CN110288149A - Multizone water resource supply and demand risk evaluating method and equipment - Google Patents
Multizone water resource supply and demand risk evaluating method and equipment Download PDFInfo
- Publication number
- CN110288149A CN110288149A CN201910547454.3A CN201910547454A CN110288149A CN 110288149 A CN110288149 A CN 110288149A CN 201910547454 A CN201910547454 A CN 201910547454A CN 110288149 A CN110288149 A CN 110288149A
- Authority
- CN
- China
- Prior art keywords
- probability distribution
- distribution function
- water resources
- region
- water
- 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.)
- Pending
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 253
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000005315 distribution function Methods 0.000 claims abstract description 127
- 241000039077 Copula Species 0.000 claims abstract description 56
- 238000007476 Maximum Likelihood Methods 0.000 claims abstract description 39
- 238000007747 plating Methods 0.000 claims abstract description 25
- 238000004458 analytical method Methods 0.000 claims abstract description 24
- 238000005457 optimization Methods 0.000 claims abstract description 24
- 238000009826 distribution Methods 0.000 claims description 49
- 230000006870 function Effects 0.000 claims description 40
- 238000003860 storage Methods 0.000 claims description 22
- 238000012502 risk assessment Methods 0.000 claims description 19
- 230000015654 memory Effects 0.000 claims description 14
- 238000011161 development Methods 0.000 claims description 10
- 238000007689 inspection Methods 0.000 claims description 10
- 238000013439 planning Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 3
- 230000005055 memory storage Effects 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 9
- 238000013461 design Methods 0.000 description 8
- 238000011160 research Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 238000004088 simulation Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 239000002351 wastewater Substances 0.000 description 4
- 238000012512 characterization method Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000004744 fabric Substances 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000013468 resource allocation Methods 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 230000008713 feedback mechanism Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000009916 joint effect Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000013011 mating Effects 0.000 description 1
- 238000003012 network analysis Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 210000003296 saliva Anatomy 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012916 structural analysis Methods 0.000 description 1
- 239000008400 supply water Substances 0.000 description 1
- 230000009466 transformation Effects 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
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the present invention provides a kind of multizone water resource supply and demand risk evaluating method and equipment, this method includes the Water resources data for obtaining multizone, and determines the characteristic indexs such as gross amount of water resources and the water demand amount of multizone water resource according to the Water resources data；For each region, the marginal probability distribution function of the gross amount of water resources in the region is determined by maximum likelihood estimate；According to the marginal probability distribution function of the gross amount of water resources in each region, the joint probability distribution function of the gross amount of water resources in each region is constructed by maximum likelihood estimate；The joint probability distribution function is introduced into the double-deck Model for Multi-Objective Optimization, constructs the double plating layer analyzed based on Copula, to determine multizone Water Resources Allocation scheme according to the double plating layer based on Copula analysis.The embodiment of the present invention can accurately analyze water resource state between supply and demand, provide reasonable Water Resources Allocation scheme.
Description
Technical field
The present embodiments relate to water resource analysis technical fields more particularly to a kind of multizone water resource supply and demand risk to comment
Valence method and apparatus.
Background technique
With the development of urbanization, people are constantly attracted by growing metropolitan area, to obtain economic opportunity
And social dominance.The economy of rapid development and growing agricultural and industrial production demand cause huge pressure to water environment
Power.As China's socio-economic development enters the new normality stage, though water resources problems obtain a degree of alleviation, water supply lance
Shield, supply and demand shortage problem still exist, and become one of the main problem for hindering socio-economic development.
Research of the prior art in terms of water resource supply and demand, substantially all only to needing water or supply water one-sided to consider.
However, only ignoring to confession from water is needed or the case where unilaterally studying water resource of supplying water, situation need to being cooperated to comment
Valence is unfavorable for providing reasonable evaluation analysis result.
Summary of the invention
The embodiment of the present invention provides a kind of multizone water resource supply and demand risk evaluating method and equipment, to improve water resource confession
The accuracy and reasonability that need to be evaluated.
In a first aspect, the embodiment of the present invention provides a kind of multizone water resource supply and demand risk evaluating method, comprising:
The Water resources data of multizone is obtained, and determines that the characterization of multizone water resource refers to according to the Water resources data
Mark；The characteristic index includes the gross amount of water resources and water demand amount of each regional water resources；
For each region, the marginal probability distribution of the gross amount of water resources in the region is determined by maximum likelihood estimate
Function obtains the marginal probability distribution function of the gross amount of water resources in each region；
According to the marginal probability distribution function of the gross amount of water resources in each region, constructed by maximum likelihood estimate each
The joint probability distribution function of the gross amount of water resources in region；
The joint probability distribution function is introduced into the double-deck Model for Multi-Objective Optimization, constructs the bilayer analyzed based on Copula
Multiobjective programming models, to determine that multizone water resource is matched according to the double plating layer based on Copula analysis
Set scheme.
In a kind of possible design, the side of the gross amount of water resources that the region is determined by maximum likelihood estimate
Edge probability-distribution function, comprising:
The marginal probability distribution letter of multiple types of the gross amount of water resources in the region is determined by maximum likelihood estimate
Number；
It is fitted goodness to the marginal probability distribution function of the multiple type to examine, and true according to the inspection result
The marginal probability distribution type in the fixed region.
In a kind of possible design, the marginal probability distribution function of the multiple type is comprised at least one of the following: just
Too distribution pattern marginal probability distribution function, logarithm just too distribution pattern marginal probability distribution function, Weibull distribution type side
Edge probability-distribution function, gamma distribution pattern marginal probability distribution function.
In a kind of possible design, the marginal probability distribution function of the gross amount of water resources according to each region,
The joint probability distribution function of the gross amount of water resources in each region is constructed by maximum likelihood estimate, comprising:
According to the marginal probability distribution function of the gross amount of water resources in each region, constructed by maximum likelihood estimate each
The joint probability distribution function of multiple types of the gross amount of water resources in region；The joint probability distribution function packet of the multiple type
Include following at least one: Gauss Copula function, binary Archimedean Copula function；
Goodness is fitted to the joint probability distribution function of the multiple type to examine, and institute is determined according to inspection result
State the joint probability distribution function of multizone water resource.
It is described that the joint probability distribution function is introduced into the double-deck Model for Multi-Objective Optimization in a kind of possible design,
Before the double plating layer that building is analyzed based on Copula, further includes:
It is by first layer target of total economic goal of the multizone and with the corresponding economic goal in each region
Two layers of target establish the double-deck multiple-objection optimization using water resources quantity, ecology, society, economic development minimum planning value as constraint condition
Model.
Second aspect, the embodiment of the present invention provide a kind of multizone water resource supply and demand risk assessment equipment, comprising:
Module is obtained, determines multizone water for obtaining the Water resources data of multizone, and according to the Water resources data
The characteristic index of resource；The characteristic index includes the gross amount of water resources and water demand amount of each regional water resources；
Edge function determining module determines the water in the region by maximum likelihood estimate for being directed to each region
The marginal probability distribution function of total resources obtains the marginal probability distribution function of the gross amount of water resources in each region；
Copula determining module is led to for the marginal probability distribution function according to the gross amount of water resources in each region
Cross the joint probability distribution function that maximum likelihood estimate constructs the gross amount of water resources in each region；
Module is constructed, for the joint probability distribution function to be introduced the double-deck Model for Multi-Objective Optimization, building is based on
The double plating layer of Copula analysis, with true according to the double plating layer based on Copula analysis
Determine multizone Water Resources Allocation scheme.
In a kind of possible design, the edge function determining module is specifically used for:
The marginal probability distribution letter of multiple types of the gross amount of water resources in the region is determined by maximum likelihood estimate
Number；
It is fitted goodness to the marginal probability distribution function of the multiple type to examine, and true according to the inspection result
The marginal probability distribution type in the fixed region.
In a kind of possible design, the marginal probability distribution function of the multiple type is comprised at least one of the following: just
Too distribution pattern marginal probability distribution function, logarithm just too distribution pattern marginal probability distribution function, Weibull distribution type side
Edge probability-distribution function, gamma distribution pattern marginal probability distribution function.
The third aspect, the embodiment of the present invention provide a kind of multizone water resource supply and demand risk assessment equipment, comprising: at least one
A processor and memory；
The memory stores computer executed instructions；
At least one described processor executes the computer executed instructions of memory storage so that it is described at least one
Processor executes method described in the various possible designs of first aspect and first aspect as above.
Fourth aspect, the embodiment of the present invention provide a kind of computer readable storage medium, the computer-readable storage medium
Computer executed instructions are stored in matter, when processor execute the computer executed instructions when, realize first aspect as above with
And method described in the various possible designs of first aspect.
Multizone water resource supply and demand risk evaluating method provided in this embodiment and equipment, this method is by obtaining multizone
Water resources data, and determine according to the Water resources data gross amount of water resources and the water demand amount etc. of multizone water resource
Characteristic index；For each region, the marginal probability point of the gross amount of water resources in the region is determined by maximum likelihood estimate
Cloth function obtains the marginal probability distribution function of the gross amount of water resources in each region；According to the gross amount of water resources in each region
Marginal probability distribution function constructs the joint probability distribution function of the gross amount of water resources in each region by maximum likelihood estimate；
The joint probability distribution function is introduced into the double-deck Model for Multi-Objective Optimization, constructs the double-deck multiple target rule analyzed based on Copula
Model is drawn, to determine multizone Water Resources Allocation scheme according to the double plating layer based on Copula analysis,
Water resource state between supply and demand can accurately be analyzed, reasonable Water Resources Allocation scheme is provided.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without any creative labor, can be with
It obtains other drawings based on these drawings.
Fig. 1 is the structural schematic diagram of cause and effect pessimistic concurrency control index system；
Fig. 2 is the flow diagram for the multizone water resource supply and demand risk evaluating method that one embodiment of the invention provides；
Fig. 3 is the flow diagram for the multizone water resource supply and demand risk evaluating method that further embodiment of this invention provides；
Fig. 4 is the structural schematic diagram for the multizone water resource supply and demand risk assessment equipment that further embodiment of this invention provides；
Fig. 5 is the structural schematic diagram for the multizone water resource supply and demand risk assessment equipment that further embodiment of this invention provides；
Fig. 6 is the hardware configuration signal for the multizone water resource supply and demand risk assessment equipment that further embodiment of this invention provides
Figure.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Currently, there is research from water resource supply and demand status, the secondary equilibrium of supply and demand point has been carried out to different year water resource
Analysis research.Using supply and demand analysis table, available water only consider in area water supply and have become water supply project by taping the latent power, being transformed,
The available water newly increased after mating and water rational allocation, water requirement are analyzed by water requirement under water-saving condition is strengthened.
This method does not have rigorous on statistical significance, it is also difficult to guarantee the correctness of analysis.However, cannot using above-mentioned technology
Randomness, the dynamic for solving the problems, such as water resource change, for the long-term rule of water resource, the finger that accidental risk is not evaded
Meaning is led, error probability is higher.
There are also researchs to be based on system dynamic modeling method, using DPSIRM causal net model foundation index system (such as Fig. 1
It is shown), construct water resources in Heihe River Basin administrative model, and the non-water money that 3 kinds of Heihe River basin Ganzhou District of analogue simulation is different
Source control mode.In the model, including drive module, modular pressure, block of state, influence module, corresponding module and management
Module.System dynamics model (System Dynamic, SD) modeling process first has to carry out network analysis to research object；Secondly
System structural analysis, dividing system structure and sub-block are carried out, determines the overall feedback mechanism with part；Third step establishes mathematics
, specification model；4th step is guidance with System Dynamics Theory, carries out simulation and policy analysis by model, can be into one
Step profiling system obtains more information, it is found that then new problem repairs model in turn；5th step examines assessment models.
However, used model, index system is huge in the technical solution, data acquisition is difficult.The result of SD simulation may not can
It leans on, climate change, it is bigger to study influence of area's social capital to management module, and these be in a model due to can not quantify
Do not consider.
Against the above technical problems, the embodiment of the invention provides a kind of multizone water resource supply and demand risk evaluating method,
To solve the problems, such as that water resource supply and demand evaluation analysis inaccuracy is unreasonable.
Technical solution of the present invention is described in detail with specifically embodiment below.These specific implementations below
Example can be combined with each other, and the same or similar concept or process may be repeated no more in some embodiments.
Fig. 2 is the flow diagram for the multizone water resource supply and demand risk evaluating method that one embodiment of the invention provides.Such as
Shown in Fig. 2, this method comprises:
201, the Water resources data of multizone is obtained, and determines the characterization of multizone water resource according to the Water resources data
Index；The characteristic index includes the gross amount of water resources and water demand amount of each regional water resources.
In practical application, the executing subject of this method can be terminal device or server with computing capability.
Specifically, can by document, on the spot investigation and related data collection, grasp the water resource of multizone
Basic condition chooses the index that can rationally characterize water resource, such as the gross amount of water resources and water demand amount in each region,
Wherein, gross amount of water resources index is used to carry out the simulation of water resource, and is used as the marginal distribution function of building water resource, water resource
Demand is used as the input data of the double plating layer based on Copula analysis of subsequent step building, is multizone
The Coordination Decision of water resource provides basis.
202, it is directed to each region, the marginal probability of the gross amount of water resources in the region is determined by maximum likelihood estimate
Distribution function obtains the marginal probability distribution function of the gross amount of water resources in each region.
In practical application, multiple types of the gross amount of water resources in the region can be determined by maximum likelihood estimate
Marginal probability distribution function；The marginal probability distribution function of the multiple type comprises at least one of the following: just too distribution pattern
Marginal probability distribution function, logarithm just too distribution pattern marginal probability distribution function, Weibull distribution type edge probability distribution
Function, gamma distribution pattern marginal probability distribution function.
It is fitted goodness to the marginal probability distribution function of the multiple type to examine, and true according to the inspection result
The marginal probability distribution type in the fixed region.
Specifically, first obtaining the marginal probability distribution letter of the water resource in each region using maximum likelihood method parameter Estimation
Number, is examined using Kolmogorov-Smirnov (K-S) and Anderson-Darling (A-D) is examined and root-mean-square error
(RMSE) goodness is fitted to the edge distribution of generation to examine.The test of fitness of fot can be used for examining maximum likelihood estimate raw
At edge distribution match value and the fitting degree of measured data value that is collected into.The goodness of fit of Joint Distribution then utilizes
Rosenblatt Transformation Tests method is tested.Pass through the edge distribution test of fitness of fot again as a result, most suitable water is selected to provide
Source marginal probability distribution type (distribution pattern includes: normal distribution, logarithm normal distribution, Weibull distribution and Gamma distribution,
Each distribution pattern is all a kind of form of distribution function).This is used based on the respective gross amount of water resources data in three regions
Joseph Pearman Spearman (ρ _ n) ρ, Ken Deer Kendall (τ _ n) and Pearson Pearson (r_n) correlation coefficient process calculate capital
Correlation degree between the three's water resource available quantity of saliva Ji region, to verify each interregional joint property.
203, according to the marginal probability distribution function of the gross amount of water resources in each region, pass through maximum likelihood estimate structure
Build the joint probability distribution function of the gross amount of water resources in each region.
In the present embodiment, according to the marginal probability distribution function of the gross amount of water resources in each region, pass through maximum likelihood
The joint probability distribution function of the gross amount of water resources in each region of estimation technique building is Copula function.Copula function is description
Be correlation between variable, it is actually a kind of that joint distribution function and their own marginal distribution function are connected to one
The function risen, therefore it is also referred to as contiguous function.
Optionally, according to the marginal probability distribution function of the gross amount of water resources in each region, pass through maximal possibility estimation
Method constructs the joint probability distribution function of multiple types of the gross amount of water resources in each region；The joint probability of the multiple type point
Cloth function comprises at least one of the following: Gauss Copula function, binary Archimedean Copula function.
Goodness is fitted to the joint probability distribution function of the multiple type to examine, and institute is determined according to inspection result
State the joint probability distribution function of multizone water resource.
Wherein, the binary Archimedean Copula function can be Gumbel Copula function, Clayton Copula
Any one of function and Frank Copula function.
Specifically, joint probability distribution function is constructed by maximum likelihood method parameter Estimation, by multiple regions water resource
Marginal distribution function is coupled to Copula function, constructs a plurality of types of Copula functions, such as can construct four kinds respectively
(constructed type has Copula function: Gaussian Copula function, Gumbel Copula function, Clayton Copula letter
Several and Frank Copula function), it recycles the test of fitness of fot to select most suitable Copula type function, obtains multizone
The joint probability distribution function of water resource reflects the Dependence Structure between multizone.
204, the joint probability distribution function is introduced into the double-deck Model for Multi-Objective Optimization, what building was analyzed based on Copula
Double plating layer, to determine that multizone water provides according to the double plating layer based on Copula analysis
Source allocation plan.
In practical application, Copula function is introduced into the double-deck Model for Multi-Objective Optimization, constructs pair analyzed based on Copula
Layer Multiobjective programming models.And then by multizone water resource distribute research rationally for, comprehensively consider grain security, the energy peace
Different joint probabilities are arranged under the multiple restrictive condition such as water resource and social economy in multiple targets such as complete and Water resources security
Horizontal and marginal probability level scene with combine, obtain multizone resource allocation proposal, be scientific and reasonable water resource collaboration
Decision provides support.
Multizone water resource supply and demand risk evaluating method provided in this embodiment, by the water resource number for obtaining multizone
According to, and the characteristic indexs such as gross amount of water resources and the water demand amount of multizone water resource are determined according to the Water resources data；
For each region, the marginal probability distribution function of the gross amount of water resources in the region is determined by maximum likelihood estimate, is obtained
Obtain the marginal probability distribution function of the gross amount of water resources in each region；According to the marginal probability of the gross amount of water resources in each region point
Cloth function constructs the joint probability distribution function of the gross amount of water resources in each region by maximum likelihood estimate；By the joint
Probability-distribution function introduces the double-deck Model for Multi-Objective Optimization, constructs the double plating layer analyzed based on Copula, with
Multizone Water Resources Allocation scheme is determined according to the double plating layer based on Copula analysis, water can be provided
Source state between supply and demand is accurately analyzed, and reasonable Water Resources Allocation scheme is provided.
Fig. 3 is the flow diagram for the multizone water resource supply and demand risk evaluating method that further embodiment of this invention provides.
As shown in figure 3, embodiment shown in Fig. 2 and on the basis of, the present embodiment carries out the building process of upper layer object module
It is described in detail, this method comprises:
301, the Water resources data of multizone is obtained, and determines the characterization of multizone water resource according to the Water resources data
Index；The characteristic index includes the gross amount of water resources and water demand amount of each regional water resources.
302, it is directed to each region, the marginal probability of the gross amount of water resources in the region is determined by maximum likelihood estimate
Distribution function obtains the marginal probability distribution function of the gross amount of water resources in each region.
303, according to the marginal probability distribution function of the gross amount of water resources in each region, pass through maximum likelihood estimate structure
Build the joint probability distribution function of the gross amount of water resources in each region.
Step 301 is similar to step 203 with step 201 in above-described embodiment to step 303 in the present embodiment, herein not
It repeats again.
304, using total economic goal of the multizone as first layer target and with the corresponding economic goal in each region
The double-deck multiple target is established using water resources quantity, ecology, society, economic development minimum planning value as constraint condition for second layer target
Optimized model.
By taking Beijing-tianjin-hebei Region as an example, the double-deck Model for Multi-Objective Optimization is described in detail below: total with Beijing-tianjin-hebei Region
Economic goal be first layer target, by three respective odjectives of economic development in area be second layer target, with water resources quantity, life
State, society, economic development minimum planning value are constraint, establish the double-deck Model for Multi-Objective Optimization.Specific steps are as follows:
The risk level in different joint risk level (5%, 10%, 15%) and Jing-jin-ji region two of them region is set
(5%, 10%, 15%) recycles Copula function to acquire trizonal value-at-risk, thus constitutes one group of associated wind
Danger value, subtracting value-at-risk with 1 is exactly to obtain probability value, in marginal distribution function, can obtain water resources quantity according to probability value,
Water resources quantity numerical value is updated to the double-deck Model for Multi-Objective Optimization.
Formula (1)-(4) are the expression formula of double plating layer, and formula (1) indicates the target preferentially met, i.e.,
Jing-jin-ji region overall goal, formula (3) are subgoal, the i.e. target of three regions respectively.Formula (2) and (4) are nonnegativity restrictions.
s.t.gi(x, y)≤0, j=1,2 ..., p；#(2)
s.t.hi(x, y)≤0, j=1,2 ..., q；#(4)
It is economy, Ecology Restriction below:
A1i(t)W1i(t)+a2i(t)W2i(t)+a3i(t)W3i(t)≥Zi(t)#(5)
In formula, Zi(t) the GDP minimum planning value of the area i t is indicated.Constraint condition indicates the production of three regional GDP
Value cannot be below the minimum GDP planning value of this area.
Indicate that water resource total score dosage cannot be greater than aggregate supply SW (t).Wherein three ground of Jing-jin-ji region meets this equation respectively
Beijing Tianjin and Hebei Region integrally also meets this equation.
Three formulas indicate that chance constraint, the probability that the water resource total score dosage in three areas is less than supply amount are greater than 1 and subtract above
Value-at-risk.Wherein each chance constraint does not occur simultaneously, each is only present under corresponding risky situation combination.Pr represents general
Rate value, pu represent the value-at-risk of Beijing, and pv represents the value-at-risk of Tianjin, and pw represents the value-at-risk in Hebei province.
C{1-pu,1-pv}=1-puv#(10)
C{1-pu,1-pw}=1-puw#(11)
C{1-pv,1-pw}=1-pvw#(12)
Three formulas are the Copula function representation of joint two-by-two of chance constraint above.Wherein, puv is the connection of Beijing-Tianjin
Risk is closed, puw is Beijing-Hebei joint risk, and pvw is Tianjin-Hebei joint risk.
In formula, Q (t) indicates grain-production coefficient, this constraint representation grain yield will reach minimum grain planning yield.
This constraint representation wastewater emission amount is less than discharge of wastewater limit.
a2i(t)W2i(t)b2i(t)+a3i(t)W3i(t)b3i(t)≤Z5i(t)#(15)
The discharge of wastewater on this three ground of constraint representation will be respectively smaller than the discharge of wastewater limit of this area.
305, the joint probability distribution function is introduced into the double-deck Model for Multi-Objective Optimization, what building was analyzed based on Copula
Double plating layer, to determine that multizone water provides according to the double plating layer based on Copula analysis
Source allocation plan.
In practical application, the water resources quantity numerical value under the different probability level that Copula function is obtained introduces double-deck more mesh
Optimized model is marked, the double plating layer analyzed based on Copula is constructed.Next with the optimization of multizone water resource
For Research on configuration, comprehensively consider multiple targets such as grain security, energy security and Water resources security, water resource and ecology,
Under the multiple restrictive condition such as society, economy, be arranged different joint probabilities it is horizontal (Jing-jin-ji region totally meets 85%, 90%, 95%,
100% water resources quantity obtains probability) and marginal probability level (Jing-jin-ji region respectively meets 85%, 90%, 95%, 100% water
Stock number obtain probability) scene with combine, obtain multizone resource allocation proposal, be scientific and reasonable water resource Coordination Decision
It provides and supports.
Multizone water resource supply and demand risk evaluating method provided in this embodiment, by constructing the double-deck multiple-objection optimization mould
Type；And the joint probability distribution function is introduced into the double-deck Model for Multi-Objective Optimization, it is more to construct the bilayer analyzed based on Copula
Goal programming Model, to determine multizone Water Resources Allocation according to the double plating layer based on Copula analysis
Scheme can accurately analyze water resource state between supply and demand, provide reasonable Water Resources Allocation scheme.
Fig. 4 is the structural schematic diagram for the multizone water resource supply and demand risk assessment equipment that further embodiment of this invention provides.
As shown in figure 4, the multizone water resource supply and demand risk assessment equipment 40 includes: to obtain module 401, edge function determining module
402, Copula determining module 403 and building module 404.
Module 401 is obtained, determines multizone for obtaining the Water resources data of multizone, and according to the Water resources data
The characteristic index of water resource；The characteristic index includes the gross amount of water resources and water demand amount of each regional water resources；
Edge function determining module 402 determines the region by maximum likelihood estimate for being directed to each region
The marginal probability distribution function of gross amount of water resources obtains the marginal probability distribution function of the gross amount of water resources in each region；
Copula determining module 403, for the marginal probability distribution function according to the gross amount of water resources in each region,
The joint probability distribution function of the gross amount of water resources in each region is constructed by maximum likelihood estimate；
Module 404 is constructed, for the joint probability distribution function to be introduced the double-deck Model for Multi-Objective Optimization, building is based on
The double plating layer of Copula analysis, with true according to the double plating layer based on Copula analysis
Determine multizone Water Resources Allocation scheme.
Multizone water resource supply and demand risk assessment equipment provided in an embodiment of the present invention is obtained more by acquisition module 401
The Water resources data in region, and determine according to the Water resources data gross amount of water resources and water demand of multizone water resource
The characteristic indexs such as amount；Edge function determining module 402 is directed to each region, determines the region by maximum likelihood estimate
The marginal probability distribution function of gross amount of water resources obtains the marginal probability distribution function of the gross amount of water resources in each region；Joint letter
Number determining module 403 passes through maximum likelihood estimate according to the marginal probability distribution function of the gross amount of water resources in each region
Construct the joint probability distribution function of the gross amount of water resources in each region；Building module 404 draws the joint probability distribution function
Enter the double-deck Model for Multi-Objective Optimization, the double plating layer analyzed based on Copula is constructed, to be based on according to
Copula analysis double plating layer determine multizone Water Resources Allocation scheme, can to water resource state between supply and demand into
The accurate analysis of row, provides reasonable Water Resources Allocation scheme.
Fig. 5 is the structural schematic diagram for the multizone water resource supply and demand risk assessment equipment that further embodiment of this invention provides.
As shown in figure 5, the multizone water resource supply and demand risk assessment equipment 40 further include: initial model establishes module 405.
Optionally, the edge function determining module is specifically used for:
The marginal probability distribution letter of multiple types of the gross amount of water resources in the region is determined by maximum likelihood estimate
Number；
It is fitted goodness to the marginal probability distribution function of the multiple type to examine, and true according to the inspection result
The marginal probability distribution type in the fixed region.
Optionally, the marginal probability distribution function of the multiple type comprises at least one of the following: just too distribution pattern side
Edge probability-distribution function, logarithm just too distribution pattern marginal probability distribution function, Weibull distribution type edge probability distribution letter
Number, gamma distribution pattern marginal probability distribution function.
Optionally, the Copula determining module, specifically for the edge according to the gross amount of water resources in each region
Probability-distribution function constructs the joint probability distribution of multiple types of the gross amount of water resources in each region by maximum likelihood estimate
Function；The joint probability distribution function of the multiple type comprises at least one of the following: Gauss Copula function, binary A Ji meter
Moral Copula function；
Goodness is fitted to the joint probability distribution function of the multiple type to examine, and institute is determined according to inspection result
State the joint probability distribution function of multizone water resource.
Optionally, the equipment further include: initial model establishes module 405, for total economic mesh of the multizone
Be designated as first layer target and using the corresponding economic goal in each region as second layer target, with water resources quantity, ecology, society,
Economic development minimum planning value is constraint condition, establishes the double-deck Model for Multi-Objective Optimization.
It is real to can be used for executing above-mentioned method for multizone water resource supply and demand risk assessment equipment provided in an embodiment of the present invention
Example is applied, it is similar that the realization principle and technical effect are similar, and details are not described herein again for the present embodiment.
Fig. 6 is the hardware configuration signal for the multizone water resource supply and demand risk assessment equipment that further embodiment of this invention provides
Figure.As shown in fig. 6, multizone water resource supply and demand risk assessment equipment 60 provided in this embodiment includes: at least one processor
601 and memory 602.The multizone water resource supply and demand risk assessment equipment 60 further includes communication component 603.Wherein, processor
601, memory 602 and communication component 603 are connected by bus 604.
During specific implementation, at least one processor 601 executes the computer execution that the memory 602 stores and refers to
It enables, so that at least one processor 601 executes multizone performed by multizone water resource supply and demand risk assessment equipment 60 as above
Water resource supply and demand risk evaluating method.
When the multizone water resource supply and demand risk evaluating method of the present embodiment is executed by server, the communication component 603
The Water resources data in each region can be sent to server, so that server constructs marginal distribution function and connection according to the data
Close the double-deck Model for Multi-Objective Optimization that distribution function and subsequent construction are analyzed based on Copula.
The specific implementation process of processor 601 can be found in above method embodiment, and it is similar that the realization principle and technical effect are similar,
Details are not described herein again for the present embodiment.
In above-mentioned embodiment shown in fig. 6, it should be appreciated that processor can be central processing unit (English:
Central Processing Unit, referred to as: CPU), can also be other general processors, digital signal processor (English:
Digital Signal Processor, referred to as: DSP), specific integrated circuit (English: Application Specific
Integrated Circuit, referred to as: ASIC) etc..General processor can be microprocessor or the processor is also possible to
Any conventional processor etc..Hardware processor can be embodied directly in conjunction with the step of invention disclosed method to have executed
At, or in processor hardware and software module combination execute completion.
Memory may include high speed RAM memory, it is also possible to and it further include non-volatile memories NVM, for example, at least one
Magnetic disk storage.
Bus can be industry standard architecture (Industry Standard Architecture, ISA) bus, outer
Portion's apparatus interconnection (Peripheral Component, PCI) bus or extended industry-standard architecture (Extended
Industry Standard Architecture, EISA) bus etc..Bus can be divided into address bus, data/address bus, control
Bus etc..For convenient for indicating, the bus in illustrations does not limit only a bus or a type of bus.
The application also provides a kind of computer readable storage medium, and calculating is stored in the computer readable storage medium
Machine executes instruction, and when processor executes the computer executed instructions, realizes multizone water resource supply and demand risk assessment as above
The multizone water resource supply and demand risk evaluating method that equipment executes.
The application also provides a kind of computer readable storage medium, and calculating is stored in the computer readable storage medium
Machine executes instruction, and when processor executes the computer executed instructions, realizes multizone water resource supply and demand risk assessment as above
The multizone water resource supply and demand risk evaluating method that equipment executes.
Above-mentioned computer readable storage medium, above-mentioned readable storage medium storing program for executing can be by any kind of volatibility or non-
Volatile storage devices or their combination realize that, such as static random access memory (SRAM), electrically erasable is only
It reads memory (EEPROM), Erasable Programmable Read Only Memory EPROM (EPROM), programmable read only memory (PROM) is read-only to deposit
Reservoir (ROM), magnetic memory, flash memory, disk or CD.Readable storage medium storing program for executing can be general or specialized computer capacity
Any usable medium enough accessed.
A kind of illustrative readable storage medium storing program for executing is coupled to processor, to enable a processor to from the readable storage medium storing program for executing
Information is read, and information can be written to the readable storage medium storing program for executing.Certainly, readable storage medium storing program for executing is also possible to the composition portion of processor
Point.Processor and readable storage medium storing program for executing can be located at specific integrated circuit (Application Specific Integrated
Circuits, referred to as: ASIC) in.Certainly, processor and readable storage medium storing program for executing can also be used as discrete assembly and be present in equipment
In.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above-mentioned each method embodiment can lead to
The relevant hardware of program instruction is crossed to complete.Program above-mentioned can be stored in a computer readable storage medium.The journey
When being executed, execution includes the steps that above-mentioned each method embodiment to sequence；And storage medium above-mentioned include: ROM, RAM, magnetic disk or
The various media that can store program code such as person's CD.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to
So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into
Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (10)
1. a kind of multizone water resource supply and demand risk evaluating method characterized by comprising
The Water resources data of multizone is obtained, and determines the characteristic index of multizone water resource according to the Water resources data；Institute
State the gross amount of water resources and water demand amount that characteristic index includes each regional water resources；
For each region, the marginal probability distribution letter of the gross amount of water resources in the region is determined by maximum likelihood estimate
Number, obtains the marginal probability distribution function of the gross amount of water resources in each region；
According to the marginal probability distribution function of the gross amount of water resources in each region, each region is constructed by maximum likelihood estimate
Gross amount of water resources joint probability distribution function；
The joint probability distribution function is introduced into the double-deck Model for Multi-Objective Optimization, constructs the more mesh of bilayer analyzed based on Copula
Plan model is marked, to determine multizone Water Resources Allocation side according to the double plating layer based on Copula analysis
Case.
2. the method according to claim 1, wherein described determine the region by maximum likelihood estimate
The marginal probability distribution function of gross amount of water resources, comprising:
The marginal probability distribution function of multiple types of the gross amount of water resources in the region is determined by maximum likelihood estimate；
It is fitted goodness to the marginal probability distribution function of the multiple type to examine, and being determined according to the inspection result should
The marginal probability distribution type in region.
3. according to the method described in claim 2, it is characterized in that, the marginal probability distribution function of the multiple type include with
Lower at least one: just too distribution pattern marginal probability distribution function, logarithm just too distribution pattern marginal probability distribution function, Wei Bu
That distribution pattern marginal probability distribution function, gamma distribution pattern marginal probability distribution function.
4. the method according to claim 1, wherein the edge of the gross amount of water resources according to each region
Probability-distribution function constructs the joint probability distribution function of the gross amount of water resources in each region by maximum likelihood estimate, comprising:
According to the marginal probability distribution function of the gross amount of water resources in each region, each region is constructed by maximum likelihood estimate
Gross amount of water resources multiple types joint probability distribution function；The joint probability distribution function of the multiple type include with
Lower at least one: Gauss Copula function, binary Archimedean Copula function；
Goodness is fitted to the joint probability distribution function of the multiple type to examine, and is determined according to inspection result described more
The joint probability distribution function of regional water resources.
5. method according to claim 1-4, which is characterized in that described to draw the joint probability distribution function
Enter the double-deck Model for Multi-Objective Optimization, before constructing the double plating layer analyzed based on Copula, further includes:
Total economic goal using the multizone is first layer target and using the corresponding economic goal in each region as the second layer
Target establishes the double-deck multiple-objection optimization mould using water resources quantity, ecology, society, economic development minimum planning value as constraint condition
Type.
6. a kind of multizone water resource supply and demand risk assessment equipment characterized by comprising
Module is obtained, determines multizone water resource for obtaining the Water resources data of multizone, and according to the Water resources data
Characteristic index；The characteristic index includes the gross amount of water resources and water demand amount of each regional water resources；
Edge function determining module determines the water resource in the region by maximum likelihood estimate for being directed to each region
The marginal probability distribution function of total amount obtains the marginal probability distribution function of the gross amount of water resources in each region；
Copula determining module, for the marginal probability distribution function according to the gross amount of water resources in each region, by most
The maximum-likelihood estimation technique constructs the joint probability distribution function of the gross amount of water resources in each region；
Module is constructed, for the joint probability distribution function to be introduced the double-deck Model for Multi-Objective Optimization, building is based on Copula
The double plating layer of analysis, to determine multi-region according to the double plating layer based on Copula analysis
Domain Water Resources Allocation scheme.
7. equipment according to claim 6, which is characterized in that the edge function determining module is specifically used for:
The marginal probability distribution function of multiple types of the gross amount of water resources in the region is determined by maximum likelihood estimate；
It is fitted goodness to the marginal probability distribution function of the multiple type to examine, and being determined according to the inspection result should
The marginal probability distribution type in region.
8. equipment according to claim 7, which is characterized in that the marginal probability distribution function of the multiple type include with
Lower at least one: just too distribution pattern marginal probability distribution function, logarithm just too distribution pattern marginal probability distribution function, Wei Bu
That distribution pattern marginal probability distribution function, gamma distribution pattern marginal probability distribution function.
9. a kind of multizone water resource supply and demand risk assessment equipment characterized by comprising at least one processor and storage
Device；
The memory stores computer executed instructions；
At least one described processor executes the computer executed instructions of the memory storage, so that at least one described processing
Device executes such as multizone water resource supply and demand risk evaluating method described in any one of claim 1 to 5.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer in the computer readable storage medium
It executes instruction, when processor executes the computer executed instructions, realizes such as multi-region described in any one of claim 1 to 5
Domain water resource supply and demand risk evaluating method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910547454.3A CN110288149A (en) | 2019-06-24 | 2019-06-24 | Multizone water resource supply and demand risk evaluating method and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910547454.3A CN110288149A (en) | 2019-06-24 | 2019-06-24 | Multizone water resource supply and demand risk evaluating method and equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110288149A true CN110288149A (en) | 2019-09-27 |
Family
ID=68004368
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910547454.3A Pending CN110288149A (en) | 2019-06-24 | 2019-06-24 | Multizone water resource supply and demand risk evaluating method and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110288149A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111539549A (en) * | 2020-03-11 | 2020-08-14 | 水利部交通运输部国家能源局南京水利科学研究院 | Water-energy-grain tie relationship prediction method based on resource life cycle process |
| CN112241607A (en) * | 2020-08-31 | 2021-01-19 | 广东工业大学 | Water resource planning method based on simulation and considering climate change |
| CN113435733A (en) * | 2021-06-23 | 2021-09-24 | 安徽维诚水务科技有限公司 | Water resource risk assessment and prediction system |
| CN114841475A (en) * | 2022-07-01 | 2022-08-02 | 长江水利委员会长江科学院 | Water resource optimization allocation method based on two-dimensional variable random simulation |
| CN115907471A (en) * | 2022-11-22 | 2023-04-04 | 安徽农业大学 | Lake blue-green algae risk diagnosis method based on VineCopula function |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105335603A (en) * | 2015-07-17 | 2016-02-17 | 清华大学 | Method for measuring drought degree of water diversion irrigation region |
-
2019
- 2019-06-24 CN CN201910547454.3A patent/CN110288149A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105335603A (en) * | 2015-07-17 | 2016-02-17 | 清华大学 | Method for measuring drought degree of water diversion irrigation region |
Non-Patent Citations (2)
| Title |
|---|
| 钱龙霞等: "基于Copula函数的水资源供需风险损失模型及其应用", 《系统工程理论与实践》 * |
| 陈义忠等: "基于不确定性的水资源配置双层模型及其实证研究", 《环境科学学报》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111539549A (en) * | 2020-03-11 | 2020-08-14 | 水利部交通运输部国家能源局南京水利科学研究院 | Water-energy-grain tie relationship prediction method based on resource life cycle process |
| CN112241607A (en) * | 2020-08-31 | 2021-01-19 | 广东工业大学 | Water resource planning method based on simulation and considering climate change |
| CN112241607B (en) * | 2020-08-31 | 2023-07-28 | 广东工业大学 | Water resource planning method based on simulation and considering climate change |
| CN113435733A (en) * | 2021-06-23 | 2021-09-24 | 安徽维诚水务科技有限公司 | Water resource risk assessment and prediction system |
| CN114841475A (en) * | 2022-07-01 | 2022-08-02 | 长江水利委员会长江科学院 | Water resource optimization allocation method based on two-dimensional variable random simulation |
| CN115907471A (en) * | 2022-11-22 | 2023-04-04 | 安徽农业大学 | Lake blue-green algae risk diagnosis method based on VineCopula function |
| CN115907471B (en) * | 2022-11-22 | 2023-07-21 | 安徽农业大学 | Lake blue algae risk diagnosis method based on VineCopula function |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110288149A (en) | Multizone water resource supply and demand risk evaluating method and equipment | |
| Wang et al. | The nexus between renewable energy consumption and human development in BRICS countries: The moderating role of public debt | |
| Yang et al. | Assessment of water resources carrying capacity for sustainable development based on a system dynamics model: a case study of Tieling City, China | |
| Latif et al. | Streamflow prediction utilizing deep learning and machine learning algorithms for sustainable water supply management | |
| Allawi et al. | Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region | |
| Baccarini | The maturing concept of estimating project cost contingency: A review | |
| CN107679751A (en) | A kind of appraisal procedure and device | |
| Jebeile et al. | Multi-model ensembles in climate science: Mathematical structures and expert judgements | |
| CN104281523B (en) | A kind of demand Testability Analysis method and system | |
| El-Kholy et al. | Prediction of simulated cost contingency for steel reinforcement in building projects: ANN versus regression-based models | |
| Kurek et al. | The impact of geothermal resources on the competitiveness of municipalities: evidence from Poland | |
| Zhou | Spatial explicit management for the water sustainability of coupled human and natural systems | |
| Xue et al. | Convergence analysis of regional marginal abatement cost of carbon dioxide in China based on spatial panel data models | |
| Wu et al. | Use of a multi-objective correlation index to analyze the power generation, water supply and ecological flow mutual feedback relationship of a reservoir | |
| Depren et al. | Effect of political stability, geopolitical risk and R&D investments on environmental sustainability: Evidence from European countries by novel quantile models | |
| Maksyshko et al. | Method of investment projects evaluation for territorial communities taking into account the concept of sustainable development | |
| Zhang et al. | A European call options pricing model using the infinite pure jump levy process in a fuzzy environment | |
| Konior et al. | Course of planned, actual and earned cost curves of diverse construction investments | |
| Brzezicka et al. | The applicability of the speculative frame method for detecting disturbances on the real estate market: evidence from Poland | |
| CN114493037A (en) | Automatic water balance test prediction method and system | |
| Avenali et al. | Heterogeneity of national accounting systems, world-class universities and financial resources: What are the links? | |
| Ng et al. | Relationships between interdependency, reliability, and vulnerability of infrastructure systems: Case study of biofuel infrastructure development | |
| CN109858699B (en) | Water quality quantitative simulation method and device, electronic equipment and storage medium | |
| Scheinert et al. | Growing collaborations: Forecasting changes in partnership networks using a bottom-up approach | |
| Jiang et al. | Comprehensive Evaluation of Runoff Forecast Level Based on Forecasting Difficulty |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190927 |