CN113435631B - Flood forecasting method, flood forecasting system, readable storage medium and computing device - Google Patents
Flood forecasting method, flood forecasting system, readable storage medium and computing device Download PDFInfo
- Publication number
- CN113435631B CN113435631B CN202110626000.2A CN202110626000A CN113435631B CN 113435631 B CN113435631 B CN 113435631B CN 202110626000 A CN202110626000 A CN 202110626000A CN 113435631 B CN113435631 B CN 113435631B
- Authority
- CN
- China
- Prior art keywords
- water level
- flow
- rainfall
- overflow
- station
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000003860 storage Methods 0.000 title claims abstract description 19
- 238000013277 forecasting method Methods 0.000 title claims abstract description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 181
- 239000011159 matrix material Substances 0.000 claims description 83
- 238000000034 method Methods 0.000 claims description 35
- 238000004364 calculation method Methods 0.000 claims description 18
- 230000008020 evaporation Effects 0.000 claims description 17
- 238000001704 evaporation Methods 0.000 claims description 17
- 230000015654 memory Effects 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000009736 wetting Methods 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Mathematical Physics (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Computational Mathematics (AREA)
- Marketing (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Primary Health Care (AREA)
- Educational Administration (AREA)
- Algebra (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Databases & Information Systems (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a flood forecasting method, a flood forecasting system, a readable storage medium and a computing device.
Description
Technical Field
The invention relates to a flood forecasting method, a flood forecasting system, a readable storage medium and computing equipment, and belongs to the field of reservoir dispatching.
Background
China is located on the western coast of the Pacific ocean, has wide region, complex topography and very remarkable continental monsoon climate, thereby causing two major characteristics of uneven distribution and time-course change of water resource regions. The precipitation amount decreases from the southeast coast to the northwest inland, and can be divided into five zones of raininess, wetting, semi-wetting, semiarid, drought and the like in sequence.
Because the precipitation is very unevenly distributed in the area, the phenomenon of unbalanced water and soil resources is caused in the whole country, the Yangtze river basin and the south cultivated land of the Yangtze river only occupy 36% of the whole country, and the water resource amount occupies 80% of the whole country; the water resource amount of the three watercourses of Huang, huai and Hai is only 8 percent of the whole country, the cultivated land is 40 percent of the whole country, and the water resource and soil resource are quite different.
In the wavy plain areas in the middle and west and in the north of China, the land elevation difference is only tens of meters, the river beach is narrow and does not develop, the width is about 10-100 m, and meanwhile, the social economic development is severely restricted due to drought and water shortage, so that a large amount of small reservoirs are established due to the influence of natural conditions and economic conditions, and the method is a better way for solving the water resource shortage in similar areas.
Because a large number of small reservoirs are distributed in the river basin, flood-period flood-stagnation effects are obvious, so that a certain influence is generated on flood forecast, the river basin is generally regarded as a whole in the traditional flood forecast model, and parameters are homogenized to different degrees, so that accurate forecast is difficult to realize.
Disclosure of Invention
The invention provides a flood forecasting method, a flood forecasting system, a readable storage medium and computing equipment, and solves the problems disclosed in the background technology.
In order to solve the technical problems, the invention adopts the following technical scheme:
a flood forecast method, comprising the steps of,
constructing a dynamic rainfall time matrix according to the historical rainfall and the future simulated rainfall of each rainfall station; the historical rainfall is rainfall of a plurality of time periods forward at the current time point, and the future simulated rainfall is simulated rainfall of a plurality of time periods backward at the current time point;
calculating a dynamic water level time matrix according to the dynamic rainfall time matrix, the position correlation of the rainfall station and the water level stations, the water evaporation condition of each water level station and the water taking condition of each water level station;
calculating a dynamic overflow flow time matrix according to the dynamic water level time matrix and the characteristics of each reservoir spillway;
calculating overflow warehouse-in flow at a predicted reservoir dam site by adopting an equal flow timeline method according to river channel parameters and a dynamic overflow flow time matrix of each water level station;
calculating and forecasting the warehousing flow according to the overflow warehousing flow and the interval warehousing flow;
and forecasting flood according to the forecasting warehouse-in flow.
According to the dynamic rainfall time matrix, the position correlation of the rainfall station and the water level station, the water evaporation condition of each water level station and the water taking condition of each water level station, the dynamic water level time matrix is calculated, and the specific process is that,
constructing weights between the associated water level stations and the rain level stations according to the position correlation of the rain level stations and the water level stations;
for each water level station, calculating a face rainfall array of each water level station according to the historical rainfall and the future simulated rainfall of the associated rainfall station and the weight between the water level station and the associated rainfall station;
and calculating a dynamic water level time matrix according to the face rainfall array, the water evaporation condition of each water level station and the water taking condition of each water level station.
The dynamic water level time matrix calculation formula is as follows,
wherein SW (I, J) is the water level of the I-th water level station in the J-th period; SW (I, J-1) is the water level of the J-1 th period of the I-th water level station; SW (I, J) and SW (I, J-1) are elements in the dynamic water level time matrix; MK (J) is the water evaporation amount at the J-th period of the water level station; QS (I, J) is the water intake of the I water level station in the J period; fall (I, J) is the face rainfall array for the J-th period of the I-th water level station.
The formula for calculating the dynamic overflow flow time matrix is as follows,
Flow(I,J)＝B I H(I,J)
the Flow (I, J) is overflow Flow of the ith reservoir in the J period and is an element in a dynamic overflow Flow time matrix; b (B) I Is the comprehensive parameter of the width of the overflow weir crest of the ith reservoir,m I b is the correction coefficient of the overflow channel of the ith reservoir I Is characteristic of the I reservoir spillway; h (I, J) is the comprehensive parameter of the height of the overflow weir top of the ith reservoir,SW (I, J) is the water level of the I water level station in the J period, the water level stations are in one-to-one correspondence with reservoirs, and H0 (I) is the overflow weir top elevation of the I reservoir.
According to the river parameters of each water level station and the dynamic overflow flow time matrix, adopting an equal flow time line method to calculate and forecast the overflow storage flow at the dam site of the reservoir, the concrete process is that,
calculating the time for each overflow flow to reach the forecast reservoir dam site according to the river channel parameters of each water level station and the dynamic overflow flow time matrix;
and calculating overflow storage flow at the predicted reservoir dam site according to the time for each overflow flow to reach the predicted reservoir dam site and the dynamic overflow flow time matrix.
The formula for calculating the time for each overflow flow to reach the predicted reservoir dam site is as follows,
wherein T (I, J) is overflow flow of the ith reservoir in the J period, and the time for reaching the forecast reservoir dam site; l (I) and B (I) are respectively the river channel length and the average river channel width between the overflow point of the ith reservoir and the dam site; flow (I, J) is the overflow Flow of the ith reservoir during the jth period.
The overflow warehouse-in flow formula is calculated as
FlowTotal(J)＝∑Flow(I,J)*T(I,J)
The flow total (J) is the overflow storage flow of the predicted reservoir dam site in the J-th period; t (I, J) is overflow flow of the ith reservoir in the J period, and the time for reaching the forecast reservoir dam site; flow (I, J) is the overflow Flow of the ith reservoir during the jth period.
A flood forecast system, comprising,
the rainfall time matrix construction module: constructing a dynamic rainfall time matrix according to the historical rainfall and the future simulated rainfall of each rainfall station; the historical rainfall is rainfall of a plurality of time periods forward at the current time point, and the future simulated rainfall is simulated rainfall of a plurality of time periods backward at the current time point;
the water level time matrix calculation module: calculating a dynamic water level time matrix according to the dynamic rainfall time matrix, the position correlation of the rainfall station and the water level stations, the water evaporation condition of each water level station and the water taking condition of each water level station;
overflow flow time matrix calculation module: calculating a dynamic overflow flow time matrix according to the dynamic water level time matrix and the characteristics of each reservoir spillway;
and the overflow warehouse-in flow calculation module: calculating overflow warehouse-in flow at a predicted reservoir dam site by adopting an equal flow timeline method according to river channel parameters and a dynamic overflow flow time matrix of each water level station;
and the forecast warehouse-in flow calculation module is as follows: calculating and forecasting the warehousing flow according to the overflow warehousing flow and the interval warehousing flow;
and a forecasting module: and forecasting flood according to the forecasting warehouse-in flow.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a flood forecast method.
A computing device comprising one or more processors, one or more memories, and one or more programs, wherein one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing a flood forecasting method.
The invention has the beneficial effects that: the invention calculates overflow warehouse-in flow based on a matrix transformation algorithm, adopts a meshing-like refinement treatment method, fully considers the flood-stagnation functions of a plurality of medium and small reservoirs, greatly improves the forecasting precision, and provides a great effective technical support for reservoir dispatching.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
As shown in fig. 1, a flood forecasting method includes the following steps:
step 1, constructing a dynamic rainfall time matrix according to the historical rainfall and the future simulated rainfall of each rainfall station; the historical rainfall is rainfall of a plurality of time periods forward at the current time point, and the future simulated rainfall is simulated rainfall of a plurality of time periods backward at the current time point;
step 2, calculating a dynamic water level time matrix according to the dynamic rainfall time matrix, the position correlation of the rainfall station and the water level stations, the water evaporation condition of each water level station and the water taking condition of each water level station;
step 3, calculating a dynamic overflow flow time matrix according to the dynamic water level time matrix and the characteristics of each reservoir spillway;
step 4, calculating and forecasting overflow warehouse-in flow at the dam site of the reservoir by adopting an equal flow time line method according to river channel parameters and dynamic overflow flow time matrixes of all water level stations;
step 5, calculating and forecasting the warehousing flow according to the overflow warehousing flow and the interval warehousing flow;
and 6, forecasting flood according to the forecasting warehouse-in flow.
The method calculates overflow warehouse-in flow based on a matrix transformation algorithm, adopts a meshing-like refinement treatment method, fully considers the flood-stagnation functions of a plurality of small and medium reservoirs, greatly improves the forecasting precision, and provides a great effective technical support for reservoir dispatching.
In order to measure the rainfall, a rain gauge is installed at each rainfall station, the rain gauge can only measure the rainfall in real time, namely, each historical rainfall is the rainfall measured in real time, and the future simulated rainfall is the predicted rainfall, so that the whole dynamic rainfall time matrix can be:
wherein Rain (K, J) is the rainfall of the J period of the Kth rainfall station; in general, a dynamic rainfall time matrix is constructed according to the rainfall of the first 3 days and the rainfall of the last 3 days of the current time point, and 1 hour is taken as a period;
if Rain (k, j) is the rainfall at the jth period of the kth rainfall station, which is the historical rainfall, it can be expressed as:
Rain(k,j)＝(Times(k,j)-Times(k,j-1))*0.5
wherein, times (k, j) is the number of Times of tipping the rain gauge in the jth period of the kth rain gauge station, times (k, j-1) is the number of Times of tipping the rain gauge in the jth-1 period of the kth rain gauge station, and 0.5 is the capacity of the rain gauge tipping, which means that the rainfall is 0.5mm after the tipping.
According to the dynamic rainfall time matrix, the position correlation of the rainfall station and the water level stations, the water evaporation condition of each water level station and the water taking condition of each water level station, the dynamic water level time matrix is calculated, and the specific process is as follows:
21 Constructing weights between the associated water level station and the rain level station according to the position correlation of the rain level station and the water level station;
each reservoir corresponds to one water level station, one water level station can be associated with one or more rainfall stations, the ith water level station is assumed to be associated with k rainfall stations, the Area corresponding to the ith water level station is assumed to be Areatotal (I), and the Area represented by the kth rainfall station in the Area range is Area k Then the weight between the I water level station and the k rainfall station can be calculated as
22 For each water level station, calculating a face rainfall array of each water level station according to the historical rainfall and the future simulated rainfall of the associated rainfall station and the weight between the water level station and the associated rainfall station;
assuming that Fall (I, J) is the face rainfall array of the I-th water level station in the J-th period, fall (I, J) = [ Rain (1, J) = 1 Rain(2,J)*Weigh 2 … Rain(k,J)*Weigh k ]；
The face rainfall arrays of all water level stations can construct a face rainfall matrix.
23 Calculating a dynamic water level time matrix according to the surface rainfall array, the water evaporation condition of each water level station and the water taking condition of each water level station;
the dynamic water level time matrix calculation formula is as follows,
wherein SW (I, J) is the water level of the J-th period of the I-th water level station, SW (I, J-1) is the water level of the J-1-th period of the I-th water level station, SW (I, J) and SW (I, J-1) are elements in a dynamic water level time matrix, MK (J) is the water evaporation amount of the J-th period of the water level station, the value is basically a fixed value in each time period of each year, and for the same basin, each water level station is basically the same in the same time period, namely, the water evaporation amount is irrelevant to the water level station, only relates to the time period, QS (I, J) is the water intake amount of the J-th period of the I-th water level station, the value relates to the water intake plan of each small reservoir, and activities such as industrial and agricultural production and life around the water level station.
The dynamic water level time matrix is combined with the spillway characteristics of each reservoir, and the dynamic overflow flow time matrix can be calculated according to the following formula:
Flow(I,J)＝B I H(I,J)
the water level stations are in one-to-one correspondence with reservoirs, and because the subscript of the reservoirs is also I, namely the I water level station corresponds to the I reservoir, flow (I, J) is overflow Flow of the J period of the I reservoir, and is an element in a dynamic overflow Flow time matrix; b (B) I Is the comprehensive parameter of the width of the overflow weir crest of the ith reservoir,m I b is the correction coefficient of the overflow channel of the ith reservoir I Is characteristic of the I reservoir spillway; h (I, J) is the comprehensive parameter of the height of the overflow weir top of the ith reservoir,h0 And (I) is the top elevation of the overflow weir of the ith reservoir.
And calculating the overflow warehouse-in flow at the predicted reservoir dam site by using the dynamic overflow flow time matrix and the river channel parameters of each water level station and adopting a constant flow time line method. Because the traditional equal flow time line method does not consider the regulation effect of the river channel and the influence of the flow rate on the flow speed, and influences the subsequent precision, the traditional equal flow time line method is improved, and the specific process is as follows:
41 According to the river parameters of each water level station and the dynamic overflow flow time matrix, calculating the time for each overflow flow to reach the forecast reservoir dam site;
the specific calculation formula is as follows:
wherein T (I, J) is overflow flow of the ith reservoir in the J period, and the time for reaching the forecast reservoir dam site; l (I) and B (I) are respectively the river length and the average river width between the overflow point of the ith reservoir and the dam site.
42 Calculating overflow storage flow at the predicted reservoir dam site according to the time for each overflow flow to reach the predicted reservoir dam site and the dynamic overflow flow time matrix;
FlowTotal(J)＝∑Flow(I,J)*T(I,J)
and the FlowTotal (J) is the overflow storage flow at the forecast reservoir dam site of the J-th period.
And adding the section warehousing flow on the basis of the overflow warehousing flow to obtain the forecasting warehousing flow, and forecasting flood according to the forecasting warehousing flow. Because the overflow flow of the flood-stagnation area is large in proportion and reaches 80-90%, after the accuracy of the overflow warehousing flow is improved, the accuracy of forecasting the warehousing flow is correspondingly improved, and the accuracy of forecasting the flood is also improved.
The method can realize the calculation of the overflow flow of the medium and small reservoirs densely distributed in the flood-stagnation area, breaks through the limitation that the prediction period in the short-term flood prediction does not exceed the converging time of the river channel, prolongs the prediction period by 10-20 hours, greatly improves the flood prediction precision of the flood-stagnation area, provides enough preparation time for reservoir flood prevention scheduling, provides technical support for protecting the life and property safety of people downstream of the reservoir in the flood period, and has general reference significance for short-term flood prediction of the flood-stagnation area.
A flood forecast system comprising:
the rainfall time matrix construction module: constructing a dynamic rainfall time matrix according to the historical rainfall and the future simulated rainfall of each rainfall station; the historical rainfall is rainfall of a plurality of time periods forward at the current time point, and the future simulated rainfall is simulated rainfall of a plurality of time periods backward at the current time point;
the water level time matrix calculation module: calculating a dynamic water level time matrix according to the dynamic rainfall time matrix, the position correlation of the rainfall station and the water level stations, the water evaporation condition of each water level station and the water taking condition of each water level station;
overflow flow time matrix calculation module: calculating a dynamic overflow flow time matrix according to the dynamic water level time matrix and the characteristics of each reservoir spillway;
and the overflow warehouse-in flow calculation module: calculating overflow warehouse-in flow at a predicted reservoir dam site by adopting an equal flow timeline method according to river channel parameters and a dynamic overflow flow time matrix of each water level station;
and the forecast warehouse-in flow calculation module is as follows: calculating and forecasting the warehousing flow according to the overflow warehousing flow and the interval warehousing flow;
and a forecasting module: and forecasting flood according to the forecasting warehouse-in flow.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a flood forecast method.
A computing device comprising one or more processors, one or more memories, and one or more programs, wherein one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing a flood forecasting method.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (7)
1. A flood forecasting method is characterized in that: comprising the steps of (a) a step of,
constructing a dynamic rainfall time matrix according to the historical rainfall and the future simulated rainfall of each rainfall station; the historical rainfall is rainfall of a plurality of time periods forward at the current time point, and the future simulated rainfall is simulated rainfall of a plurality of time periods backward at the current time point;
calculating a dynamic water level time matrix according to the dynamic rainfall time matrix, the position correlation of the rainfall station and the water level stations, the water evaporation condition of each water level station and the water taking condition of each water level station;
the method comprises the following steps: constructing weights between the associated water level stations and the rain level stations according to the position correlation of the rain level stations and the water level stations; for each water level station, calculating a face rainfall array of each water level station according to the historical rainfall and the future simulated rainfall of the associated rainfall station and the weight between the water level station and the associated rainfall station; calculating a dynamic water level time matrix according to the surface rainfall array, the water evaporation condition of each water level station and the water taking condition of each water level station; wherein, the calculation formula of the dynamic water level time matrix is as follows,
the water level of the water level station I in the water level station J period is SW (I, J-1), and the water level station I corresponds to the water level of the water reservoir one by one; SW (I, J) and SW (I, J-1) are elements in the dynamic water level time matrix; MK (J) is the water evaporation amount at the J-th period of the water level station; QS (I, J) is the water intake of the I water level station in the J period; fall (I, J) is the face rainfall array of the J period of the I water level station;
calculating a dynamic overflow flow time matrix according to the dynamic water level time matrix and the characteristics of each reservoir spillway; wherein, the formula for calculating the dynamic overflow flow time matrix is as follows,
Flow(I,J)＝B I H(I,J)
the Flow (I, J) is overflow Flow of the ith reservoir in the J period and is an element in a dynamic overflow Flow time matrix; b (B) I Is the comprehensive parameter of the width of the overflow weir crest of the ith reservoir,m I b is the correction coefficient of the overflow channel of the ith reservoir I Is characteristic of the I reservoir spillway; h (I, J) is the comprehensive parameter of the height of the overflow weir top of the ith reservoir,h0 (I) is the top elevation of the overflow weir of the ith reservoir;
calculating overflow warehouse-in flow at a predicted reservoir dam site by adopting an equal flow timeline method according to river channel parameters and a dynamic overflow flow time matrix of each water level station;
calculating and forecasting the warehousing flow according to the overflow warehousing flow and the interval warehousing flow;
and forecasting flood according to the forecasting warehouse-in flow.
2. The flood forecast method of claim 1, wherein: according to the river parameters of each water level station and the dynamic overflow flow time matrix, adopting an equal flow time line method to calculate and forecast the overflow storage flow at the dam site of the reservoir, the concrete process is that,
calculating the time for each overflow flow to reach the forecast reservoir dam site according to the river channel parameters of each water level station and the dynamic overflow flow time matrix;
and calculating overflow storage flow at the predicted reservoir dam site according to the time for each overflow flow to reach the predicted reservoir dam site and the dynamic overflow flow time matrix.
3. A flood forecast method according to claim 2, characterized by: the formula for calculating the time for each overflow flow to reach the predicted reservoir dam site is as follows,
wherein T (I, J) is overflow flow of the ith reservoir in the J period, and the time for reaching the forecast reservoir dam site; l (I) and B (I) are respectively the river length and the average river width between the overflow point of the ith reservoir and the dam site.
4. A flood forecast method according to claim 2, characterized by: the overflow warehouse-in flow formula is calculated as
FlowTotal(J)＝∑Flow(I,J)*T(I,J)
The flow total (J) is the overflow storage flow of the predicted reservoir dam site in the J-th period; t (I, J) is overflow flow of the ith reservoir in the J period and reaches the time of forecasting the reservoir dam site.
5. A flood forecast system, characterized by: comprising the steps of (a) a step of,
the rainfall time matrix construction module: constructing a dynamic rainfall time matrix according to the historical rainfall and the future simulated rainfall of each rainfall station; the historical rainfall is rainfall of a plurality of time periods forward at the current time point, and the future simulated rainfall is simulated rainfall of a plurality of time periods backward at the current time point;
the water level time matrix calculation module: calculating a dynamic water level time matrix according to the dynamic rainfall time matrix, the position correlation of the rainfall station and the water level stations, the water evaporation condition of each water level station and the water taking condition of each water level station;
the method comprises the following steps: constructing weights between the associated water level stations and the rain level stations according to the position correlation of the rain level stations and the water level stations; for each water level station, calculating a face rainfall array of each water level station according to the historical rainfall and the future simulated rainfall of the associated rainfall station and the weight between the water level station and the associated rainfall station; calculating a dynamic water level time matrix according to the surface rainfall array, the water evaporation condition of each water level station and the water taking condition of each water level station; wherein, the calculation formula of the dynamic water level time matrix is as follows,
the water level of the water level station I in the water level station J period is SW (I, J-1), and the water level station I corresponds to the water level of the water reservoir one by one; SW (I, J) and SW (I, J-1) are elements in the dynamic water level time matrix; MK (J) is the water evaporation amount at the J-th period of the water level station; QS (I, J) is the water intake of the I water level station in the J period; fall (I, J) is the face rainfall array of the J period of the I water level station;
overflow flow time matrix calculation module: calculating a dynamic overflow flow time matrix according to the dynamic water level time matrix and the characteristics of each reservoir spillway;
wherein, the formula for calculating the dynamic overflow flow time matrix is as follows,
Flow(I,J)＝B I H(I,J)
the Flow (I, J) is overflow Flow of the ith reservoir in the J period and is an element in a dynamic overflow Flow time matrix; b (B) I Is the comprehensive parameter of the width of the overflow weir crest of the ith reservoir,m I b is the correction coefficient of the overflow channel of the ith reservoir I Is characteristic of the I reservoir spillway; h (I, J) is the comprehensive parameter of the height of the overflow weir top of the ith reservoir,h0 (I) is the top elevation of the overflow weir of the ith reservoir;
and the overflow warehouse-in flow calculation module: calculating overflow warehouse-in flow at a predicted reservoir dam site by adopting an equal flow timeline method according to river channel parameters and a dynamic overflow flow time matrix of each water level station;
and the forecast warehouse-in flow calculation module is as follows: calculating and forecasting the warehousing flow according to the overflow warehousing flow and the interval warehousing flow;
and a forecasting module: and forecasting flood according to the forecasting warehouse-in flow.
6. A computer readable storage medium storing one or more programs, characterized by: the one or more programs include instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-4.
7. A computing device, comprising:
one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110626000.2A CN113435631B (en) | 2021-06-04 | 2021-06-04 | Flood forecasting method, flood forecasting system, readable storage medium and computing device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110626000.2A CN113435631B (en) | 2021-06-04 | 2021-06-04 | Flood forecasting method, flood forecasting system, readable storage medium and computing device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113435631A CN113435631A (en) | 2021-09-24 |
| CN113435631B true CN113435631B (en) | 2024-02-02 |
Family
ID=77803735
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110626000.2A Active CN113435631B (en) | 2021-06-04 | 2021-06-04 | Flood forecasting method, flood forecasting system, readable storage medium and computing device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113435631B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115203639B (en) * | 2022-06-21 | 2023-03-10 | 中国长江三峡集团有限公司 | Irregular grid surface rainfall calculation method and system based on matrix operation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681848A (en) * | 2018-08-25 | 2018-10-19 | 黄河水利委员会黄河水利科学研究院 | One kind " Trinity " small reservoir or silt arrester flood season method for early warning |
| CN108921345A (en) * | 2018-06-28 | 2018-11-30 | 杭州市水文水资源监测总站 | The river flood-peak stage Real-time Forecasting Method that precipitation forecast is coupled with support vector machines |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130346150A1 (en) * | 2012-06-21 | 2013-12-26 | Data Ventures, Inc. | System, method, and computer program product for forecasting sales |
-
2021
- 2021-06-04 CN CN202110626000.2A patent/CN113435631B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108921345A (en) * | 2018-06-28 | 2018-11-30 | 杭州市水文水资源监测总站 | The river flood-peak stage Real-time Forecasting Method that precipitation forecast is coupled with support vector machines |
| CN108681848A (en) * | 2018-08-25 | 2018-10-19 | 黄河水利委员会黄河水利科学研究院 | One kind " Trinity " small reservoir or silt arrester flood season method for early warning |
Non-Patent Citations (1)
| Title |
|---|
| 石板水库洪水预报;陈鸣, 陆卫鲜, 万永波, 邹立军;人民长江(11);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113435631A (en) | 2021-09-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhao et al. | Three-dimensional hydrodynamic and water quality model for TMDL development of Lake Fuxian, China | |
| Wu et al. | Simulation of soil loss processes based on rainfall runoff and the time factor of governance in the Jialing River Watershed, China | |
| CN102508961A (en) | Design method for high-resolution fully distributed hydrological model TOPX | |
| CN113011685A (en) | Simulation prediction method for water level change of inland lake in runoff data-free area | |
| CN102419788A (en) | Method for designing distributed-type hydrographical model based on penetration-storage integrated dynamic runoff yield mechanism | |
| CN105320827A (en) | Rainfall runoff forecasting system | |
| CN103093114A (en) | Distributed-type river basin water deficit calculating method based on terrain and soil characteristics | |
| CN113742910A (en) | Reservoir water inflow early warning and forecasting method and system based on flood forecasting of medium and small watershed | |
| CN112257352A (en) | Coupling method and system of one-dimensional hydrodynamic model and two-dimensional hydrodynamic model | |
| Guang et al. | Transformation of surface water and groundwater and water balance in the agricultural irrigation area of the Manas River Basin, China | |
| CN105760703B (en) | A kind of Flood Forecasting Method that the simulation that discharges water is stored based on polymerization reservoir | |
| CN113435631B (en) | Flood forecasting method, flood forecasting system, readable storage medium and computing device | |
| CN106598918A (en) | Non-uniform designed flood calculation method based on quantile regression | |
| Zhou et al. | WRF model for precipitation simulation and its application in real-time flood forecasting in the Jinshajiang River Basin, China | |
| Xu | Response of land accretion of the Yellow River delta to global climate change and human activity | |
| CN116562448B (en) | Quick prediction method for flood inundation of flood storage area based on coupling of grid analysis and artificial intelligence technology | |
| CN103020735B (en) | A kind of sky yardstick primary productivity estimation method based on photoresponse segmentation feature | |
| CN107092796A (en) | A kind of evaluation method of soil watershed rainwash regulating power | |
| Jingjing et al. | Identification of impacts of climate change and direct human activities on streamflow in Weihe River Basin in Northwest China | |
| CN106355264A (en) | Combined prediction method of reference crop evapotranspiration | |
| CN113869804B (en) | Power grid equipment risk early warning method and system under flood disaster | |
| CN105976103B (en) | A kind of Flood Forecasting Method based on dynamic water storage capacity | |
| CN114742299A (en) | Method for constructing basin sediment prediction model based on runoff erosion power | |
| CN107274113A (en) | A kind of new maintenance and repair of dam Decision Evaluation method | |
| Yang et al. | Potential causes of salinity trend in seawater overlaying a mudflat inside a semi-enclosed bay |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |