CN115829320A - Water resource shortage risk prediction method and device and electronic equipment - Google Patents
Water resource shortage risk prediction method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides a method and a device for predicting water resource shortage risk and electronic equipment, and relates to the technical field of water conservancy. The method comprises the following steps: acquiring a historical runoff sequence of a target watershed; determining a threshold runoff quantity according to the historical runoff sequence; fitting the historical runoff sequence by using a generalized additive GALSS model to determine a probability distribution function; determining a first calculation result from the probability distribution function, the first calculation result representing a predicted value from a calculation year to a year in which the runoff volume is first below the threshold runoff volume. The embodiment of the invention can predict the year predicted value of water resource shortage, thereby improving the evaluation effect on the risk of water resource shortage.
Description
Technical Field
The invention relates to the technical field of water conservancy, in particular to a method and a device for predicting water resource shortage risk and electronic equipment.
Background
In recent years, water resources have exhibited a significant decline trend, in which human activities such as social water use increase and underlying surface change are major causes affecting river runoff reduction. For the drainage basin with the lower cushion surface changed greatly and the hydrologic sequence consistency lost, how to calculate and evaluate the drainage basin water resource shortage risk under the dynamic situation has an important supporting function for the future drainage basin water resource safety guarantee.
In the prior art, firstly, trend analysis and variation diagnosis can be performed on a runoff sequence by adopting a Manner-Kendall (M-K) mutation inspection method to reveal a runoff evolution rule of a runoff, secondly, runoff evolution cause analysis can be performed on the basis of methods such as runoff hydrothermal coupling balance and hydrological model simulation, thirdly, non-uniformity hydrological frequency analysis and engineering hydrological design value calculation can be performed on the basis of a variable parameter probability distribution model, and many researches introduce covariates (such as time or rainfall) to be combined with a regression model (such as a GALSS model) to depict non-uniformity of the hydrological sequence. Most techniques only aim at numerical calculation and analysis of flood and runoff, and lack calculation of water resource shortage risk.
Therefore, the problem that the evaluation effect on the risk of water resource shortage is poor exists in the prior art.
Disclosure of Invention
The embodiment of the invention provides a method and a device for predicting water resource shortage risk, electronic equipment and a storage medium, and aims to solve the problem that in the prior art, the evaluation effect on the water resource shortage risk is poor.
According to a first aspect of the present invention, there is provided a water resource shortage risk prediction method, including:
acquiring a historical runoff sequence of a target watershed;
determining a threshold runoff quantity according to the historical runoff sequence;
fitting the historical runoff sequence by using a generalized additive GALSS model to determine a probability distribution function;
determining a first calculation result from the probability distribution function, the first calculation result representing a predicted value from a calculation year to a year in which the runoff volume is first below the threshold runoff volume.
According to a second aspect of the present invention, there is provided a water resource shortage risk prediction apparatus, comprising:
the acquisition module is used for acquiring a historical runoff sequence of the target watershed;
the first determining module is used for determining a threshold runoff quantity according to the historical runoff sequence;
the second determination module is used for determining a probability distribution function by applying the fitting of the generalized additive GALSS model to the historical runoff sequence;
a third determining module, configured to determine a first calculation result according to the probability distribution function, where the first calculation result represents a predicted value from a calculation year to a year in which the runoff volume is first lower than the threshold runoff volume.
According to a third aspect of the present invention, there is provided an electronic apparatus comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the water resource shortage risk prediction method provided by the present invention.
According to a fourth aspect of the present invention, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the method for predicting a risk of water shortage provided by the present invention.
In the embodiment of the invention, the historical runoff sequence of the target basin is firstly obtained, the threshold runoff corresponding to the target basin is determined according to the historical runoff sequence, then the model with the best fitting degree and the corresponding probability distribution function are obtained by fitting the historical runoff sequence corresponding to the generalized additive GALSS model, and finally the number of years from the calculation year to the time when the runoff is first lower than the threshold runoff is predicted by using the probability distribution function, so that the risk of basin water resource shortage caused by extreme dry water in the specified time period is quantitatively evaluated, and the evaluation effect of the water resource shortage risk is improved.
It should be understood that the statements in this section do not necessarily represent key or critical features of any embodiment of the present invention, nor do they necessarily limit the scope of the present invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a schematic flow chart of a method for predicting a risk of water shortage according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a water resource shortage risk prediction apparatus according to an embodiment of the present invention;
fig. 3 is a second schematic structural diagram of a water resource shortage risk prediction device according to an embodiment of the present invention;
fig. 4 is a block diagram of an electronic device for implementing a water resource shortage risk prediction method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The terms "first," "second," and the like in the embodiments of the present invention are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for predicting a risk of water resource shortage according to an embodiment of the present invention, as shown in fig. 1, including the following steps:
and step S101, obtaining a historical runoff sequence of the target watershed.
The historical runoff sequence can be determined according to the selection of a target basin by a user, and the historical runoff sequence can comprise a plurality of groups of data.
And, in each runoff sequence, runoff total amount data corresponding to the target runoff domain can be included.
And S102, determining a threshold runoff quantity according to the historical runoff sequence.
The step can be understood as that the accumulated frequency corresponding to each average flow is calculated by using the historical runoff sequence, the standard normalization is carried out to obtain the normalized runoff index corresponding to each average flow, and then the threshold runoff is determined according to the normalized runoff index.
In addition, the threshold runoff volume can be divided into runoff volumes of different levels according to the situation of water resource shortage, and in this case, the threshold runoff volume at different water resource shortage levels needs to be determined.
And step S103, fitting the historical runoff sequence by using a generalized additive GALSS model, and determining a probability distribution function.
In the above steps, the probability distribution corresponding to the runoff sequence may be determined first, so as to obtain a probability density function of the runoff sequence, where the runoff probability density function represents possible runoff values of the runoff of the river at different time periods, and since the runoff of the river is influenced by the ecological environment around the river, the runoff probability density function is set to include a time-varying variable that represents a change of the position of the river, the scale of the river, and the shape of the river over time, so as to establish a change relationship between the runoff and time.
It should be noted that the runoff volume probability density function is a density function of a non-stationary probability distribution, and may be a normal distribution, a lognormal distribution, a gunn-bell distribution, a gamma distribution, or a weibull distribution.
Wherein each probability distribution in the probability distribution set comprises a position parameter and a scale parameter.
The probability distribution function of the runoff volume represents the distribution situation of the runoff volume of the river in different time periods, and since the runoff volume of the river is influenced by the ecological environment around the river, the runoff volume distribution function is also set to include a time-varying variable which represents the situation that the position of the river, the scale of the river and the shape of the river change with time, so that the distribution of the runoff volume is in a changing relation with time.
GALSS model is used to obtain the GAIC value of the red pond information criterion corresponding to each probability distribution type, the probability distribution type with the minimum GAIC value and the parameter are selected to be set as the optimal model corresponding to the runoff series, and the probability distribution function is set through the determined optimal model.
And step S104, determining a first calculation result according to the probability distribution function, wherein the first calculation result represents a predicted value of the number of years from the calculation year to the time when the runoff volume is firstly lower than the threshold runoff volume.
In this step, the probability of occurrence of a runoff volume being lower than the threshold runoff volume year after year from the beginning of the year may be calculated based on the probability distribution function, and the probability of occurrence of a runoff volume being lower than the threshold runoff volume year from the beginning of the year to the first may be determined based on the first probability value, and the expected value of the number of years from the beginning of the year to the first occurrence of a runoff volume being lower than the threshold runoff volume may be determined based on the second probability value, that is, the first calculation result may be interpreted as a predicted value of the number of years from the beginning of the calculation year to the first occurrence of a runoff volume being lower than the threshold runoff volume.
It should be noted that the steps S101 to S104 may be executed by an electronic device, for example: laptop computers, desktop computers, workstations, personal digital assistants, servers, mainframe computers, other suitable computers, and the like, to which embodiments of the present invention are not limited.
In the embodiment of the invention, the historical runoff sequence of the target basin is firstly obtained, the threshold runoff quantity corresponding to the target basin is determined according to the historical runoff sequence, then the generalized additive GALSS model is used for fitting corresponding to the historical runoff sequence to obtain a model with the best fitting degree and a corresponding probability distribution function, and finally the probability distribution function is used for predicting the number of years from the year of calculation to the time when the runoff quantity is lower than the threshold runoff quantity for the first time, so that the risk of water resource shortage of the basin due to extreme dry water in a specified time period is quantitatively evaluated, and the evaluation effect on the risk of water resource shortage is improved.
As an optional implementation, the threshold runoff amount includes at least one of: medium drought threshold runoff, heavy drought threshold runoff, and extra drought threshold runoff.
In the embodiment of the invention, the threshold runoff can be divided into the intermediate drought threshold runoff, the heavy drought threshold runoff and the extra drought threshold runoff, and the first calculation result can comprise the predicted values corresponding to the three low water grades through dividing the low water conditions of different grades, so that the prediction effect of the risk of water resource shortage is improved, and the predicted values can be more accurate according to the severe conditions of actual low water.
As an optional implementation, the determining a threshold runoff amount according to the historical runoff sequence includes:
determining scale parameters and shape parameters of gamma distribution by using a maximum likelihood estimation method based on the historical runoff sequence, wherein the historical runoff sequence obeys the gamma distribution;
acquiring a standardized runoff index, and determining a first parameter according to the standardized runoff index;
determining the threshold runoff amount as a function of the first parameter and an inverse function of a Γ -distribution.
In the embodiment of the present invention, the Γ distribution is solved by using a maximum likelihood estimation method, so as to determine the scale parameter and the shape parameter corresponding to the Γ distribution, it should be understood that the history runoff sequence needs to obey the Γ distribution, and then the normalized runoff index is obtained, where the normalized runoff index may be determined according to a dry water condition, for example: when the threshold runoff rate comprises a moderate drought threshold runoff rate, a severe drought threshold runoff rate and an extreme drought threshold runoff rate, the normalized runoff index can be selected from-2.0, -1.5 and-1.0, then the first parameter is obtained by solving according to the normalized runoff index and other parameters, and finally the threshold runoff rate is obtained by solving according to the first parameter and an inverse function of gamma distribution.
However, since the inverse function corresponding to the Γ distribution also includes the scale parameter and the shape parameter, the scale parameter and the shape parameter determined by the maximum likelihood estimation method need to be substituted and solved.
As an alternative embodiment, the first parameter is calculated by the following formula:
wherein Z represents the normalized runoff index, c 0 、c 1 、c 2 、d 1 、d 2 And d 3 Respectively representing sub-parameters in the parameter group, and t represents a first parameter;
calculating the threshold runoff amount by the following formula:
wherein F represents the second parameter, e represents a natural constant, t represents a first parameter, and ΓInverse function of gamma- 1 (F | β, γ), β representing the scale parameter and γ representing the shape parameter.
In the embodiment of the present invention, Z is the above-mentioned normalized runoff index, and in the case that the dry water grades are divided into medium drought, heavy drought, and extra drought, Z may also select three different values to correspond to three dry water grades, for example: z can be selected from-2.0, -1.5 and-1.0, and in addition, c 0 、c 1 、c 2 、d 1 、d 2 And d 3 Respectively representing sub-parameters in a parameter set, in some alternative embodiments c 0 ＝2.515517，c 1 ＝0.802853，c 2 ＝0.010328，d 1 ＝1.432788,d 2 ＝0.189269,d 3 =0.001308, solving for the first parameter t.
After the first parameter t is obtained, the second parameter F can be solved directly because the inverse function of F is F -1 (F | beta, gamma), so the threshold runoff Q can be obtained by solving according to the second parameter F, the scale parameter beta and the shape parameter gamma, and the threshold runoff Q of the medium drought is obtained under the condition that the dry water grades are divided into medium drought, heavy drought and extra drought In Threshold runoff of drought and drought Q Heavy load Specific drought threshold runoff Q Specially for treating diabetes 。
As an alternative embodiment, the determining the probability distribution function through the fitting of the GALSS model to the historical runoff sequence includes:
obtaining a set of probability distributions, the set of probability distributions comprising: normal distribution, lognormal distribution, gunbel distribution, gamma distribution, and weibull distribution;
determining a probability density function according to the historical runoff sequence and the probability distribution set, wherein the probability density function passes through f (x) t |θ j (t)), x and θ represent constant variables, and time t is a covariate;
determining a gibberellin information criterion GAIC value according to the type of the probability distribution set;
GAIC values were calculated by the following formula:
wherein, # denotes a penalty factor, df denotes an overall degree of freedom in the GAMLSS model,
representing a log-likelihood function;
taking the probability distribution type with the minimum GAIC value as a target probability distribution type, and determining a probability distribution function
x and
representing a constant variable, and time t is a covariate.
In the embodiment of the present invention, a plurality of probability distributions including a position parameter and a scale parameter are obtained, and a probability distribution set is established, for example: by M j (j =1,2, \ 8230;, S) represents S different candidate probability distributions, M for one candidate probability distribution j The probability density function of the historical runoff sequence can be expressed as f (x) t |θ j (t)), the historical runoff series may also be denoted as Q i (i =1,2, \8230;, n), wherein two parameters in the probability density function can be set to two types, a constant variable and a time variable with time t as a covariate, and time t as a covariate includes a first power and a second power of time t. And finally, selecting a probability distribution type and a parameter with the minimum GAIC value as an optimal model by comparing the sizes of GAIC values corresponding to different probability distribution types and different parameter settings. According to the embodiment of the invention, a most suitable probability distribution type can be selected from a plurality of probability distributions, and a probability distribution function matched with the most suitable probability distribution type is obtained, so that the calculation precision is improved, a foundation is laid for the prediction of the water resource shortage risk in the subsequent embodiment, and the prediction and evaluation effects are further improved.
It should be noted that the probability density function of the optimal model can be used
RepresentThe probability distribution function can be used
And (4) showing.
As an optional implementation manner, the determining the first calculation result according to the probability distribution function includes:
determining first information according to the probability distribution function, wherein the first information represents the probability that the annual runoff volume is lower than the threshold runoff volume after the calculation year;
determining second information according to the first information, wherein the second information represents the probability that the first runoff volume is lower than the threshold runoff volume from the beginning of the calculation year to the h year;
and determining the first calculation result according to the second information.
In the embodiment of the present invention, the probability of occurrence of a runoff volume that is lower than the threshold runoff volume year after year start is calculated by using a determined probability distribution function, that is, the first information is determined, then the probability of occurrence of a runoff volume that is lower than the threshold runoff volume for the year after year start is solved by using the first information, that is, the second information is determined, and after the second information is obtained, the predicted value of occurrence of a dry event that indicates an expected value of the number of years from year start to year when the dry event occurs for the first time is determined based on the probability of occurrence of the dry event that is lower than the threshold runoff volume from year start. Through the embodiment of the invention, the expected value of the number of years from the beginning of the calculation of the years to the first occurrence of the dry water event can be obtained, so that the evaluation effect on the risk of water resource shortage is improved.
After obtaining the first calculation result, the user may determine the time of the occurrence of the dry water event according to the number of years indicated by the first calculation result and the calculated initial year, for example: the initial year of the calculation was 1990, and the predicted value represented by the above first calculation result was 8, the time of occurrence of the dry water event was finally determined to be 1998 by the embodiment of the present invention.
As an alternative implementation, the first information is calculated by the following formula:
wherein p is t, disaster Representing first information, t representing time, Q Disaster recovery A threshold amount of runoff is indicated and,
representing a constant variable;
calculating the second information by the following formula:
wherein f is Disaster recovery (h) Representing second information, h representing a year in which the calculation is stopped, and P representing a probability that the annual runoff volume is lower than the threshold runoff volume one year after the calculation year starts;
calculating the first calculation result by the following formula:
where Eh denotes the first calculation result, h denotes the year in which the calculation was stopped, f Disaster recovery (h) Indicating the probability that the first runoff is below the threshold runoff from the beginning of the year of calculation to the h-th year.
It should be noted that if there is a certain time T, p is satisfied Disaster of T If not less than 0, then z max = T, otherwise, then z max ＝∞。
In the embodiment of the present invention, when the threshold runoff volume includes a moderate drought threshold runoff volume, a heavy drought threshold runoff volume, and a special drought threshold runoff volume, the first information also includes annual occurrence probabilities corresponding to the three cases, which can be used
And (4) showing.
Also, the same applies toIn the case that the threshold runoff volume includes a moderate drought threshold runoff volume, a heavy drought threshold runoff volume and a special drought threshold runoff volume, the second information also includes first occurrence probabilities corresponding to the three cases, and the second information may be f k (h) Wherein k represents the rating of dry water, including moderate drought, severe drought, and extreme drought.
Of course, when the threshold runoff volume includes a moderate drought threshold runoff volume, a heavy drought threshold runoff volume, and an extreme drought threshold runoff volume, the threshold runoff volume may also be applied to the first calculation result, for example: the first calculation result indicates an expected value of the number of years in which a certain level of dry water event first occurs from the initial year.
As an optional implementation, after the determining the first calculation result according to the probability distribution function, the method further includes:
determining a second calculation result according to the second information, wherein the second calculation result represents the probability that the bore flow is lower than the threshold bore flow in a preset time period.
In the embodiment of the present invention, the second calculation result may represent a probability that the bore flow is lower than the threshold bore flow in a preset time period, that is, it may be understood that there is a risk of water resource shortage in the preset time period, the second information represents a probability that the first bore flow is lower than the threshold bore flow from the beginning of the calculation year to the h year, and then the second calculation result may represent a sum of probabilities of occurrence of dry water every year from the beginning of the calculation year to the n year, where h is less than or equal to n. By the embodiment of the invention, the risk of water resource shortage can be evaluated and predicted from another angle, and the evaluation effect is further improved.
It should be noted that, the user may simultaneously refer to the first calculation result and the second calculation result to evaluate the risk of water resource shortage, the first calculation result may assist the user to determine a specific year, the second calculation result may indicate the probability of occurrence of the dry water event within a preset time period, and it may also be understood as determining an average time interval from the initial year to the next dry water year of a specific grade under a changing environment, so as to quantitatively evaluate the risk of river channel water resource shortage caused by extreme dry water within a specified time period.
As an alternative embodiment, the second calculation result is calculated by the following formula:
wherein R represents a second calculation result, n represents the number of years included in the preset time period, and f Disaster recovery (h) Representing the probability that the first runoff is below the threshold runoff from the beginning of the year of calculation to the h-th year, p t, disaster A probability that the annual runoff volume is below the threshold runoff volume after the beginning of the year of the calculation.
When the threshold runoff rate includes a moderate drought threshold runoff rate, a heavy drought threshold runoff rate, and a special drought threshold runoff rate, the second calculation result may be calculated by the following formula:
where k represents the level of risk of water shortage, such as moderate drought, severe drought, and extreme drought.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a water resource shortage risk prediction apparatus according to an embodiment of the present invention, and as shown in fig. 2, the water resource shortage risk prediction apparatus 200 includes:
an obtaining module 201, configured to obtain a historical runoff sequence of a target drainage basin;
a first determining module 202, configured to determine a threshold runoff amount according to the historical runoff sequence;
the second determining module 203 is configured to determine a probability distribution function by applying a fit of a generalized additive GAMLSS model to the historical runoff sequence;
a third determining module 204, configured to determine a first calculation result according to the probability distribution function, where the first calculation result represents a predicted value from a calculation year to a year when the runoff volume is first lower than the threshold runoff volume.
As an optional implementation, the threshold runoff amount includes at least one of: medium drought threshold runoff, heavy drought threshold runoff, and extra drought threshold runoff.
Optionally, the first determining module includes:
the first determining unit is used for determining scale parameters and shape parameters of gamma distribution by using a maximum likelihood estimation method based on the historical runoff sequence, wherein the historical runoff sequence obeys the gamma distribution;
the second determining unit is used for acquiring a standardized runoff index and determining a first parameter according to the standardized runoff index;
a third determining unit for determining the threshold runoff amount according to the first parameter and an inverse function of the Γ -distribution.
As an alternative embodiment, the first parameter is calculated by the following formula:
wherein Z represents the standard runoff index, c 0 、c 1 、c 2 、d 1 、d 2 And d 3 Respectively representing sub-parameters in the parameter group, and t represents a first parameter;
calculating the threshold runoff amount by the following formula:
wherein F represents the second parameter, e represents a natural constant, t represents the first parameter, and the inverse function of Γ is Γ - 1 (F | β, γ), β representing the scale parameter and γ representing the shape parameter.
As an optional implementation, the second determining module includes:
a first obtaining unit configured to obtain a probability distribution set, the probability distribution set including: normal distribution, lognormal distribution, gunn bell distribution, gamma distribution, and weibull distribution;
a fourth determining unit, configured to determine a probability density function according to the historical runoff sequence and the probability distribution set, wherein the probability density function passes through f (x) t |θ j (t)), x and θ represent constant variables, and time t is a covariate;
a fifth determining unit, configured to determine a gibberellin information criterion GAIC value according to the type of the probability distribution set;
GAIC values were calculated by the following formula:
wherein, # denotes a penalty factor, df denotes an overall degree of freedom in the GAMLSS model,
representing a log-likelihood function;
a sixth determining unit for determining a probability distribution function by using the probability distribution type with the minimum GAIC value as the target probability distribution type
x and
representing a constant variable, and time t is a covariate.
As an optional implementation, the third determining module includes:
a seventh determining unit configured to determine first information indicating a probability that a yearly runoff volume is lower than the threshold runoff volume after a calculation year in accordance with the probability distribution function;
an eighth determining unit configured to determine, based on the first information, second information indicating a probability that a first runoff volume is lower than the threshold runoff volume from a beginning of a calculation year to an h-th year;
a ninth determining unit that determines the first calculation result according to the second information.
As an alternative implementation, the first information is calculated by the following formula:
wherein p is t, disaster Representing first information, t representing time, Q Disaster recovery A threshold amount of runoff is indicated and,
representing a constant variable;
calculating the second information by the following formula:
wherein f is Disaster recovery (h) Representing second information, h representing a year in which the calculation is stopped, and P representing a probability that the annual runoff volume is lower than the threshold runoff volume one year after the calculation year starts;
calculating the first calculation result by the following formula:
where Eh denotes the first calculation result, h denotes the year in which the calculation was stopped, f Disaster recovery (h) Indicating the probability that the first runoff is below the threshold runoff from the beginning of the year of calculation to the h-th year.
As an alternative embodiment, referring to fig. 3, the water resource shortage risk prediction apparatus 200 further includes:
a fourth determining module 205, configured to determine a second calculation result according to the second information, where the second calculation result represents a probability that the bore flow is lower than the threshold bore flow in a preset time period.
As an alternative embodiment, the second calculation result is calculated by the following formula:
wherein R represents a second calculation result, n represents the number of years included in the preset time period, and f Disaster recovery (h) Representing the probability that the first runoff is below the threshold runoff from the beginning of the year of calculation to the h-th year, p t, disaster A probability that the annual runoff volume is below the threshold runoff volume after the beginning of the year of the calculation.
The invention also provides an electronic device and a readable storage medium according to the embodiment of the invention.
FIG. 4 shows a schematic block diagram of an example electronic device 400 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the apparatus 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present invention may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A water resource shortage risk prediction method is characterized by comprising the following steps:
acquiring a historical runoff sequence of a target watershed;
determining a threshold runoff quantity according to the historical runoff sequence;
fitting the historical runoff sequence by using a generalized additive GALSS model to determine a probability distribution function;
determining a first calculation result from the probability distribution function, the first calculation result representing a predicted value from a calculation year to a year in which the runoff volume is first below the threshold runoff volume.
2. The method of predicting risk of water shortage according to claim 1, wherein the threshold runoff amount comprises at least one of: medium drought threshold runoff, heavy drought threshold runoff, and extra drought threshold runoff.
3. The method for predicting the risk of water shortage according to claim 1, wherein the determining a threshold runoff volume according to the historical runoff sequence comprises:
determining scale parameters and shape parameters of gamma distribution by using a maximum likelihood estimation method based on the historical runoff sequence, wherein the historical runoff sequence obeys the gamma distribution;
acquiring a standardized runoff index, and determining a first parameter according to the standardized runoff index;
determining the threshold runoff amount as a function of the first parameter and an inverse function of the Γ -distribution.
4. The method for predicting the risk of water shortage according to claim 3, wherein the first parameter is calculated by the following formula:
wherein Z represents the standard runoff index, c 0 、c 1 、c 2 、d 1 、d 2 And d 3 Respectively representing sub-parameters in the parameter group, and t represents a first parameter;
calculating the threshold runoff amount by the following formula:
wherein F represents a second parameter, e represents a natural constant, t represents a first parameter, and the inverse function of Γ is Γ -1 (F | β, γ), β representing the scale parameter and γ representing the shape parameter.
5. The method of predicting the risk of water resource shortage according to claim 4, wherein the fitting the historical runoff sequence through the GALSS model and determining the probability distribution function comprises:
obtaining a set of probability distributions, the set of probability distributions comprising: normal distribution, lognormal distribution, gunbel distribution, gamma distribution, and weibull distribution;
determining a probability density function according to the historical runoff sequence and the probability distribution set, wherein the probability density function passes through f (x) t |θ j (t)), x and θ represent constant variables, and time t is a covariate;
determining a gibberellin information criterion GAIC value according to the type of the probability distribution set;
GAIC values were calculated by the following formula:
wherein, # denotes a penalty factor, df denotes an overall degree of freedom in the GAMLSS model,
representing a log-likelihood function;
taking the probability distribution type with the minimum GAIC value as a target probability distribution type, and determining a probability distribution function
x and
representing a constant variable, and time t is a covariate.
6. The method as claimed in claim 5, wherein the determining the first calculation result according to the probability distribution function comprises:
determining first information according to the probability distribution function, wherein the first information represents the probability that the annual runoff volume is lower than the threshold runoff volume after the calculation year;
determining second information according to the first information, wherein the second information represents the probability that the first runoff volume is lower than the threshold runoff volume from the beginning of the calculation year to the h year;
and determining the first calculation result according to the second information.
7. The method for predicting the risk of water shortage according to claim 6, wherein the first information is calculated by the following formula:
wherein p is t, disaster Representing first information, t representing time, Q Disaster recovery A threshold amount of runoff is indicated and,
representing a constant variable;
calculating the second information by the following formula:
wherein f is Disaster recovery (h) Representing second information, h representing a year in which the calculation is stopped, and P representing a probability that the annual runoff volume is lower than the threshold runoff volume one year after the calculation year starts;
calculating the first calculation result by the following formula:
where Eh denotes the first calculation result, h denotes the year in which the calculation was stopped, f Disaster recovery (h) Indicating the probability that the first runoff is below the threshold runoff from the beginning of the year of calculation to the h-th year.
8. The method for predicting the risk of water shortage according to claim 6, wherein after the determining the first calculation result according to the probability distribution function, the method further comprises:
and determining a second calculation result according to the second information, wherein the second calculation result represents the probability that the bore flow is lower than the threshold bore flow in a preset time period.
9. The method for predicting the risk of water shortage according to claim 8, wherein the second calculation result is calculated by the following formula:
wherein R represents a second calculation result, n represents the number of years included in the preset time period, and f Disaster recovery (h) Representing the probability that the first runoff is below the threshold runoff from the beginning of the year of calculation to the h-th year, p t, disaster A probability that the annual runoff volume is below the threshold runoff volume after the beginning of the year of the calculation.
10. A water resource shortage risk prediction device, comprising:
the acquisition module is used for acquiring a historical runoff sequence of the target watershed;
the first determining module is used for determining a threshold runoff quantity according to the historical runoff sequence;
the second determination module is used for determining a probability distribution function by applying the fitting of the generalized additive GALSS model to the historical runoff sequence;
a third determining module, configured to determine a first calculation result according to the probability distribution function, where the first calculation result represents a predicted value from a calculation year to a year in which the runoff volume is first lower than the threshold runoff volume.
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