Hydrological ecological state prediction method, device, equipment and medium

Abstract

The invention provides a hydrological ecological state prediction method and device, equipment and a medium, and relates to the technical field of data analysis. The method comprises the following steps: acquiring historical hydrological ecological data, and preprocessing the historical hydrological ecological data to obtain hydrological ecological optimization data; determining hydrological characteristic data based on the hydrological ecological optimization data, and determining a hydrological ecological state corresponding to the hydrological characteristic data; constructing a dynamic Markov chain model about a time sequence based on the hydrological characteristic data and the hydrological ecological state; and taking the current hydrological ecological data and the current hydrological ecological state as the input of the dynamic Markov chain model, and predicting the hydrological ecological state at the target moment. According to the technical scheme, the efficiency and accuracy of hydrological ecological state prediction can be improved.

Description

Background Art

[0002] Hydrological and ecological changes are an intricate natural process, and their changing patterns are often affected by multiple factors, such as climate, topography, geology, soil, and human activities. Due to the intertwined influences and potential unpredictability of these factors, high-precision predictions of hydrological ecology pose certain challenges.

[0003] In related technologies, historical hydrological and ecological data are usually counted and analyzed through statistical methods to extract the laws of hydrological and ecological changes, and the laws are used to predict the future hydrological and ecological status, but the prediction efficiency is poor. Moreover, the prediction effect is highly limited by the continuity and representativeness of historical data. Due to the nonlinear characteristics of nature and the uncertainty of future environmental conditions, it is often difficult to accurately grasp the evolution trend of hydrological ecology by predicting only based on past trends, resulting in poor prediction accuracy.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the embodiments of the present disclosure is to provide a method for predicting hydrological and ecological status, a device for predicting hydrological and ecological status, an electronic device and a computer-readable storage medium, thereby at least to a certain extent overcoming the problem of insufficient prediction efficiency and accuracy in related hydrological and ecological status prediction technologies.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to a first aspect of an embodiment of the present disclosure, a method for predicting a hydrological ecological state is provided, comprising: acquiring historical hydrological ecological data, and preprocessing the historical hydrological ecological data to obtain hydrological ecological optimization data; determining hydrological characteristic data based on the hydrological ecological optimization data, and determining the hydrological ecological state corresponding to the hydrological characteristic data; constructing a dynamic Markov chain model about a time series based on the hydrological characteristic data and the hydrological ecological state; and using current hydrological ecological data as input to the dynamic Markov chain model to predict the hydrological ecological state at a target moment.

[0008] In some example embodiments of the present disclosure, based on the aforementioned scheme, the historical hydrological and ecological data are preprocessed to obtain hydrological and ecological optimization data, including: performing missing value detection and outlier detection on the historical hydrological and ecological data to determine the missing values ​​and outliers in the historical hydrological and ecological data; filling the missing values ​​with a mean interpolation method and removing the outliers to obtain hydrological and ecological preliminary screening data; standardizing and normalizing the hydrological and ecological preliminary screening data to obtain the hydrological and ecological optimization data.

[0009] In some example embodiments of the present disclosure, based on the aforementioned scheme, the determining of hydrological characteristic data based on the hydrological-ecological optimization data, and determining the hydrological-ecological state corresponding to the hydrological characteristic data include: determining the hydrological characteristic data based on the hydrological-ecological optimization data, the hydrological characteristic data including one or more of vegetation characteristic data, runoff characteristic data, soil moisture characteristic data, terrain characteristic data and climate characteristic data; determining the hydrological-ecological state corresponding to the hydrological characteristic data based on a preset state standard.

[0010] In some example embodiments of the present disclosure, based on the aforementioned scheme, the dynamic Markov chain model about the time series is constructed based on the hydrological characteristic data and the hydrological ecological state, including: based on the hydrological ecological state, constructing a state transition probability matrix about the hydrological characteristic data and the time series; constructing a dynamic Markov chain model based on the state transition probability matrix, and using the dynamic Markov chain model as the hydrological ecological prediction model.

[0011] In some example embodiments of the present disclosure, based on the aforementioned scheme, constructing a dynamic Markov chain model based on the state transition probability matrix includes: determining an initial state transition probability matrix, and optimizing the initial state transition probability matrix based on a maximizing likelihood function; constructing a dynamic Markov chain model based on the optimized state transition probability matrix, and verifying the prediction performance of the dynamic Markov chain model based on a cross-validation method.

[0012] In some example embodiments of the present disclosure, based on the aforementioned scheme, the method for predicting the hydrological ecological status also includes: when the hydrological characteristic data is a single variable, predicting the hydrological ecological status at the target time based on a combined model of an autoregressive integral moving average model and a long short-term memory network.

[0013] In some example embodiments of the present disclosure, based on the aforementioned scheme, the combined model based on the autoregressive integrated moving average model and the long short-term memory network predicts the hydrological ecological state at the target moment, including: predicting the linear part of the hydrological ecological state based on the autoregressive integrated moving average model to obtain a linear prediction result and a model residual sequence; using the model residual sequence as the input of the long short-term memory network to predict the nonlinear part of the hydrological ecological state to obtain a nonlinear prediction result; determining the hydrological ecological state at the target moment based on the sum of the linear prediction result and the nonlinear prediction result.

[0014] According to the second aspect of the embodiment of the present disclosure, a device for predicting hydrological and ecological status is provided, and the device includes: a data processing module, which can be used to obtain historical hydrological and ecological data, and pre-process the historical hydrological and ecological data to obtain hydrological and ecological optimization data; a state determination module, which can be used to determine hydrological characteristic data based on the hydrological and ecological optimization data, and determine the hydrological and ecological status corresponding to the hydrological characteristic data; a model construction module, which can be used to construct a dynamic Markov chain model about time series based on the hydrological characteristic data and the hydrological and ecological status; a state prediction module, which can be used to use the current hydrological and ecological data as the input of the dynamic Markov chain model to predict the hydrological and ecological status at the target moment.

[0015] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and the computer-readable instructions, when executed by the processor, implement the above-mentioned method for predicting the hydrological ecological status.

[0016] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for predicting the hydrological ecological status described above is implemented.

[0017] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: The method for predicting the hydrological and ecological state in the example embodiment of the present disclosure, on the one hand, can ensure data quality by preprocessing historical hydrological and ecological data, and obtain more representative hydrological and ecological optimization data; on the other hand, the dynamic Markov chain model is used to predict the hydrological and ecological state. Compared with the traditional static Markov model, the dynamic model can dynamically adjust the state transition probability according to the changes in the time series, and better capture the non-stationary characteristics of the hydrological ecology changing over time; on the other hand, by combining the optimized hydrological characteristic data with the corresponding hydrological and ecological state, the constructed dynamic Markov chain model can comprehensively consider the impact of various factors on the state of the hydrological ecosystem; on the other hand, based on the high efficiency of the dynamic Markov chain model, even when processing large amounts of data and complex state transitions, it can maintain a high computing speed, which is helpful to quickly predict the hydrological and ecological state; thereby, to a certain extent, the efficiency and accuracy of the prediction of the hydrological and ecological state are improved.

[0018] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0020] Figure 1 A schematic diagram showing an exemplary system architecture of a method for predicting a hydrological ecological state according to an embodiment of the present disclosure is shown.

[0021] Figure 2 A flowchart of a method for predicting hydrological ecological status according to some embodiments of the present disclosure is schematically shown.

[0022] Figure 3 The flowchart of predicting the hydrological ecological state at a target moment based on a combined model according to some embodiments of the present disclosure is schematically shown.

[0023] Figure 4 A block diagram of a hydrological ecological state prediction device according to some embodiments of the present disclosure is schematically shown.

[0024] Figure 5 A schematic diagram of the structure of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0025] Figure 6A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0026] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0027] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Instead, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0028] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the" and "the" used in this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0029] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete and will fully convey the concept of the example embodiments to those skilled in the art.

[0031] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0032] Furthermore, the drawings are only schematic illustrations and are not necessarily drawn to scale. The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] In the current context of ecological environmental protection and sustainable development, the hydrological ecological state has become an important indicator for measuring the quality of the natural environment. However, the mechanism of hydrological ecological change is complex, involving the interaction of multi-dimensional factors, including but not limited to climate change, topography, geological structure, soil type, and the impact of human activities. Due to the intertwined influence and potential unpredictability of these factors, high-precision prediction of hydrological ecology poses certain challenges.

[0034] In related technologies, historical hydrological and ecological data are usually counted and analyzed by statistical methods to extract the laws of hydrological and ecological changes, and the laws are used to predict the future hydrological and ecological status, but the prediction efficiency is poor. Moreover, the prediction effect is highly limited by the continuity and representativeness of historical data. Due to the nonlinear characteristics of nature and the uncertainty of future environmental conditions, it is often difficult to accurately grasp the evolution trend of hydrological ecology based on past trends, resulting in poor prediction accuracy. Some methods also predict the hydrological and ecological status by constructing physical models. However, the establishment of physical models requires detailed prior data support, including high-resolution geographic, climate and soil parameters. The collection and integration of these data is arduous and costly. Moreover, the construction and solution process of the physical model is highly complex, which is not only time-consuming and labor-intensive, but also has extremely high requirements for computing resources. The generalization ability of the model is weak and the prediction efficiency is poor.

[0035] In order to solve all or part of the technical problems in the above-mentioned related technologies, in an exemplary embodiment of the present disclosure, a method for predicting hydrological ecological status is firstly proposed. Figure 1 A schematic diagram showing an exemplary system architecture of a method for predicting a hydrological ecological state according to an embodiment of the present disclosure is shown.

[0036] like Figure 1As shown, the system architecture 100 may include one or more of the terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc. The terminal devices 101, 102, 103 may be various devices for collecting historical hydrological and ecological data, including but not limited to sensors, meteorological monitoring equipment, hydrological monitoring equipment, remote sensing satellites, drones and other electronic devices with hydrological and ecological data acquisition functions. It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0037] The prediction method of the hydrological ecological state provided in the embodiment of the present disclosure can be executed based on the above-mentioned terminal device and server. For example, historical hydrological ecological data can be collected through preset terminal devices 101, 102, and 103, and terminal devices 101, 102, and 103 can send the historical hydrological ecological data to server 105 through network 104. After receiving the historical hydrological ecological data, server 105 can first pre-process the historical hydrological ecological data to obtain hydrological ecological optimization data, and then determine the hydrological characteristic data based on the hydrological ecological optimization data, and determine the hydrological ecological state corresponding to the hydrological characteristic data, and then construct a dynamic Markov chain model about the time series based on the hydrological characteristic data and the hydrological ecological state, and finally use the current hydrological ecological data as the input of the dynamic Markov chain model to predict the hydrological ecological state at the target time to obtain the prediction result. However, it is easy for those skilled in the art to understand that the above application scenario is only used for example, and this is not limited to this in this exemplary embodiment.

[0038] Figure 2 The following schematically shows a flow chart of a method for predicting a hydrological ecological state according to some embodiments of the present disclosure. Figure 2 As shown, the method for predicting the hydrological ecological state may include the following steps: Step S210, acquiring historical hydrological and ecological data, and preprocessing the historical hydrological and ecological data to obtain hydrological and ecological optimization data; Step S220, determining hydrological characteristic data based on the hydrological ecological optimization data, and determining the hydrological ecological state corresponding to the hydrological characteristic data; Step S230, constructing a dynamic Markov chain model about the time series based on the hydrological characteristic data and the hydrological ecological state; Step S240: using the current hydrological and ecological data as the input of the dynamic Markov chain model to predict the hydrological and ecological state at the target time.

[0039] According to the method for predicting the hydrological and ecological state in the above-mentioned example embodiment, on the one hand, by preprocessing the historical hydrological and ecological data, the data quality can be ensured and more representative hydrological and ecological optimization data can be obtained; on the other hand, the dynamic Markov chain model is used to predict the hydrological and ecological state. Compared with the traditional static Markov model, the dynamic model can dynamically adjust the state transition probability according to the changes in the time series, and better capture the non-stationary characteristics of the hydrological ecology over time; on the other hand, by combining the optimized hydrological characteristic data with the corresponding hydrological and ecological state, the constructed dynamic Markov chain model can comprehensively consider the impact of various factors on the state of the hydrological ecosystem; on the other hand, based on the high efficiency of the dynamic Markov chain model, even when processing large amounts of data and complex state transitions, it can maintain a high computing speed, which is helpful to quickly predict the hydrological and ecological state; thereby, to a certain extent, the efficiency and accuracy of the prediction of the hydrological and ecological state are improved.

[0040] Next, the method for predicting the hydrological ecological state in the exemplary embodiment will be further described.

[0041] In step S210, historical hydrological and ecological data are acquired and preprocessed to obtain hydrological and ecological optimization data.

[0042] Among them, historical hydrological and ecological data can represent a series of quantitative information records on hydrological and ecological environmental changes accumulated through various monitoring methods and technologies over a period of time in the past. Preprocessing can represent the data optimization steps performed before analyzing historical hydrological and ecological data.

[0043] In some embodiments, the historical hydrological and ecological data may include other suitable data such as meteorological data, hydrological data, surface data, and human behavior data. For example, meteorological data may include temperature, precipitation, evaporation, relative humidity, wind speed and direction, etc., hydrological data may include water flow rate, water level, etc., surface data may include surface cover type (such as forest, grassland, cultivated land, water area, urbanized area, etc.), vegetation coverage, soil moisture, height, slope, slope direction, near infrared band reflectivity and red light band reflectivity, etc., and human behavior data may include water consumption, pollutant emissions, etc.

[0044] In some embodiments, meteorological data and hydrological data can be obtained through sensing equipment such as monitoring equipment of meteorological stations and hydrological stations, soil moisture meters, remote sensing satellites, and drones. Surface data can also be obtained through remote sensing satellites. Historical hydrological and ecological data can also be obtained through other channels such as publicly available data sets, meteorological websites, or hydrological websites. In addition, when using monitoring equipment or other devices to collect data, the equipment or device can be set to collect frequency and location, where the collection period can be one hour, one day, one month, or other suitable time periods.

[0045] In some embodiments, preprocessing historical hydrological and ecological data to obtain hydrological and ecological optimization data may include the following steps: performing missing value detection and outlier detection on historical hydrological and ecological data to determine missing values ​​and outliers in the historical hydrological and ecological data; using the mean interpolation method to fill in missing values ​​and remove outliers to obtain hydrological and ecological preliminary screening data; standardizing and normalizing the hydrological and ecological preliminary screening data to obtain hydrological and ecological optimization data.

[0046] Among them, missing value detection can refer to the process of identifying missing or unrecorded data in historical hydrological and ecological data. Outlier detection can refer to identifying data in historical hydrological and ecological data that are significantly different from most hydrological and ecological data, which may be caused by measurement errors, input errors or other abnormal situations. The mean interpolation method can refer to replacing all missing values ​​of a variable with the average of all non-missing observations of the variable.

[0047] Specifically, outlier detection can be performed using other appropriate methods such as the Z-score (Standard Score), the Interquartile Range Rule (IQR Rule), and density estimation.

[0048] For example, the Z-score method can be used to detect outliers. First, for each variable that needs to be detected, such as temperature, precipitation, etc., its sample mean μ and sample standard deviation σ are calculated. Then, the following formula (1) is used to perform outlier detection: z Value calculation: (1) in, represents the actual observed value of the variable, μ represents the sample mean of the variable, and σ represents the sample standard deviation of the variable.

[0049] Next, set the judgment threshold. For example, the judgment threshold may be 3, which means that when z When the absolute value of the value is greater than 3, the zThe observation corresponding to the value is marked as an outlier; finally, the outlier is removed.

[0050] In addition, the missing values ​​can be interpolated by the following formula (2): (2) in, represents the sum of all non-missing values ​​in a particular variable, n Represents the number of non-missing values.

[0051] After filling the missing values ​​in the historical hydrological and ecological data and removing the outliers, the hydrological and ecological preliminary screening data can be obtained. Then the hydrological and ecological data can be standardized and normalized to make the scales of different features consistent, adjust the distribution of the data, eliminate unnecessary deviations, and improve the training efficiency and prediction accuracy of the model in the subsequent process.

[0052] Specifically, the hydrological ecological initial screening data can be normalized by the following formula (3) to scale the data to the interval [0,1]: (3) in, represents the standardized data value, Represents the original data value, represents the maximum value in the variable. Represents the minimum value in a variable.

[0053] The hydrological ecological preliminary screening data can be normalized by the following formula (4) to transform it into a standard normal distribution so that the data has a mean of 0 and a standard deviation of 1.

[0054] (4) in, represents the normalized data value, Represents the original data value, u represents the mean value of the variable, σ Represents the standard deviation of the variable.

[0055] In step S220, the hydrological characteristic data is determined based on the hydrological ecological optimization data, and the hydrological ecological state corresponding to the hydrological characteristic data is determined.

[0056] Among them, the hydrological characteristic data can represent the characteristics that have a significant impact on the change of the hydrological ecological state according to the principles of hydrological ecology, such as vegetation characteristics, runoff characteristics, soil moisture characteristics and other suitable characteristics. The hydrological ecological state can represent the comprehensive status of the target river and its surrounding ecosystem within a specific time. Exemplarily, the hydrological ecological state may include precipitation status, river flow status, vegetation cover status, soil moisture status, water quality status, etc. Specifically, the precipitation status may include drought, normal, rainy, etc.; the river flow status may include low water level, normal water level, high water level, etc.; the vegetation cover status may include barren, medium cover and lush, etc.; the soil moisture status may include dry, suitable, moist, etc.; the water quality status may include excellent, good, poor, etc.

[0057] In some embodiments, hydrological characteristic data is determined based on hydrological ecological optimization data, and the hydrological ecological state corresponding to the hydrological characteristic data is determined, which specifically includes the following steps: determining the hydrological characteristic data based on the hydrological ecological optimization data, the hydrological characteristic data including one or more of vegetation characteristic data, runoff characteristic data, soil moisture characteristic data, terrain characteristic data and climate characteristic data; determining the hydrological ecological state corresponding to the hydrological characteristic data based on a preset state standard.

[0058] Specifically, vegetation characteristic data can be represented by the Normalized Difference Vegetation Index (NDVI), which can represent a vegetation index based on the near-infrared band reflectance and the red light band reflectance to quantify the vegetation coverage density in a specific area, so as to assess the health status and coverage of vegetation. Specifically, the Normalized Difference Vegetation Index can be calculated by the following formula (5): (5) in, is the near-infrared band reflectivity of the multispectral remote sensing image, is the red light band reflectance of the multispectral remote sensing image. For example, according to the preset vegetation state standard, the vegetation coverage state corresponding to the vegetation feature data can be determined, when NDVI When it is less than 0.1, the vegetation coverage is poor. NDVI When it is greater than 0.1 and less than 0.5, the vegetation coverage is medium. NDVI When it is greater than 0.5, the vegetation coverage is lush.

[0059] In addition, the runoff characteristic data can be obtained by calculating the amount of water flowing through a certain cross-section of the river within a certain period of time. Specifically, the runoff can be calculated by the following formula (6): (6) Wherein, Q represents runoff, in cubic meters per second; P represents precipitation, in millimeters; I represents initial loss, i.e., evaporation and vegetation interception before runoff is generated, in millimeters; Ea represents actual evapotranspiration, including evaporation and plant transpiration, in millimeters; Ei represents deep penetration, i.e., water exceeding the depth of plant roots, in millimeters; R represents the change in storage capacity, in millimeters; G represents the discharge outflow or inflow of the aquifer, with positive values ​​representing inflow and negative values ​​representing outflow, in millimeters; A represents the catchment area, in square meters. Exemplarily, for a small river, when Q is less than 20, the corresponding river flow state is low water level; when Q is greater than 20 and less than 80, the corresponding river flow state is normal water level; when Q is greater than 80, the corresponding river flow state is high water level. Of course, in other embodiments of the present disclosure, different river flow state standards can be set for different rivers.

[0060] Furthermore, soil moisture characteristic data can be represented by water content, which can be collected by hydrological monitoring sensors set on the ground to reflect the soil's ability to absorb and evaporate water. Specifically, the soil moisture content W can be calculated by the following formula (7): (7) Among them, Mw can represent the mass of water in the soil, and M can represent the dry soil mass.

[0061] After obtaining the soil moisture content, the soil moisture state corresponding to the target river area can be determined according to the preset soil moisture state standard.

[0062] In addition, terrain characteristic data can be represented by terrain data such as altitude, slope, and slope direction corresponding to the target river area, which can be obtained through remote sensing technology. Climate characteristic data can be represented by climate data such as temperature, rainfall, and evaporation, which can be obtained from weather station records or satellite remote sensing data.

[0063] Referring to 2, in step S230, a dynamic Markov chain model for time series is constructed based on the hydrological characteristic data and the hydrological ecological status.

[0064] Among them, the dynamic Markov Chain model (DMC) can represent a model that allows the probability of state transition to change over time, which is equivalent to an extension of the basic Markov chain. In the basic Markov chain, the probability of transition between states is fixed, that is, the probability of transitioning from any state to another state does not change over time. In the dynamic Markov chain, the state transition probability can be a function of time, reflecting the dynamic characteristics of the system evolving over time. In the prediction of hydrological and ecological status, precipitation, runoff, climate, topography, vegetation coverage, etc. may change over time. Using the dynamic Markov chain model of time series, the laws and trends of the evolution of hydrological and ecological status over time can be better predicted.

[0065] In an example embodiment, a dynamic Markov chain model for a time series is constructed based on hydrological characteristic data and hydrological ecological status, specifically comprising the following steps: constructing a state transition probability matrix for the hydrological characteristic data and the time series based on the hydrological ecological status; constructing a dynamic Markov chain model based on the state transition probability matrix, and using the dynamic Markov chain model as a hydrological ecological prediction model.

[0066] Among them, the state transition probability matrix can be used to represent a square matrix of transition probabilities between different states in the dynamic Markov chain. Specifically, the state transition probability matrix S of the dynamic Markov chain model can be defined, and the state transition probability matrix of the model, that is, the possible state set, can be determined based on the collected historical hydrological and ecological data and its corresponding hydrological and ecological state. For example, when there is n When there are hydrological and ecological states, the state transition probability matrix can be defined as S={S 1 , S 2 ,…S n}.

[0067] The transition probability between different hydrological and ecological states is calculated based on historical hydrological and ecological data. n The Markov chain of the hydrological ecological state can be obtained n × n The state transition probability matrix. t , from the state i Transfer to state j The probability can be used It means that it is time t function, and assume that the state transition probability is affected by environmental factors, that is, historical hydrological and ecological data E Therefore, It can be expressed as , that is, the state transition probability is E ( t )and tThe function of It can represent the probability of transitioning from state i to state j. The elements in the matrix The following conditions need to be met:

[0068]

[0069] Among them, Σ represents the summation, It means that it satisfies for all possible j.

[0070] At a specific time t and environmental factors, namely historical hydrological and ecological data E ( t ) under the condition that the model t From the status i Transfer to state j The probability of . A state transition matrix is ​​used to represent all state transition probabilities. Each row of the matrix corresponds to a state, and each column corresponds to a time point. The element in the i-th row and j-th column of the matrix is . Then the dynamic Markov chain model based on the state transition probability matrix can be expressed as:

[0071] In an example embodiment, a dynamic Markov chain model is constructed based on a state transition probability matrix, specifically comprising the following steps: determining an initial state transition probability matrix, and optimizing the initial state transition probability matrix based on a maximized likelihood function; constructing a dynamic Markov chain model based on the optimized state transition probability matrix, and verifying the prediction performance of the dynamic Markov chain model based on a cross-validation method.

[0072] Among them, the Maximum Likelihood Estimation (MLE) is a commonly used parameter estimation method, whose goal is to find a set of parameters that maximizes the probability of the observed data. Specifically in the dynamic Markov chain model, for a set of observed state sequence data, the maximum likelihood function means adjusting the parameters in the state transition probability matrix so that the probability of the state sequence calculated based on these parameters is maximized. The cross-validation method can be used to estimate the performance of the model on unknown data to avoid overfitting.

[0073] Specifically, for the dynamic Markov chain model, the parameter that needs to be initialized is the state transition probability matrix. Each element in the transition probability matrix can be initialized to equal probability or the initial value can be set according to some known prior knowledge. For example, if the transition between all states is equally likely, then each element in the state transition probability matrix can be initialized to 1 / n (n is the number of states). Furthermore, if there is some prior knowledge of the state transition probability, the initial value can be set according to the prior knowledge, and the sum of each row of the state transition probability matrix is ​​1.

[0074] For the dynamic Markov chain model, the data used are historical hydrological and ecological data and the corresponding hydrological and ecological status. Parameter learning can be performed by maximizing the likelihood function of the model, which can be ,in, represents the model parameters, X represents the observed data, L Represents the likelihood function. By solving the likelihood function, we can get a set of model parameters that maximizes the likelihood of observing the actual data under this set of parameters.

[0075] Specifically, for the data {X 1 ,X 2 ,…X T}, the likelihood function of the dynamic Markov chain model can be expressed as: , where P represents the parameters in the dynamic Markov chain model, that is, the elements in the state transition probability matrix. The goal is to find a set of parameters , so that the likelihood function L reaches the maximum.

[0076] Furthermore, the prediction performance of the dynamic Markov chain model was verified by cross-validation method, which can be done by taking the historical hydro-ecological data and its corresponding hydro-ecological state as the training set and dividing the training set into k subset, you can set k 5, 10, 15 or other suitable values. k -1 subset is used for training, and the remaining subset is used for validation; loop k times, each time changing a validation subset, we get k The performance evaluation results of the model are averaged and used as the final evaluation result of the model. For each validation, the model fit and prediction error, such as mean square error (MSE), can be calculated to obtain k Then, the average value of the mean square error can be calculated by the following formula (8), where: Represents the average of the mean squared errors.

[0077] (8) The standard deviation of the mean square error can be calculated by the following formula (9), where: It can be expressed as the standard deviation of the mean square error.

[0078] (9) The average value and standard deviation of the mean square error can be used to indicate the prediction performance and stability of the dynamic Markov chain model. The smaller the average value of the mean square error, the better the prediction effect of the model, and the smaller the standard deviation of the mean square error, the better the stability of the model.

[0079] In some embodiments, when training a dynamic Markov chain model, the number of iterations can be appropriately increased and the learning rate can be adjusted. Specifically, in the process of training the model, the model may learn slowly, resulting in the inability to achieve the expected prediction accuracy within a pre-set number of iterations. In this case, the number of iterations can be appropriately increased to allow the model to learn and adjust more times, thereby improving the learning effect of the model. The learning rate is the step size of model learning. If the learning rate is too large, the model may oscillate during the learning process, affecting the stability of the prediction; if the learning rate is too small, the model may learn too slowly. In this case, the learning rate can be adjusted appropriately, and a suitable learning rate can be selected based on the performance of the validation set.

[0080] Next reference Figure 2 In step S240, the current hydrological and ecological data is used as the input of the dynamic Markov chain model to predict the hydrological and ecological state at the target time.

[0081] Among them, the current hydrological and ecological data can represent the current meteorological data, hydrological data, surface data, human behavior data and other appropriate hydrological and ecological data of the target river area. After the current hydrological and ecological data is used as the input of the dynamic Markov chain model, the prediction time point is set at the same time. The dynamic Markov chain model will predict the probability distribution of the target river area at that time point based on the current hydrological and ecological data. According to the state with the largest probability distribution, the hydrological and ecological state at that time point can be predicted.

[0082] Furthermore, after predicting the hydrological and ecological status at a specific point in the future, the prediction results can be analyzed and interpreted. Based on the analysis results, it can be predicted what problems may exist in the future hydrological and ecological conditions, such as whether there will be water shortages, risks of flood disasters, etc., and corresponding response measures can be formulated based on these analysis results, such as preparing for flood prevention and control in advance, improving irrigation systems, etc., so as to transform the model prediction results into specific applications and provide support for decision-making.

[0083] In an example embodiment, the method for predicting the hydrological ecological status may also include the following steps: when the hydrological characteristic data is a single variable, the hydrological ecological status at the target time is predicted based on a combined model of an autoregressive integrated moving average model and a long short-term memory network.

[0084] Specifically, for the single variable hydrological characteristic data, it can be any data in the terrain characteristic data, such as altitude, slope, aspect, etc., and can also be any data in the climate characteristic data, such as temperature, rainfall, evaporation, etc. In addition, the hydrological ecological state corresponds to the hydrological characteristic data. For example, when the hydrological characteristic data is rainfall, the hydrological ecological state can be rainfall intensity; when the hydrological characteristic data is evaporation, the hydrological ecological state can be soil moisture state.

[0085] Among them, the Autoregressive Integrated Moving Average (ARIMA) model is suitable for linear time series data and can be used to capture the trend of linear changes in hydrological and ecological status and predict its linear part. The Long Short-Term Memory (LSTM) network is suitable for processing nonlinear relationships and long-term memory tasks in time series. By combining the linear prediction results of the ARIMA model with the nonlinear prediction results of the LSTM model, a comprehensive prediction of the hydrological and ecological status at the target time is obtained. The advantages of the two models can be combined, which not only takes into account the linear trend of the data, but also captures the potential nonlinear complex relationship, improving the accuracy and robustness of the prediction.

[0086] Furthermore, for multivariate hydrological characteristic data, the hydrological ecological status can be predicted through the dynamic Markov chain model, while for single variable hydrological characteristic data, the hydrological ecological status at the target time can be predicted through the combined model of autoregressive integrated moving average model and long short-term memory network. Using different models to predict different types of hydrological characteristic data can significantly improve the accuracy of the prediction and the practicality of the model.

[0087] In an example embodiment, reference Figure 3 As shown in the figure, the prediction of the hydrological ecological state at the target time based on the combined model of the autoregressive integrated moving average model and the long short-term memory network specifically includes the following steps: Step S310, predicting the linear part of the hydrological ecological state based on the autoregressive integrated moving average model to obtain a linear prediction result and a model residual sequence.

[0088] Specifically, the order of difference d, autoregression p and moving average q in the autoregressive integrated moving average model is first determined. Based on the above determined order (p, d, q), the ARIMA (p, d, q) model is constructed and trained with historical data to learn the linear trend and autocorrelation of the data. The trained ARIMA model is used to linearly predict the hydrological and ecological status at the future target moment, and the model residual sequence generated during the prediction process is recorded, which reflects the nonlinear part of the data and the fluctuations not captured by the model.

[0089] Step S320, using the model residual sequence as the input of the long short-term memory network to predict the nonlinear part of the hydrological ecological state and obtain a nonlinear prediction result.

[0090] Specifically, the LSTM network architecture is predefined, including the input layer, several LSTM hidden layers, and the output layer. The preprocessed residual sequence is divided into a training set and a validation set. The training set data is used to train the LSTM network, and the network weights are adjusted through back propagation and optimization algorithms to minimize the prediction error. At the same time, the validation set is used for verification to prevent overfitting. Then, the trained LSTM model is used to predict the residual sequence, and the prediction results of the nonlinear part of the hydrological and ecological state are obtained to capture the complex dynamics and nonlinear patterns in the residual.

[0091] Step S330, determining the hydrological ecological state at the target time based on the sum of the linear prediction result and the nonlinear prediction result. Specifically, the linear prediction result and the nonlinear prediction result can be weighted and summed, and the comprehensive value of the linear and nonlinear prediction results is used as the final prediction value of the hydrological ecological state at the target time.

[0092] The method for predicting the hydrological and ecological status in the above-mentioned example embodiment, on the one hand, can ensure data quality by preprocessing historical hydrological and ecological data, and obtain more representative hydrological and ecological optimization data; on the other hand, the dynamic Markov chain model is used to predict the hydrological and ecological status. Compared with the traditional static Markov model, the dynamic model can dynamically adjust the state transition probability according to the changes in the time series, and better capture the non-stationary characteristics of the hydrological ecology over time; on the other hand, by combining the optimized hydrological characteristic data with the corresponding hydrological and ecological status, the constructed dynamic Markov chain model can comprehensively consider the impact of various factors on the state of the hydrological ecosystem; on the other hand, based on the high efficiency of the dynamic Markov chain model, even when processing large amounts of data and complex state transitions, it can maintain a high computing speed, which is helpful to quickly predict the hydrological and ecological status; thereby, to a certain extent, the efficiency and accuracy of the prediction of the hydrological and ecological status are improved.

[0093] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0094] Next, in the embodiment of the present disclosure, a device for predicting the hydrological ecological state is also provided. Figure 4 As shown in , the hydrological ecological state prediction device 400 can be composed of a data processing module 401, a state determination module 402, a model construction module 403 and a state prediction module 404, wherein: the data processing module 401 can be used to obtain historical hydrological ecological data, and pre-process the historical hydrological ecological data to obtain hydrological ecological optimization data; the state determination module 402 can be used to determine the hydrological characteristic data based on the hydrological ecological optimization data, and determine the hydrological ecological state corresponding to the hydrological characteristic data; the model construction module 403 can be used to construct a dynamic Markov chain model about time series based on the hydrological characteristic data and the hydrological ecological state; the state prediction module 404 can be used to use the current hydrological ecological data and the current hydrological ecological state as inputs of the dynamic Markov chain model to predict the hydrological ecological state at the target time.

[0095] It should be noted that the specific details of each part of the above-mentioned hydrological ecological status prediction device have been described in detail in the implementation method of the hydrological ecological status prediction method part. The undisclosed details can be found in the implementation method content of the method part, and will not be repeated here.

[0096] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned hydrological ecological state prediction method is also provided.

[0097] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0098] Refer to the following Figure 5 hereinafter describes an electronic device 500 according to such an embodiment of the present disclosure. Figure 5 The electronic device 500 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0099] like Figure 5As shown, the electronic device 500 is in the form of a general computing device. The components of the electronic device 500 may include but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510), and a display unit 540.

[0100] The storage unit stores program codes, which can be executed by the processing unit 510, so that the processing unit 510 performs the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit 510 can perform the following steps: Figure 2 In step S210 shown in the figure, historical hydrological and ecological data are obtained, and the historical hydrological and ecological data are preprocessed to obtain hydrological and ecological optimization data; in step S220, hydrological characteristic data are determined based on the hydrological and ecological optimization data, and the hydrological and ecological state corresponding to the hydrological characteristic data is determined; in step S230, a dynamic Markov chain model about time series is constructed based on the hydrological characteristic data and the hydrological and ecological state; in step S240, the current hydrological and ecological data are used as the input of the dynamic Markov chain model to predict the hydrological and ecological state at the target time.

[0101] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 521 and / or a cache memory unit 522 , and may further include a read-only memory unit (ROM) 523 .

[0102] The storage unit 520 may also include a program / utility 524 having a set (at least one) of program modules 525, such program modules 525 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0103] Bus 530 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0104] The electronic device 500 may also communicate with one or more external devices 570 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 500, and / or communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 550. In addition, the electronic device 500 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 560. As shown, the network adapter 560 communicates with other modules of the electronic device 500 through a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0105] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0106] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0107] refer to Figure 6 As shown, a program product 600 for implementing the above-mentioned hydrological ecological state prediction method according to an embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0108] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0109] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0110] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, electromagnetic waves, etc., or any suitable combination of the foregoing.

[0111] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0112] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0113] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0114] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0115] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for predicting hydrological ecological status, It is characterized in that include: Acquiring historical hydrological and ecological data, and preprocessing the historical hydrological and ecological data to obtain hydrological and ecological optimization data; Determining hydrological characteristic data based on the hydrological ecological optimization data, and determining the hydrological ecological state corresponding to the hydrological characteristic data; Constructing a dynamic Markov chain model about time series based on the hydrological characteristic data and the hydrological ecological state; The current hydrological and ecological data are used as the input of the dynamic Markov chain model to predict the hydrological and ecological status at the target time.

2. The method for predicting hydrological ecological status according to claim 1, It is characterized in that The preprocessing of the historical hydrological and ecological data to obtain hydrological and ecological optimization data includes: Performing missing value detection and outlier detection on the historical hydrological and ecological data to determine missing values ​​and outliers in the historical hydrological and ecological data; The missing values ​​are filled by using the mean interpolation method, and the abnormal values ​​are eliminated to obtain the initial screening data of hydrological ecology; The hydrological ecological preliminary screening data is standardized and normalized to obtain the hydrological ecological optimization data.

3. The method for predicting hydrological ecological status according to claim 1, It is characterized in that The determining of the hydrological characteristic data based on the hydrological ecological optimization data, and determining the hydrological ecological state corresponding to the hydrological characteristic data, includes: Determine hydrological characteristic data based on the hydrological ecological optimization data, wherein the hydrological characteristic data includes one or more of vegetation characteristic data, runoff characteristic data, soil moisture characteristic data, terrain characteristic data and climate characteristic data; Based on a preset state standard, the hydrological ecological state corresponding to the hydrological characteristic data is determined.

4. The method for predicting hydrological ecological status according to claim 1, It is characterized in that The dynamic Markov chain model about time series is constructed based on the hydrological characteristic data and the hydrological ecological state, including: Based on the hydrological ecological state, constructing a state transition probability matrix about the hydrological characteristic data and time series; A dynamic Markov chain model is constructed based on the state transition probability matrix, and the dynamic Markov chain model is used as the hydrological ecological prediction model.

5. The method for predicting hydrological ecological status according to claim 4, It is characterized in that The constructing of a dynamic Markov chain model based on the state transition probability matrix includes: Determining an initial state transition probability matrix, and optimizing the initial state transition probability matrix based on maximizing the likelihood function; A dynamic Markov chain model is constructed based on the optimized state transition probability matrix, and the prediction performance of the dynamic Markov chain model is verified based on a cross-validation method.

6. The method for predicting hydrological ecological status according to claim 1, It is characterized in that Also includes: When the hydrological characteristic data is a single variable, the hydrological ecological state at the target time is predicted based on a combined model of an autoregressive integrated moving average model and a long short-term memory network.

7. The method for predicting hydrological ecological status according to claim 6, It is characterized in that The combined model based on the autoregressive integrated moving average model and the long short-term memory network predicts the hydrological ecological state at the target time, including: Predicting the linear part of the hydrological ecological state based on the autoregressive integrated moving average model to obtain a linear prediction result and a model residual sequence; The model residual sequence is used as the input of the long short-term memory network to predict the nonlinear part of the hydrological ecological state to obtain a nonlinear prediction result; The hydrological ecological state at the target time is determined based on the sum of the linear prediction result and the nonlinear prediction result.

8. A device for predicting hydrological and ecological conditions, It is characterized in that include: A data processing module is used to obtain historical hydrological and ecological data and pre-process the historical hydrological and ecological data to obtain hydrological and ecological optimization data; A state determination module, used to determine hydrological characteristic data based on the hydrological ecological optimization data, and determine the hydrological ecological state corresponding to the hydrological characteristic data; A model building module, used for building a dynamic Markov chain model about time series based on the hydrological characteristic data and the hydrological ecological state; The state prediction module is used to use the current hydrological and ecological data as the input of the dynamic Markov chain model to predict the hydrological and ecological state at the target moment.

9. An electronic device, It is characterized in that include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the method for predicting the hydrological ecological state according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the method for predicting the hydrological ecological state according to any one of claims 1 to 7 is implemented.

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