A hydrological forecasting method and system

Abstract

The present invention discloses a hydrological forecasting method and system, which relates to the technical field of hydrological forecasting. By performing a data-driven analysis on the initial values of each key parameter in the hydrological forecasting model, the second optimal initial value of each key parameter is obtained. During the obtaining process, by combining the correlation coefficient with the upper and lower limits of the threshold calculated based on the error value, a more stringent standard threshold interval can be obtained, so that the error value can be judged more reasonably and scientifically, and the initial values of each key parameter that meet the conditions can be strictly screened out. Furthermore, the hydrological forecasting model can be calibrated more scientifically and effectively, so as to effectively improve the accuracy of the prediction results of the subsequent hydrological forecasting model.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological forecasting, and particularly to a hydrological forecasting method and system. Background Art

[0002] Hydrological forecasting is the process of predicting the water volume changes of rivers, lakes and other water bodies. The existing hydrological forecasting process generally is as follows: First, historical hydrological data is collected through ground observation stations, meteorological stations, satellite remote sensing, etc. For example, real-time or regular hydrological data is obtained through hydrological stations on rivers such as flow meters, water level gauges, rain gauges, etc. After collecting the corresponding data, preprocessing is performed, including steps such as removing outliers, filling in missing values, unifying measurement units, and time alignment to ensure the quality and consistency of the data. At the same time, feature extraction is also included to obtain the key parameters for constructing a hydrological forecasting model such as a distributed hydrological model. Then, based on a literature review, initial values are set for each key parameter, and the actual measured values of the current hydrological data are used as the input conditions of the model for the first simulation prediction. During the continuous prediction and simulation process, the initial values of each key parameter in the model need to be continuously adjusted to seek the optimal initial values of the key parameters, so as to ensure the accuracy of subsequent model predictions;

[0003] However, the existing process of obtaining the optimal initial values of each key parameter in the hydrological forecasting model still depends on determining the error value in the form of setting a threshold interval for the error value, and determining whether the current initial value is the optimal initial value based on the determination result. Although this method is simple and intuitive, it often ignores the problem of the overall fitting degree of the model, resulting in a lack of certain scientificity in the existing method of determining the error value. At the same time, the existing setting of the threshold interval for the error value is usually determined and divided based on expert experience. Therefore, the division is also made on the premise of lacking a reliable data basis, making the application of the current threshold interval prone to misjudgment of the error value, thus easily causing some correct and reasonable initial values to be eliminated due to misjudgment of the error value. Similarly, some incorrect initial values are also easily selected due to misjudgment of the error value, which not only easily affects the calibration accuracy of the hydrological forecasting model, but also easily affects the accuracy of the subsequent prediction results of the hydrological forecasting model.

[0004] Therefore, the prior art urgently needs a technical solution for a hydrological forecasting method. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a hydrological forecasting method, which specifically includes the following steps:

[0006] Step S1: Collect hydrological data through historical data and perform preprocessing on the hydrological data to obtain the key parameters of the hydrological data;

[0007] Step S2: Set initial values for each key parameter respectively, obtain the actual measured values of the current hydrological data, and input the initial values of each key parameter and the actual measured values of the current hydrological data into the pre-constructed hydrological forecasting model to obtain the predicted values of the hydrological data;

[0008] Step S3: Analyze the initial values of each key parameter based on the predicted values of the hydrological data to obtain the second optimal initial values of each key parameter;

[0009] Step S3a: Analyze the error value between the predicted value of the hydrological data and the actual measured value of the hydrological data to obtain the upper threshold and the lower threshold of the error value;

[0010] Step S3a1: Manually adjust the initial value of each key parameter at least twice and record the predicted values of the hydrological data predicted by the hydrological forecasting model after each adjustment;

[0011] Step S3a2: Calculate the error value between the predicted value of the hydrological data predicted by the hydrological forecasting model after each adjustment and the actual measured value of the hydrological data to obtain at least two sets of error values;

[0012] Step S3a3: Set a threshold for the error value. If the current error value is greater than or equal to the threshold, it is classified as the first error value; if the current error value is less than the threshold, it is classified as the second error value;

[0013] Step S3a4: Calculate the mean of all the first error values to obtain the upper threshold; and calculate the mean of all the second error values to obtain the lower threshold;

[0014] Step S3b: Analyze the correlation between the predicted value of the hydrological data and the actual measured value of the hydrological data to obtain the correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data;

[0015] Step S3b1: Obtain at least two sets of actual measured values of the hydrological data and construct the first data set of the actual measured values of the hydrological data; obtain at least two sets of predicted values of the hydrological data and construct the second data set of the predicted values of the hydrological data;

[0016] Step S3b2: Calculate the average actual measured value of all the hydrological data in the first data set and calculate the average predicted value of all the hydrological data in the second data set;

[0017] Step S3b3: Based on the actual measured value of each hydrological data in the first data set, the average actual measured value of all the hydrological data in the first data set, the predicted value of each hydrological data in the second data set, and the average predicted value of all the hydrological data in the second data set, calculate the correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data;

[0018] Among them, the calculation formula for the correlation coefficient between the predicted value of hydrological data and the actual measured value of hydrological data is:

[0019] ;

[0020] In the formula, represents the correlation coefficient between the predicted value of hydrological data and the actual measured value of hydrological data; represents the actual measured value of the i-th hydrological data in the first dataset; represents the average actual measured value of all hydrological data in the first dataset; represents the predicted value of the i-th hydrological data in the second dataset; represents the average predicted value of all hydrological data in the second dataset; n represents the number of actual measured values of hydrological data in the first dataset or the predicted values of hydrological data in the second dataset;

[0021] Step S3c: Combine the correlation coefficient between the predicted value of hydrological data and the actual measured value of hydrological data with the upper threshold and the lower threshold respectively to obtain the standard threshold interval of the error value between the predicted value of hydrological data and the actual measured value of hydrological data;

[0022] Step S3d: Use the standard threshold interval to determine the error value between the predicted value of the current hydrological data and the actual measured value of the hydrological data. If the current error value is within the standard threshold interval, it means that the initial value of each current key parameter is the first optimal initial value, and record the initial value of each current key parameter; if the current error value is not within the standard threshold interval, manually adjust the initial value of each key parameter until the adjusted initial value of each key parameter is the first optimal initial value, and record the adjusted initial value of each key parameter;

[0023] Step S3e: Loop through Step S2 and Step S3 until at least two sets of first optimal initial values are obtained, and sort each set of first optimal initial values. Obtain the first optimal initial value with the highest sorting rank as the second optimal initial value according to the sorting result;

[0024] Step S3e1: Calculate the middle value of the standard threshold interval according to the upper limit and the lower limit of the standard threshold interval;

[0025] Step S3e2: Sort each set of first optimal initial values according to the principle that the closer the error value is to the middle value, the higher the sorting rank. Obtain the first optimal initial value with the highest sorting rank as the second optimal initial value according to the sorting result;

[0026] Step S4: Calibrate the hydrological forecasting model using the second optimal initial value of each key parameter to obtain the calibrated hydrological forecasting model, and input the hydrological data to be predicted into the calibrated hydrological forecasting model to obtain the hydrological forecasting result.

[0027] The present invention also provides a hydrological forecasting system for implementing a hydrological forecasting method, including the following modules:

[0028] Key parameter processing module: Used to collect hydrological data through historical data and perform preprocessing on the hydrological data to obtain the key parameters of the hydrological data;

[0029] Prediction module: Connected to the key parameter processing module, used to set initial values for each key parameter respectively, obtain the actual measured values of the current hydrological data, and input the initial values of each key parameter and the actual measured values of the current hydrological data into the pre-constructed hydrological forecasting model to obtain the predicted values of the hydrological data;

[0030] Key parameter initial value analysis module: Connected to the prediction module, used to analyze the initial values of each key parameter based on the predicted values of the hydrological data to obtain the second optimal initial value of each key parameter;

[0031] Hydrological forecasting result prediction module: Connected to the key parameter initial value analysis module, used to calibrate the hydrological forecasting model using the second optimal initial value of each key parameter to obtain the calibrated hydrological forecasting model, and input the hydrological data to be predicted into the calibrated hydrological forecasting model to obtain the hydrological forecasting result.

[0032] The embodiments of the present invention have the following technical effects:

[0033] The present invention aims to improve the accuracy of the prediction results of the hydrological forecasting model. By performing a data-driven analysis method on the initial values of each key parameter in the hydrological forecasting model to obtain the second optimal initial value of each key parameter, and combining the correlation coefficient with the upper and lower limits of the threshold calculated based on the error value during the obtaining process, a more strict standard threshold interval can be obtained, so that the error value can be judged more reasonably and scientifically, and the initial values of each key parameter that meet the conditions can be strictly screened out. Furthermore, the hydrological forecasting model can be calibrated more scientifically and effectively to effectively improve the accuracy of the subsequent prediction results of the hydrological forecasting model. Description of the Drawings

[0034] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 It is a flowchart of a hydrological forecasting method provided by an embodiment of the present invention. Specific embodiments

[0036] To make the purpose, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present invention.

[0037] Embodiment 1: As Figure 1 shown, the present invention provides a hydrological forecasting method, including the following steps:

[0038] Step S1: Collect hydrological data through historical data and perform preprocessing on the hydrological data to obtain key parameters of the hydrological data;

[0039] Regarding collecting hydrological data through historical data, it is mainly to obtain long-term accumulated hydrological data by referring to existing research literature and public databases, such as online resources provided by meteorological bureaus; regarding the preprocessing of hydrological data, it includes identifying and processing obvious error data points, such as extreme values or missing values outside the reasonable range, and then using statistical methods such as the 3σ principle to judge which are outliers; for the missing data that appears, interpolation methods such as linear interpolation, spline interpolation, nearest neighbor average method or physical model-based methods are used to fill it; and it is necessary to ensure that all data use the same measurement unit, such as the flow unit is cubic meters per second, and the rainfall unit is millimeters, etc.; then feature extraction is carried out. Usually, based on hydrological principles and existing research results, key parameters affecting the hydrological process are determined. For example, the evaporation rate is calculated by the energy balance method; the infiltration capacity is estimated by combining factors such as soil texture and vegetation cover; the surface roughness is determined according to topographic features and land use conditions; the soil water holding capacity is obtained through soil profile surveys or laboratory tests; and the vegetation coverage is obtained using the classification results of remote sensing images.

[0040] Step S2: Based on the key parameters of hydrological data, construct a hydrological forecasting model and set initial values for each key parameter in the hydrological forecasting model; obtain the actual measured values of the current hydrological data and input them into the hydrological forecasting model, and combine with the initial values of each key parameter in the hydrological forecasting model to obtain the predicted values of the hydrological data.

[0041] It should be noted that the method for obtaining the actual measured values of hydrological data only needs to directly obtain the latest actual measured values from hydrological stations installed in rivers, lakes, reservoirs, etc., such as flow meters, water level gauges, and rain gauges. These stations are usually equipped with automatic recording devices and upload data through wireless communication or the Internet. Therefore, the acquisition of actual measured values is relatively simple. Of course, the obtained instant actual measured values usually need to be subjected to preliminary quality checks, such as removing obvious errors or outliers.

[0042] Regarding the construction of the hydrological forecasting model, first, the most suitable hydrological model needs to be selected according to the characteristics of the study area and the forecasting objectives. Common models include, but are not limited to: distributed hydrological models and lumped hydrological models; subsequently, initial values are set for each key parameter in the hydrological forecasting model, such as evaporation rate, infiltration capacity, surface roughness, etc., according to the literature review. Subsequently, the actual measured values of the current hydrological data, such as flow rate, rainfall, temperature, etc., are used as the input conditions of the model to ensure that all the information required for the model to run is ready. Immediately afterwards, the first prediction is carried out, that is, the hydrological forecasting model is run using the set initial parameters and input conditions to obtain the predicted values of the hydrological data, such as the river flow rate in the future for a period of time.

[0043] Step S3: Analyze the initial values of each key parameter based on the predicted values of the hydrological data to obtain the second optimal initial values of each key parameter.

[0044] Step S3a: Analyze the error values between the predicted values of the hydrological data and the actual measured values of the hydrological data to obtain the upper threshold and lower threshold of the error values.

[0045] Step S3a1: Manually adjust the initial values of each key parameter at least twice and record the predicted values of the hydrological data predicted by the hydrological forecasting model after each adjustment.

[0046] Step S3a2: Calculate the error values between the predicted values of the hydrological data predicted by the hydrological forecasting model after each adjustment and the actual measured values of the hydrological data to obtain at least two sets of error values.

[0047] Step S3a3: Set a threshold for the error values. If the current error value is greater than or equal to the threshold, it is classified as the first error value; if the current error value is less than the threshold, it is classified as the second error value.

[0048] Step S3a4: Calculate the mean of all the first error values to obtain the upper threshold; and calculate the mean of all the second error values to obtain the lower threshold.

[0049] It should be noted that in the above, the error values are divided into two categories using thresholds, namely the first error values and the second error values. This can clearly distinguish which prediction results have higher errors, that is, the first error values, and which have lower errors, that is, the second error values. This helps to identify situations where the model performs poorly and provides a direction for subsequent optimization. Further, by calculating the means of the first error values and the second error values, specific upper and lower thresholds are obtained, providing a quantitative basis for model calibration. This makes the entire process more transparent, repeatable, and easy to verify. And the method of setting the upper and lower thresholds based on the mean can more effectively get rid of the interference of subjective factors compared with the previous method relying on manual experience, making the obtained upper and lower thresholds more objectively effective.

[0050] Step S3b: Analyze the correlation between the predicted value of the hydrological data and the actual measured value of the hydrological data to obtain the correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data.

[0051] Step S3b1: Obtain at least two sets of actual measured values of the hydrological data and construct a first data set of the actual measured values of the hydrological data; obtain at least two sets of predicted values of the hydrological data and construct a second data set of the predicted values of the hydrological data.

[0052] Step S3b2: Calculate the average actual measured value of all the hydrological data in the first data set and calculate the average predicted value of all the hydrological data in the second data set.

[0053] Step S3b3: Based on the actual measured value of each hydrological data in the first data set, the average actual measured value of all the hydrological data in the first data set, the predicted value of each hydrological data in the second data set, and the average predicted value of all the hydrological data in the second data set, calculate the correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data.

[0054] Among them, the calculation formula for obtaining the correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data is:

[0055] ;

[0056] In the formula, represents the correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data; represents the actual measured value of the i-th hydrological data in the first data set; represents the average actual measured value of all the hydrological data in the first data set; represents the predicted value of the i-th hydrological data in the second dataset; represents the average predicted value of all hydrological data in the second dataset; n represents the number of actual measured values of hydrological data in the first dataset or the predicted values of hydrological data in the second dataset;

[0057] Step S3c: Combine the correlation coefficients between the predicted values of hydrological data and the actual measured values of hydrological data with the upper threshold and the lower threshold respectively to obtain the standard threshold interval of the error value between the predicted values of hydrological data and the actual measured values of hydrological data;

[0058] It should be noted that by combining the correlation coefficient with the upper and lower thresholds calculated based on the error value to obtain the standard threshold interval, where the correlation coefficient reflects the strength of the linear relationship between the prediction result and the actual measured data, and the error value directly measures the degree of difference between the two. The combination of the two can comprehensively understand the performance of the model; and by setting the standard threshold interval, it is possible to more strictly screen out the parameter combinations that meet the conditions, that is, the initial values of each key parameter that meet the conditions. This not only requires the error value to be within a reasonable range, but also requires the model to maintain a good overall fitting degree, so as to ensure that the finally selected parameter combination can accurately reflect the actual situation and stably and reliably predict future hydrological conditions.

[0059] Step S3d: Use the standard threshold interval to determine the error value between the predicted value of the current hydrological data and the actual measured value of the hydrological data. If the current error value is within the standard threshold interval, it means that the initial value of each current key parameter is the first optimal initial value, and record the initial value of each current key parameter; if the current error value is not within the standard threshold interval, manually adjust the initial value of each key parameter until the adjusted initial value of each key parameter is the first optimal initial value, and record the adjusted initial value of each key parameter;

[0060] Step S3e: Loop steps S2 and S3 until at least two groups of first optimal initial values are obtained, and sort each group of first optimal initial values, and obtain the first optimal initial value with the highest sorting rank as the second optimal initial value according to the sorting result;

[0061] Step S3e1: Calculate the middle value of the standard threshold interval according to the upper limit and the lower limit of the standard threshold interval;

[0062] Step S3e2: Sort each group of first optimal initial values according to the principle that the closer the error value is to the middle value, the higher the sorting rank, and obtain the first optimal initial value with the highest sorting rank as the second optimal initial value according to the sorting result;

[0063] It should be noted that a high error value means a large difference between the model prediction result and the actual observed value, while an extremely low error value, close to zero, is likely to lead to overfitting of the model and inability to generalize to new situations. At this time, the middle value of the standard threshold interval represents a moderate position in the error value distribution, neither too high nor too low. Selecting the first optimal initial value with an error value close to the middle value can ensure that the model can exhibit good prediction ability in most cases. Especially for the second optimal initial value obtained by screening the first optimal initial value according to the principle that the closer the error value is to the middle value, the higher the ranking level, it can further ensure that the model will not make misjudgments due to extreme errors.

[0064] Step S4: Calibrate the hydrological forecasting model using the second optimal initial value of each key parameter to obtain the calibrated hydrological forecasting model, and input the hydrological data to be predicted into the calibrated hydrological forecasting model to predict the hydrological forecasting result;

[0065] It should be noted that during calibration, first, it should be confirmed that at least two sets of first optimal initial values have been obtained through multiple rounds of iteration and sorting, and the one with the highest sorting level has been selected as the second optimal initial value. At the same time, it is ensured that these second optimal initial values have been accurately recorded, such as the specific values of key parameters including evaporation rate, infiltration capacity, surface roughness, etc. Then, it is necessary to ensure that the current actual measurement values have been correctly loaded into the hydrological forecasting model, and a hydrological data set for verification and future prediction is prepared, including input conditions such as rainfall, temperature, humidity, etc. Subsequently, the second optimal initial value of each key parameter is replaced into the hydrological forecasting model. For example, the second optimal initial value of the evaporation rate is set to the newly determined initial value, the second optimal initial value of the infiltration capacity is set to the newly determined initial value, and the second optimal initial value of the surface roughness is set to the newly determined initial value. At the same time, it is also necessary to ensure that all other model settings such as time step, grid division, etc. remain unchanged to facilitate the comparison of differences before and after calibration. Immediately afterwards, the calibrated hydrological forecasting model is run for a trial, that is, the hydrological forecasting model is run using the updated second optimal initial value of each key parameter to obtain preliminary calibration results. The output of the calibrated model is compared with an independent verification data set to evaluate whether there is a significant improvement in model performance. This can be achieved by calculating a new error value and comparing it with the results before calibration. If it is found during the verification process that some key parameters still need to be further optimized, the first optimal initial value ranked second in the sorting result can be selected as the second optimal initial value according to the specific situation and the hydrological forecasting model can be run again until the model performance reaches the best state. Finally, the finally calibrated hydrological forecasting model is applied to actual prediction tasks to generate reliable hydrological forecasting results. For example, according to the time period to be predicted, the corresponding input conditions such as future rainfall, temperature, humidity, etc. are prepared and loaded into the calibrated hydrological forecasting model to ensure that all necessary information is ready. Subsequently, the model is started to begin the prediction process. The model will generate hydrological forecasting results for a future period based on the updated second optimal initial value of the key parameters and the input conditions, such as river flow, groundwater level, etc.

[0066] This embodiment also discloses a hydrological forecasting system for performing the above-mentioned hydrological forecasting method, including the following modules:

[0067] Key parameter processing module: used to collect hydrological data through historical data and perform preprocessing on the hydrological data to obtain the key parameters of the hydrological data;

[0068] Prediction module: connected to the key parameter processing module, used to set initial values for each key parameter respectively, obtain the actual measurement values of the current hydrological data, and input the initial values of each key parameter and the actual measurement values of the current hydrological data into a pre-constructed hydrological forecasting model to obtain the predicted values of the hydrological data;

[0069] Critical parameter initial value analysis module: Connected to the prediction module, it is used to analyze the initial values of each critical parameter based on the predicted values of hydrological data to obtain the second optimal initial value of each critical parameter;

[0070] Hydrological forecast result prediction module: Connected to the critical parameter initial value analysis module, it is used to calibrate the hydrological forecast model by using the second optimal initial value of each critical parameter to obtain a calibrated hydrological forecast model, and input the hydrological data to be predicted into the calibrated hydrological forecast model to predict the hydrological forecast result.

[0071] It should be noted that the terms used in the present invention are only for describing specific embodiments and do not limit the scope of the present application. As shown in the specification of the present invention, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" do not specifically refer to the singular and may also include the plural. The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method or device including the said element.

[0072] It should also be noted that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation of the present invention. Unless otherwise clearly specified and limited, terms such as "installed", "connected", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0073] Finally, it should be noted that: The above embodiments are only used to illustrate the technical solutions of the present invention and do not limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A hydrological forecasting method, characterized in that: The following steps are involved: Step S1, collecting hydrological data through historical data, and performing preprocessing on the hydrological data to obtain key parameters of the hydrological data; Step S2, setting initial values ​​for each key parameter, and obtaining actual measured values ​​of current hydrological data, inputting the initial values ​​of each key parameter and the actual measured values ​​of current hydrological data into a pre-built hydrological forecast model, and obtaining predicted values ​​of hydrological data; Step S3, analyzing the initial value of each key parameter based on the predicted value of the hydrological data to obtain the second optimal initial value of each key parameter; Step S4: calibrate the hydrological forecast model using the second optimal initial value of each key parameter to obtain a calibrated hydrological forecast model, and input the hydrological data to be predicted into the calibrated hydrological forecast model to predict the hydrological forecast result.

2. A hydrological forecasting method according to claim 1, characterized in that: The analyzing the initial value of each key parameter to obtain the second optimal initial value of each key parameter includes: Step S3a, analyzing the error value between the predicted value of the hydrological data and the actual measured value of the hydrological data to obtain an upper threshold value and a lower threshold value of the error value; Step S3b, analyzing the correlation between the predicted value of the hydrological data and the actual measured value of the hydrological data to obtain a correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data; Step S3c, combining the correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data with the upper threshold limit and the lower threshold limit, respectively, to obtain a standard threshold interval of the error value between the predicted value of the hydrological data and the actual measured value of the hydrological data; Step S3d, using the standard threshold interval to determine the error value between the predicted value of the current hydrological data and the actual measured value of the hydrological data, if the current error value is within the standard threshold interval, it means that the current initial value of each key parameter is the first optimal initial value, and the current initial value of each key parameter is recorded; if the current error value is not within the standard threshold interval, the initial value of each key parameter is manually adjusted until the adjusted initial value of each key parameter is the first optimal initial value, and the adjusted initial value of each key parameter is recorded; Step S3e, looping step S2 and step S3 until at least two groups of first optimal initial values ​​are obtained, and each group of first optimal initial values ​​is sorted, and the first optimal initial value with the highest sorting level is obtained as the second optimal initial value according to the sorting result.

3. A hydrological forecasting method according to claim 2, characterized in that: The error value between the predicted value of the hydrological data and the actual measured value of the hydrological data is analyzed to obtain an upper threshold value and a lower threshold value of the error value, including: Step S3a1, manually adjusting the initial value of each key parameter at least twice, and recording the predicted value of the hydrological data obtained by the hydrological forecast model after each adjustment; Step S3a2, calculating the error value between the predicted value of the hydrological data predicted by the hydrological forecast model after each adjustment and the actual measured value of the hydrological data, to obtain at least two sets of error values; Step S3a3: setting a threshold for the error value. If the current error value is greater than or equal to the threshold, it is classified as a first error value; if the current error value is less than the threshold, it is classified as a second error value; Step S3a4: Calculate the mean of all first error values ​​to obtain the upper threshold limit; and calculate the mean of all second error values ​​to obtain the lower threshold limit.

4. A hydrological forecasting method according to claim 2, characterized in that: The analysis of the correlation between the predicted value of the hydrological data and the actual measured value of the hydrological data to obtain the correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data includes: Step S3b1, obtaining at least two sets of actual measured values ​​of hydrological data, and constructing a first data set of actual measured values ​​of hydrological data; obtaining at least two sets of predicted values ​​of hydrological data, and constructing a second data set of predicted values ​​of hydrological data; Step S3b2, calculating the average actual measured value of all the hydrological data in the first data set, and calculating the average predicted value of all the hydrological data in the second data set; Step S3b3, based on the actual measured value of each hydrological data in the first data set, the average actual measured value of all the hydrological data in the first data set, the predicted value of each hydrological data in the second data set, and the average predicted value of all the hydrological data in the second data set, calculate the correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data.

5. A hydrological forecasting method according to claim 2, characterized in that: The step S2 and the step S3 are looped until at least two groups of first optimal initial values ​​are obtained, and each group of first optimal initial values ​​is sorted, and the first optimal initial value with the highest sorting level is obtained as the second optimal initial value according to the sorting result, including: Step S3e1, calculating the middle value of the standard threshold interval according to the upper limit and the lower limit of the standard threshold interval; Step S3e2, sort each group of first optimal initial values ​​according to the principle that the closer the error value is to the middle value, the higher the sorting level is, and obtain the first optimal initial value with the highest sorting level as the second optimal initial value according to the sorting result.

6. A hydrological forecasting method according to claim 4, characterized in that: The calculation formula for the correlation coefficient between the predicted value of the hydrological data and the actual measured value of the hydrological data is: ； In the formula, Represents the correlation coefficient between the predicted value of hydrological data and the actual measured value of hydrological data; represents the actual measured value of the i-th hydrological data in the first data set; represents the average actual measurement value of all hydrological data in the first dataset; Represents the predicted value of the i-th hydrological data in the second data set; represents the average predicted value of all hydrological data in the second dataset; n represents the number of actual measured values ​​of the hydrological data in the first dataset or the number of predicted values ​​of the hydrological data in the second dataset.

7. A hydrological forecasting system, used to execute a hydrological forecasting method according to any one of claims 1 to 6, characterized in that: Includes the following modules: Key parameter processing module: used to collect hydrological data through historical data, and perform preprocessing on the hydrological data to obtain key parameters of the hydrological data; Prediction module: connected with the key parameter processing module, used to set the initial value of each key parameter, obtain the actual measured value of the current hydrological data, input the initial value of each key parameter and the actual measured value of the current hydrological data into the pre-built hydrological forecast model, and obtain the predicted value of the hydrological data; A key parameter initial value analysis module: connected to the prediction module, used to analyze the initial value of each key parameter based on the predicted value of the hydrological data to obtain the second optimal initial value of each key parameter; Hydrological forecast result prediction module: connected to the key parameter initial value analysis module, used to calibrate the hydrological forecast model using the second optimal initial value of each key parameter to obtain the calibrated hydrological forecast model, and input the hydrological data to be predicted into the calibrated hydrological forecast model to predict the hydrological forecast results.

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