a clear correspondence between relative soil moisture content and AMC level, generates a relationship curve between RWC and AMC level, provides more accurate AMC input parameters for hydrological models, and is easy to operate and reliable in terms of results.

[0019] A piecewise linear regression model for RWC and AMC levels was established, realizing the quantitative relationship model between the two. A complete technical route for model construction, validation and application was provided to support practical engineering applications.

[0020] The method of this invention can determine the AMC level in real time and automatically, which improves the calculation efficiency and accuracy of hydrological models and is particularly suitable for real-time monitoring and early warning systems.

[0021] The soil moisture sensor technology used in this invention is mature, the monitoring data is reliable, and the method is highly adaptable, making it widely applicable to different regions and different types of soil. Attached Figure Description

[0022] Figure 1The flowchart of the method for this invention is shown below; Figure 2 The curve showing the relationship between AMC and RWC levels. Detailed Implementation

[0023] The present invention will be further described in detail below with reference to embodiments, but it is not intended to limit the scope of protection of the present invention.

[0024] Example 1 A method for determining AMC level based on soil moisture monitoring data, such as Figure 1 As shown, it includes the following steps: (1) Soil moisture data collection: Install soil monitoring equipment in the monitoring area, use soil moisture sensors to monitor soil volumetric water content, and install temperature and humidity sensors to record ambient temperature and humidity data. In step (1), the data acquisition frequency can be set as needed, generally once every 15 minutes to 1 hour.

[0025] (2) Data preprocessing: The raw soil relative water content (RWC) data were denoised and filtered. In step (2), the relative soil moisture content (RWC) is directly monitored and obtained by the front-end soil monitoring equipment.

[0026] (3) Establish the correspondence between soil relative water content (RWC) and AMC grade: In step (3), the method includes using historical data regression analysis to establish the correspondence between soil relative moisture content and AMC grade; the regression analysis based on historical data includes the following steps: (3-1) Historical data collection: Collect at least 3 years of soil moisture monitoring data, including relative soil moisture content data under different seasons and climatic conditions, rainfall and evaporation data for the corresponding period, and environmental temperature and humidity data; (3-2) Data Structure Design: Establish a unified data structure containing the following fields: Date and time, relative soil moisture content (RWC), ambient temperature (°C), ambient humidity (%), rainfall (mm), evaporation (mm), and soil type; (3-3) Data cleaning: Remove obvious RWC outliers, process missing data, identify and correct outliers based on adjacent sensor data and historical data, and standardize the data; Remove data whose RWC values ​​significantly exceed the range of [0, 100]%, and check and remove periods of consecutive data missing for more than 24 hours. The criteria for identifying outliers are as follows: outliers are identified based on the 3 sigma principle. When the difference between the monitoring data and the mean of the adjacent sensor data exceeds 3 times the standard deviation, it is considered an outlier; when the difference between the monitoring data and the mean of the historical data for the same period exceeds 3 times the standard deviation, it is considered an outlier. Correction method: Replace with the arithmetic mean of the previous three valid monitoring values ​​for this location.

[0027] (3-4) Regression model establishment: A piecewise linear regression method was used to establish a correspondence between soil relative water content (RWC) data and AMC level; Based on the AMC grading criteria, the following piecewise linear regression model can be established: AMC = β0 + β1 × RWC + β2 × (RWC - a) + β3 × (RWC - b) (1) Where a and b represent the critical values ​​of RWC corresponding to the three levels of AMC, β0-β3 are the coefficients to be estimated, β0 is the intercept term, β1 is the slope of the first segment, β2 is the change in the slope of the second segment, and β3 is the change in the slope of the third segment, which are calculated by regression based on the relationship between AMC and RWC from historical data. (RWC - a) means that the value is (RWC - a) when RWC > a, and 0 otherwise; (RWC - b) means that the value is (RWC - b) when RWC > b, and 0 otherwise.

[0028] (3-5) Parameter optimization: The least squares method is used to optimize the parameters of the regression model and determine the optimal segmentation point (i.e., the values ​​of a and b).

[0029] The steps include the following: (3-5-1) Establish the objective function: min J(β0, β1, β2, β3, a, b) = Σ[AMC_obs - ]²(2) in: = β0 + β1 × RWC + β2 × (RWC - a) + + β3 × (RWC - b) (3-5-2) Take the partial derivative with respect to each parameter: Partial derivative with respect to β0: J / β0 = -2Σ[AMC_obs - ] Partial derivative with respect to β1: J / β1 = -2Σ{[AMC_obs - ] × RWC} Partial derivative with respect to β2: J / β2 = -2Σ{[AMC_obs - ] × (RWC - a)} Partial derivative with respect to β3: J / β3 = -2Σ{[AMC_obs - ] × (RWC - b)} Partial derivative with respect to a: J / a = 2Σ{[AMC_obs - ] × β2 × I(RWC>a)} Where I(·) is the indicator function; Partial derivative with respect to b: J / b = 2Σ{[AMC_obs - ] × β3 × I(RWC>b)}; (3-5-3) Parameter initialization: β0 (0) = 0, β1 (0) = 0, β2 (0) = 0, β3 (0) = 0, a (0) = 50%, b (0) = 80% (initial value set based on experience), learning rate α = 0.001; (3-5-4) Iterative update formula: β0 (k+1) = β0 (k) - α × J / β0 β1 (k+1) = β1 (k) - α × J / β1 β2 (k+1) = β2 (k) - α × J / β2 β3 (k+1) = β3 (k) - α × J / β3 a (k+1) = a(k) - α × J / a b (k+1) = b (k) - α × J / b Convergence condition: |J (k+1) - J (k) |<ε,ε = 10 -6 ; or reaching the maximum number of iterations, k max = 10000; (3-5-5) Iteratively solve for parameters β0, β1, β2, β3 and critical values ​​a and b based on historical data samples; calculate the RWC critical values ​​a and b, a = 51.8%, b = 81.1%, and simultaneously generate the relationship curve between RWC and AMC level, such as... Figure 2 As shown.

[0030] Based on the calculated RWC value, RWC is divided into three categories according to AMC level: When RWC < 51.8%, it is classified as AMC Grade I (dry); When 51.8% ≤ RWC < 81.1%, it is judged as AMC II (medium humidity); When RWC ≥ 81.1%, it is classified as AMC Level III (wet).

[0031] Example 2 A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of a method for determining AMC levels based on soil moisture monitoring data as described above.

[0032] Example 3 An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to perform the steps of the aforementioned method for determining AMC level based on soil moisture monitoring data.

[0033] Experimental Example In a certain experimental watershed, FDR soil moisture sensors were installed to monitor the soil volumetric water content in the 0-20cm soil layer. Soil experiments revealed that the RWC was 75%. According to the judgment criteria, 75% falls within the range of 51.8%-81.1%, therefore the AMC level was determined to be AMC II.

[0034] Studies have shown that using relative soil moisture content to determine AMC levels is more accurate and intuitive than traditional empirical methods based on rainfall, and can truly reflect the actual soil moisture status, thereby improving the calculation accuracy of hydrological models and soil erosion models.

Claims

1. A method for determining AMC class based on soil moisture monitoring data, characterized in that, The method comprises the following steps: (1) Soil moisture data collection: soil volume water content is monitored by using a soil moisture sensor, and environmental temperature and humidity data are recorded; (2) Data preprocessing: the original soil relative water content (RWC) data collected are subjected to denoising and filtering processing; (3) Corresponding relationship between soil relative water content RWC and AMC grade is established: When RWC < 51.8%, AMC I grade (dry) is determined; When 51.8% ≤ RWC < 81.1%, AMC II grade (moderate wet) is determined; When RWC ≥ 81.1%, AMC III grade (wet) is determined.

2. The method of determining AMC class based on soil moisture monitoring data according to claim 1, characterized in that, In step (1), the data collection frequency is 15 minutes to 1 hour once.

3. The method of determining AMC class based on soil moisture monitoring data according to claim 1, characterized in that, In step (2), the soil relative water content (RWC) is directly monitored and obtained by a front-end soil monitoring device.

4. The method of determining AMC class based on soil moisture monitoring data according to claim 1, characterized in that, In step (3), the establishment method comprises the following steps: (3-1) Historical data collection: soil moisture monitoring data for at least 3 years are collected, including soil relative water content data under different seasons and different climate conditions, rainfall and evaporation data in the corresponding period, and environmental temperature and humidity data; (3-2) Data structure design: a unified data structure is established, which comprises the following fields: date and time, soil relative water content RWC, environmental temperature, environmental humidity, rainfall, evaporation, and soil type; (3-3) Data cleaning: obvious RWC outliers are removed, missing data are processed, outliers are identified and corrected according to adjacent sensor data and historical data, and data are standardized; (3-4) Regression model establishment: a segmented linear regression method is adopted to establish the corresponding relationship between soil relative water content RWC data and AMC grade; (3-5) Parameter optimization: the least square method is used to optimize the regression model parameters to determine the best segmentation point.

5. The method of determining AMC class based on soil moisture monitoring data according to claim 4, characterized in that, In (3-3), data with RWC values obviously exceeding the range of [0, 100]% are removed, and time periods with continuous data missing for more than 24 hours are checked and removed; The judgment principle of outliers is: according to the 3-sigma principle, when the difference between the monitoring data and the mean value of adjacent sensor data exceeds 3 times the standard deviation, the data is determined to be an outlier; when the difference between the monitoring data and the mean value of historical data of the same period exceeds 3 times the standard deviation, the data is determined to be an outlier; The correction method is: the arithmetic mean of the previous three effective monitoring values of the point is used to replace the data.

6. The method of determining AMC class based on soil moisture monitoring data according to claim 4, characterized in that, In (3-4), based on the AMC grade division standard, the following segmented linear regression model is established: AMC = β0 + β1 × RWC + β2 × (RWC - a) + β3 × (RWC - b) (1) Wherein a, b represent the critical value of RWC corresponding to the three levels of AMC respectively, β0-β3 are to be estimated coefficients, β0 is the intercept term, β1 is the first segment slope, β2 is the second segment slope change, β3 is the third segment slope change, which is calculated according to the relationship between AMC and RWC by regression of historical data; (RWC-a) represents the value of (RWC-a) when RWC>a, otherwise 0; (RWC-b) represents the value of (RWC-b) when RWC>b, otherwise 0.

7. The method of determining AMC class based on soil moisture monitoring data according to claim 4, characterized in that, In (3-5), the steps are as follows: (3-5-1), establishing the objective function: min J(β0, β1, β2, β3, a, b) = Σ[AMC_obs - ]²(2) where: = β0 + β1 × RWC + β2 × (RWC - a) + + β3 × (RWC - b) (3-5-2), taking the partial derivative of each parameter: Partial derivatives with respect to β0: J / β0 = -2Σ[AMC_obs - ] Partial derivative with respect to β1: J / β1 = -2Σ{[AMC_obs - ] × RWC} Partial derivative of β2: J / β2 = -2Σ{[AMC_obs - ] × (RWC - a)} Partial derivative with respect to β3: J / β3 = -2Σ{[AMC_obs - ] × (RWC - b)} Partial derivative of a: J / a = 2Σ{[AMC_obs - ] × β2 × I(RWC > a)} Wherein I(·) is the indicator function; Partial derivative with respect to b: J / b = 2Σ{[AMC_obs - ] × β3 × I(RWC > b)}; (3-5-3), parameter initialization: β0 (0) = 0, β1 (0) = 0, β2 (0) = 0, β3 (0) = 0, a (0) = 50%, b (0) = 80%, learning rate a = 0.001; (3-5-4), iterative update formula: β0 (k+1) = β0 (k) - α × J / β0 β1 (k+1) = β1 (k) - α × J / β1 β2 (k+1) = β2 (k) - α × J / β2 β3 (k+1) = β3 (k) - α × J / β3 a (k+1) = a (k) - α × J / a b (k+1) = b (k) - α × J / b Convergence condition: |J (k+1) - J (k) | < ε, ε = 10 -6 ; or maximum number of iterations, k max = 10000; (3-5-5), according to the historical data sample to solve the parameters β0, β1, β2, β3 and the critical value a, b; the value of RWC critical value a and b is a=51.8%, b=81.1%, and the relationship curve of RWC and AMC level is generated at the same time.

8. A computer-readable storage medium, characterized in that, A program is stored thereon, which is executed by the processor to implement the steps in the method for determining AMC level based on soil moisture monitoring data as described above.

9. An electronic device, comprising: A memory, a processor and a program stored on the memory and executable on the processor are included, and the processor implements the steps in the method for determining AMC level based on soil moisture monitoring data as described above when executing the program.

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