Hydrological forecasting method and system

Abstract

The invention discloses a hydrological forecasting method and system, and relates to the technical field of hydrological forecasting, and the method comprises the steps: carrying out the analysis of an initial value of each key parameter in a hydrological forecasting model based on data driving, so as to obtain a second optimal initial value of each key parameter; in the acquisition process, the correlation coefficient is combined with the upper limit and the lower limit of the threshold value calculated based on the error value, so that a stricter standard threshold value interval can be obtained, the error value can be judged more reasonably and scientifically, the initial value of each key parameter meeting the condition can be strictly screened out, and the accuracy of the detection result is improved. And therefore, the hydrological forecasting model can be calibrated more scientifically and effectively, and the accuracy of the prediction result of the subsequent hydrological forecasting model can be effectively improved.

Description

Technical Field

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

[0002] Hydrological forecasting is the process of predicting the changes in water volume in rivers, lakes and other water bodies. The existing hydrological forecasting process is usually as follows: first, historical hydrological data is collected through ground observation stations, meteorological stations, satellite remote sensing, etc. For example, real-time or regularly recorded 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 removing outliers, filling missing values, unifying measurement units, time alignment and other steps to ensure data quality and consistency. It also includes feature extraction to obtain key parameters for building hydrological forecasting models such as distributed hydrological models. Then, based on the 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. In the process of continuous prediction and simulation, 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, thereby ensuring the accuracy of subsequent model predictions; However, the existing process of obtaining the optimal initial value of each key parameter in the hydrological forecast model still relies on setting a threshold interval for the error value to judge the error value, and determining whether the current initial value is the optimal initial value based on the judgment result. Although this method is simple and intuitive, it often ignores the problem of the overall fit of the model, resulting in the existing method of judging the error value often lacking a certain degree of scientificity. At the same time, the existing setting of the threshold interval for the error value is usually based on expert experience for judgment and division. Therefore, the division is also made under the premise of lacking a reliable data basis, which makes the application of the current threshold interval easy to lead to misjudgment of the error value, so that some correct and reasonable initial values ​​are easily eliminated due to misjudgment of the error value. Similarly, it is also easy for some wrong initial values ​​to be wrongly selected due to misjudgment of the error value, which not only easily affects the calibration accuracy of the hydrological forecast model, but also easily affects the accuracy of the subsequent prediction results of the hydrological forecast model.

[0003] Therefore, the prior art is in urgent need of a technical solution for a hydrological forecasting method. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a hydrological forecasting method, which specifically comprises the following steps: 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 to obtain the second optimal initial value of each key parameter; 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 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, calculating the mean of all first error values ​​to obtain an upper threshold limit; and calculating the mean of all second error values ​​to obtain a lower threshold limit; 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 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; Among them, 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 data set; n represents the number of actual measured values ​​of hydrological data in the first data set or predicted values ​​of hydrological data in the second data set; 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; 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, sorting 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 obtaining the first optimal initial value with the highest sorting level as the second optimal initial value according to the sorting result; 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.

[0005] The present invention also provides a hydrological forecasting system for executing a hydrological forecasting method, comprising 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; Key parameter initial value analysis module: connected to the prediction module, used to analyze the initial value of each key parameter 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.

[0006] The embodiments of the present invention have the following technical effects: The present invention aims to improve the accuracy of the prediction results of the hydrological forecast model by performing a data-driven analysis method on the initial value of each key parameter in the hydrological forecast model to obtain the second optimal initial value of each key parameter. During the acquisition process, a more stringent standard threshold range can be obtained by combining the correlation coefficient with the upper and lower limits of the threshold calculated based on the error value, so that the error value can be judged more reasonably and scientifically, so as to strictly screen out the initial value of each key parameter that meets the conditions, and then the hydrological forecast model can be calibrated more scientifically and effectively, so as to effectively improve the accuracy of the subsequent prediction results of the hydrological forecast model. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0008] Figure 1 It is a flow chart of a hydrological forecasting method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0010] Embodiment 1: Figure 1 As shown, the present invention provides a hydrological forecasting method, comprising the following steps: Step S1, collecting hydrological data through historical data, and performing preprocessing on the hydrological data to obtain key parameters of the hydrological data; Regarding the collection of hydrological data through historical data, it is mainly through consulting existing research literature and public databases, such as online resources provided by the Meteorological Bureau, to obtain long-term accumulated hydrological data; regarding the preprocessing of hydrological data, including identifying and processing obviously erroneous data points, such as extreme values ​​or missing values ​​that exceed the reasonable range, statistical methods such as the 3σ principle are used to determine which are outliers; for missing data, interpolation methods such as linear interpolation, spline interpolation, neighbor point averaging or methods based on physical models are used to fill them; and it is necessary to ensure that all data use the same measurement units, such as flow units in cubic meters per second, rainfall units in millimeters, etc.; then feature extraction is performed, usually based on hydrological principles and existing research results, to determine the key parameters affecting the hydrological process, for example: evaporation rate is calculated by the energy balance method; infiltration capacity is estimated by combining factors such as soil texture and vegetation cover; surface roughness is determined based on terrain characteristics and land use conditions; soil water holding capacity is obtained through soil profile surveys or laboratory tests; vegetation cover is obtained using remote sensing image classification results.

[0011] Step S2: construct a hydrological forecast model based on the key parameters of the hydrological data, and set initial values ​​for each key parameter in the hydrological forecast model; obtain the actual measured value of the current hydrological data, and input it into the hydrological forecast model, and obtain the predicted value of the hydrological data in combination with the initial value of each key parameter in the hydrological forecast model; It is worth noting that the actual measurement values ​​of hydrological data can be obtained by directly obtaining the latest actual measurement values ​​from hydrological stations such as flow meters, water level meters, and rain gauges installed in rivers, lakes, reservoirs, etc. These stations are usually equipped with automatic recording equipment and upload data through wireless communication or the Internet. Therefore, it is relatively simple to obtain the actual measurement values. Of course, the actual measurement values ​​obtained in real time usually need to be subjected to preliminary quality checks, such as removing obvious errors or outliers. Regarding the construction of the hydrological forecast model, first of all, it is necessary to select the most appropriate hydrological model according to the characteristics of the study area and the forecast objectives. Common models include but are not limited to: distributed hydrological models and centralized hydrological models; then, according to the literature review, set initial values ​​for each key parameter in the hydrological forecast model, such as evaporation rate, infiltration capacity, surface roughness, etc., and then use the actual measured values ​​of the current hydrological data such as flow, rainfall, temperature, etc. as the input conditions of the model to ensure that all the information required for the model operation is ready, and then make the first prediction, that is, use the set initial parameters and input conditions to run the hydrological forecast model to obtain the predicted values ​​of the hydrological data, such as river flow in the future.

[0012] Step S3, analyzing the initial value of each key parameter to obtain the second optimal initial value of each key parameter; 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 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, calculating the mean of all first error values ​​to obtain an upper threshold limit; and calculating the mean of all second error values ​​to obtain a lower threshold limit; It is worth noting that the error values ​​are divided into two categories using thresholds, namely the first error value and the second error value. This can clearly distinguish which prediction results have higher errors, namely the first error value, and which have lower errors, namely the second error value. This helps to identify situations where the model performs poorly and provides direction for subsequent optimization. Furthermore, by calculating the mean of the first error value and the second error value, the specific upper and lower threshold limits are obtained, which provides a quantitative basis for model calibration, making the entire process more transparent, repeatable, and easy to verify. Moreover, the upper and lower threshold limits set based on the mean can more effectively get rid of the interference of subjective factors than the previous method of relying on manual experience, so that the obtained upper and lower threshold limits can be more objective and effective.

[0013] 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 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; Among them, 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 data set; n represents the number of actual measured values ​​of hydrological data in the first data set or predicted values ​​of hydrological data in the second data set; 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; It is worth noting that the standard threshold interval is obtained by combining the correlation coefficient with the upper and lower limits of the threshold calculated based on the error value, where the correlation coefficient reflects the strength of the linear relationship between the predicted results and the actual measured data, while the error value directly measures the degree of difference between the two. The combination of the two can provide a more comprehensive understanding of the performance of the model; and by setting the standard threshold interval, it is possible to more strictly screen out qualified parameter combinations, that is, the initial value of each key parameter that meets the conditions. This requires not only that the error value is within a reasonable range, but also that the model can maintain a good overall fit, thereby ensuring that the final selected parameter combination can both accurately reflect the actual situation and stably and reliably predict future hydrological conditions.

[0014] 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; 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, sorting 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 obtaining the first optimal initial value with the highest sorting level as the second optimal initial value according to the sorting result; It is worth noting that a high error value means that the model prediction result is greatly different from the actual observation value, while an extremely low error value, close to zero, will easily 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, which is 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 show good predictive ability in most cases, especially the second optimal initial value obtained after 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, which can further ensure that the model will not make misjudgments due to extreme errors.

[0015] Step S4, calibrating the hydrological forecast model using the second optimal initial value of each key parameter to obtain a calibrated hydrological forecast model, and inputting the hydrological data to be predicted into the calibrated hydrological forecast model to predict and obtain a hydrological forecast result; It is worth noting that during the calibration, first of all, it should be confirmed that at least two sets of first optimal initial values ​​have been obtained through multiple rounds of iterations and sorting, and the ones with the highest sorting level are selected as the second optimal initial values. 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 such as evaporation rate, infiltration capacity, and surface roughness. Then, it is necessary to ensure that the current actual measured values ​​have been correctly loaded into the hydrological forecast model, and at the same time, prepare the hydrological data set for verification and future prediction, including input conditions such as rainfall, temperature, and humidity. Then, the second optimal initial value of each key parameter is replaced into the hydrological forecast model, for example: the second optimal initial value of evaporation rate is set to the newly determined initial value, the second optimal initial value of infiltration capacity is set to the newly determined initial value, and the second optimal initial value of surface roughness is set to the newly determined initial value. At the same time, it is necessary to ensure that all other model settings such as time step, grid division, etc. remain unchanged to facilitate comparison of the differences before and after calibration; then, the calibrated hydrological forecast model is tested, that is, each key parameter is updated. The hydrological forecast model is run with the second optimal initial value to obtain preliminary calibration results. The calibrated model output is compared with the independent verification data set to evaluate whether the model performance has been significantly improved. This can be done by calculating new error values ​​and comparing them 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 with the second ranking result can be selected as the second optimal initial value according to the specific situation and the hydrological forecast model can be run again until the model performance reaches the optimal state; finally, the final calibrated hydrological forecast model is applied to the actual prediction task to generate reliable hydrological forecast 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 these input conditions are loaded into the calibrated hydrological forecast model to ensure that all necessary information is ready. Then the model is started and the prediction process begins. The model will generate hydrological forecast results for a period of time in the future based on the second optimal initial values ​​and input conditions of the updated key parameters, such as river flow, groundwater level, etc.

[0016] This embodiment also discloses a hydrological forecasting system, which is used to execute the above hydrological forecasting method, and 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; Key parameter initial value analysis module: connected to the prediction module, used to analyze the initial value of each key parameter 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.

[0017] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0018] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are 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 therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. 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 a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the 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 circumstances.

[0019] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions 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 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 1, 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; Key parameter initial value analysis module: connected to the prediction module, used to analyze the initial value of each key parameter 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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