CN112084608A - Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model - Google Patents

Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model Download PDF

Info

Publication number
CN112084608A
CN112084608A CN202010742654.7A CN202010742654A CN112084608A CN 112084608 A CN112084608 A CN 112084608A CN 202010742654 A CN202010742654 A CN 202010742654A CN 112084608 A CN112084608 A CN 112084608A
Authority
CN
China
Prior art keywords
node
pipeline
pipe
model
uncertainty
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.)
Granted
Application number
CN202010742654.7A
Other languages
Chinese (zh)
Other versions
CN112084608B (en
Inventor
吴珊
马晴晴
侯本伟
程玉林
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing University of Technology
Original Assignee
Beijing University of Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing University of Technology filed Critical Beijing University of Technology
Priority to CN202010742654.7A priority Critical patent/CN112084608B/en
Publication of CN112084608A publication Critical patent/CN112084608A/en
Application granted granted Critical
Publication of CN112084608B publication Critical patent/CN112084608B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/02—CAD in a network environment, e.g. collaborative CAD or distributed simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/14—Pipes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • 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)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Geometry (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Health & Medical Sciences (AREA)
  • General Business, Economics & Management (AREA)
  • Quality & Reliability (AREA)
  • Computational Mathematics (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Water Supply & Treatment (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Primary Health Care (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Complex Calculations (AREA)

Abstract

A method for identifying risk pipelines and nodes by adopting a parameter uncertainty analysis model belongs to the technical field of drainage pipe network analysis. And solving the failure probability of the nodes and the pipeline by adopting an SWMM model solver according to a Monte Carlo random simulation principle, and expressing the risks of the pipeline and the nodes according to the failure probability, thereby providing theoretical guidance for the pipe network risk assessment work under the conditions of uncertain parameters and no monitoring data.

Description

Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model
Technical Field
The invention relates to a method for identifying high-risk pipelines and nodes by establishing an analysis model considering model parameter uncertainty aiming at the requirement of functional risk evaluation of a municipal drainage pipe network. Belongs to the technical field of drainage pipe network analysis.
Background
The drainage pipe network model is a reasonable generalization of an actual drainage pipe network system according to the basic rules of urban surface runoff production, runoff and pipe network confluence. By simulating the pipe network state under various working condition situations, mastering the drainage and operation rules of the pipe network and combining the effect analysis of each pipe section and each node in the pipe network, a scientific decision basis can be provided for planning, designing, operating management and updating and transforming schemes of the drainage pipe network.
Because the drainage model has uncertainty of parameter values during construction, when the drainage model is applied to evaluate the functional state of the urban drainage pipe network, the model parameters are often firstly calibrated to ensure the accuracy of simulation evaluation. The most common way is to rate the model parameters using data from monitoring points in the actual pipe network. However, the method needs to provide data of relatively dense monitoring points, but the density of most urban drainage monitoring points in China at present hardly meets the requirements, and even some cities or regions do not have monitoring data, so that a method for ensuring that the simulation precision of a drainage model meets the use requirements under the condition that the monitoring data required by model calibration is not available needs to be established, and therefore, the purpose of analyzing dangerous pipelines and nodes in a pipeline network by using the model is achieved, and a pipeline updating and transforming scheme is optimized.
Disclosure of Invention
Aiming at the condition of no monitoring data, the invention adopts a model to identify the risk problem in a drainage pipe network and provides a drainage pipe network simulation and evaluation method considering model parameter uncertainty. And solving the failure probability of the nodes and the pipeline by adopting an SWMM model solver according to a Monte Carlo random simulation principle, and expressing the risks of the pipeline and the nodes according to the failure probability, thereby providing theoretical guidance for the pipe network risk assessment work under the conditions of uncertain parameters and no monitoring data.
The technical scheme is as follows: for a drainage network model containing n uncertainty parameters, the uncertainty parameters are considered as random variables X ═ X1, X2, …, Xn, subject to a certain probability distribution, and the random variable parameters are assumed to be independent of each other. The steps of using a model solver of SWMM and adopting Monte Carlo simulation sampling calculation are as follows:
the method comprises the following steps: and (4) selecting uncertainty parameters. And selecting n parameters which have large influence on the number of overflow nodes and full pipelines as uncertainty analysis parameters.
Step two: and (4) uncertainty parameter values and obedience distribution type determination. And determining the value range of the uncertainty model parameters and the obedience probability distribution type by referring to relevant specifications, manuals, documents and other data.
Step three: the settings are calculated. The accuracy requirement of the Monte Carlo simulation times N or the simulation estimation value variation coefficient is automatically determined according to the requirement of the simulation accuracynode]And 2pipe]The value of (c).
Step four: and (4) a calculation method and definition of failure probability and a structural function thereof.
Under the working condition of considering the uncertainty parameters, the calculation principles of the node and pipeline failure probability are expressed as formulas (1) and (2).
Figure BDA0002599848880000011
Figure BDA0002599848880000012
In the formula, Pf,nodeIs the node failure probability; pf,pipeIs the pipeline failure probability; (x) is a joint probability density function of the random variable vectors; for the function gnode(X) and gpipe(X), respectively defining node overflow and pipeline full pipe as node and pipeline failure modes, and expressing as follows:
gnode(X)＝W0-W(X) (3)
gpipe(X)＝D0-D(X) (4)
where X is a set of random variables of uncertainty parameters that affect the state of the model (system), W0The well depth of the inspection well corresponding to the model node is W (X), and the maximum water depth of the node is calculated by the model; d0The diameter of the model pipeline is shown, and D (X) is the maximum water level of the pipeline relative to the bottom of the pipe calculated by the model.
Therefore, in the formula (1), gnode(X) 0 or lessThe maximum water depth W (X) of the node exceeds the well depth W of the corresponding inspection well0The node overflows, namely the node fails; gpipe(X) 0 or less represents that the maximum water depth D (X) of the pipeline relative to the bottom of the pipeline exceeds the diameter D of the corresponding pipeline0The pipe is full, i.e. the pipe fails.
And step five, solving the failure probability of the nodes and the pipelines by a Monte Carlo random sampling method.
Since the maximum water depth w (X) of the node and the maximum water level d (X) of the pipeline in the formulas (3) and (4) are nonlinear functions related to X, and when the number of variables in X is large, it is very difficult to directly solve the failure probability of the node and the pipeline by using the formula (1), the Monte Carlo method is a common approximate solution for describing and estimating function statistics, and an approximate solution with higher precision can be obtained for a highly nonlinear function. The solving steps are as follows:
respectively carrying out random sampling of kth simulation on n uncertainty parameters, wherein each simulation randomly takes one value from a corresponding range according to the probability distribution type obeyed by each uncertainty parameter, and respectively generating n random numbers X(k)＝(x1k,x2k,…,xnk) Writing the random numbers as model parameter values of the current sampling into a model inp file, calling a swmm5.dll model solving function library to perform model calculation, and reading the calculation results of node overflow and pipeline fullness in an rpt report file;
and secondly, simulating estimation value statistics.
Calculating node and pipeline failure probability estimated values corresponding to the current result (previous k times of simulation) after the k time of simulation according to the formulas (5) and (6)
Figure BDA0002599848880000021
And
Figure BDA0002599848880000022
and calculating the coefficient of variation of the simulated estimation value according to the equations (7) and (8)k,nodeAndk,pipe；
Figure BDA0002599848880000023
Figure BDA0002599848880000024
where k is the number of analog samples, X(i)Sampling value of random variable X obtained for the ith analog sampling; i (g)node(X(i)) Less than or equal to 0) is an indicative function of {0,1} binary value, when g isnode(X(i)) When the ratio is less than or equal to 0, I (g)node(X(i))≤0)＝1；gnode(X(i))＞0，I(gnode(X(i))＞0)＝0。I(gpipe(X(i)) Not more than 0) are the same;
Figure BDA0002599848880000025
Figure BDA0002599848880000031
in the formula
Figure BDA0002599848880000032
And
Figure BDA0002599848880000033
is the variance and expectation sign in mathematics;
judging convergence termination; when the condition simulation times k is N or the condition is satisfiedk,node≤[node]Andk,pipe≤[pipe]when so, terminating the sampling; otherwise, turning to the first step;
step six: according to the failure probabilities obtained by the formulas (5) and (6) for the pipes, a pipe and a node with a failure probability of 1 are defined as high risk, a pipe and a node between 0.5 and 1 are defined as medium risk, a pipe and a node between 0 and 0.5 are defined as low risk, and a pipe and a node with a failure probability of 0 are defined as no risk.
Table 1 shows uncertainty parameters and their value ranges and distribution types;
Figure BDA0002599848880000034
drawings
FIG. 1 is a flow of Monte Carlo stochastic simulation to solve node and pipeline failure probabilities.
Figure 2 embodiment of the research area drainage network
FIG. 3 example Pf,nodeConvergence of value
FIG. 4 is a graph of node and pipe failure probability distribution in the embodiment;
a node failure probability distribution; b, distribution of pipeline failure probability.
Detailed Description
The present invention will be further illustrated with reference to the following examples, but the present invention is not limited to the following examples.
Example 1
Taking a drainage pipe network (figure 2) of a certain city research area as an example, the total area of the research area is 3.1km2The design standard of the rainwater pipe network is once in 3-5 years, 764 inspection wells, 766 pipelines (with the total length of 22.36km) and 7 water outlets. The river flood control design standard reappearance period is 50-100 years, and the water outlet is set to freely flow out. The parameters shown in attached table 1 are used as uncertainty parameters of the model of the present case, and the method is applied to the identification of the risk pipelines and nodes in the region.
The simulation times N of Monte Carlo are set to 5000, and all Monte Carlo simulation calculation results in the statistical model are converged
Figure BDA0002599848880000041
And
Figure BDA0002599848880000042
coefficient of variation of (2)k,nodeAndk,pipethe maximum values are {0.057,0.047}, respectively, which shows that the Monte Carlo simulation result has better convergence. FIG. 3 shows that 3 optional nodes are respectively paired in the rainfall recurrence period of 3,5,10 and 20 yearsAnd (3) carrying out 5000 times of simulation of value selection on the n uncertain parameters respectively, marking the change condition of the node failure probability estimated value along with the simulation times, marking the corresponding coefficient of variation value, basically flattening the curve change, and considering that the simulation result is converged.
Under the condition that the designed rainfall recurrence periods are 3 years, 5 years, 10 years and 20 years respectively, the failure probability of the pipelines and the nodes is counted, and the failure probability is shown in figure 4. The result statistics show that: the rainfall intensity (the recurrence period) increases, the number of the pipelines and the nodes with the failure probability of 1 increases, and the number of the nodes and the pipelines with the failure probability of 0 decreases. Under different reappearance period conditions, the failure probability of the pipelines with the percentage of { 79.68%, 67.17%, 35.9 and 30.88% } and the nodes with the percentage of { 92.15%, 87.04%, 63.87% and 44.24% } is 0, which means that in the uncertainty parameter value range, the parameters take any value, and the pipelines and the nodes cannot be full pipes or overflow and are corresponding risk-free pipelines and nodes in the reappearance period. Under different reappearance period conditions, the failure probability of a pipeline with { 7.94%, 16.49%, 29.77%, 55.59% } and a node with { 0.65%, 1.44%, 3.4%, 8.38% } is 1. This means that when the model parameter uncertainty is considered for simulation, no matter what value the uncertainty parameter takes in the value range, the pipelines and nodes will be full of pipes or overflow; corresponding to high risk pipes and nodes in the recurrence period. For pipelines and nodes with failure probability between 0 and 1, the possibility of node overflow and pipeline full exists, the risk degree is judged according to the failure probability, wherein the risk degree is low from 0 to 0.5 and medium from 0.5 to 1.

Claims (1)

1. A method for identifying risk pipelines and nodes by adopting a parameter uncertainty analysis model is characterized by comprising the following steps:
for a drainage network model containing n uncertainty parameters, the uncertainty parameters are considered as random variables X ═ X1, X2, …, Xn, subject to a certain probability distribution, and the random variable parameters are assumed to be independent of each other. The steps of using a model solver of SWMM and adopting Monte Carlo simulation sampling calculation are as follows:
the method comprises the following steps: selecting uncertainty parameters; and selecting n parameters which have large influence on the number of overflow nodes and full pipelines as uncertainty analysis parameters.
Step two: and (4) uncertainty parameter values and obedience distribution type determination. Referring to relevant specifications, manuals, documents and other data, determining the value range of the uncertainty model parameters and the obedience probability distribution type of the uncertainty model parameters;
step three: calculating and setting; the accuracy requirement of the Monte Carlo simulation times N or the simulation estimation value variation coefficient is automatically determined according to the requirement of the simulation accuracynode]And 2pipe]A value of (d);
step four: calculating and defining failure probability and a structure function thereof;
under the working condition of considering uncertainty parameters, the calculation principles of the node and pipeline failure probability are expressed as formulas (1) and (2);
Figure FDA0002599848870000011
Figure FDA0002599848870000012
in the formula, Pf,nodeIs the node failure probability; pf,pipeIs the pipeline failure probability; (x) is a joint probability density function of the random variable vectors; for the function gnode(X) and gpipe(X), respectively defining node overflow and pipeline full pipe as node and pipeline failure modes, and expressing as follows:
gnode(X)＝W0-W(X) (3)
gpipe(X)＝D0-D(X) (4)
where X is a set of random variables of uncertainty parameters that affect the state of the model (system), W0The well depth of the inspection well corresponding to the model node is W (X), and the maximum water depth of the node is calculated by the model; d0The diameter of the model pipeline is obtained, and D (X) is the maximum pipeline water level relative to the bottom of the pipe obtained by model calculation;
therefore, in the formula (1), gnode(X) is less than or equal to 0, the maximum water depth W (X) of the node exceeds the well depth W of the corresponding inspection well0The node overflows, namely the node fails; gpipe(X) 0 or less represents that the maximum water depth D (X) of the pipeline relative to the bottom of the pipeline exceeds the diameter D of the corresponding pipeline0The pipeline is full flow, namely the pipeline is failed;
solving the failure probability of the nodes and the pipelines by a Monte Carlo random sampling method;
because the maximum water depth W (X) of the node and the maximum water level D (X) of the pipeline in the formulas (3) and (4) are nonlinear functions about X, and when the number of variables in X is large, it is very difficult to directly solve the failure probability of the node and the pipeline by adopting the formula (1), the Monte Carlo method is a common approximate solution for describing and estimating function statistics, and for a highly nonlinear function, an approximate solution with higher precision can be obtained; the solving steps are as follows:
respectively carrying out random sampling of kth simulation on n uncertainty parameters, wherein each simulation randomly takes one value from a corresponding range according to the probability distribution type obeyed by each uncertainty parameter, and respectively generating n random numbers X(k)＝(x1k,x2k,…,xnk) Writing the random numbers as model parameter values of the current sampling into a model inp file, calling a swmm5.dll model solving function library to perform model calculation, and reading the calculation results of node overflow and pipeline fullness in an rpt report file;
simulation estimation statistics
Calculating node and pipeline failure probability estimated values corresponding to the current result (previous k times of simulation) after the k time of simulation according to the formulas (5) and (6)
Figure FDA0002599848870000021
And
Figure FDA0002599848870000022
and calculating the coefficient of variation of the simulated estimation value according to the equations (7) and (8)k,nodeAndk,pipe；
Figure FDA0002599848870000023
Figure FDA0002599848870000024
where k is the number of analog samples, X(i)Sampling value of random variable X obtained for the ith analog sampling; i (g)node(X(i)) Less than or equal to 0) is an indicative function of {0,1} binary value, when g isnode(X(i)) When the ratio is less than or equal to 0, I (g)node(X(i))≤0)＝1；gnode(X(i))＞0，I(gnode(X(i))＞0)＝0。I(gpipe(X(i)) Not more than 0) are the same;
Figure FDA0002599848870000025
Figure FDA0002599848870000026
in the formula
Figure FDA0002599848870000027
And
Figure FDA0002599848870000028
is the variance and expectation sign in mathematics;
judging convergence termination; when the condition simulation times k is N or the condition is satisfiedk,node≤[node]Andk,pipe≤[pipe]when so, terminating the sampling; otherwise, turning to the first step;
step six: according to the failure probabilities obtained by the formulas (5) and (6) for the pipes, a pipe and a node with a failure probability of 1 are defined as high risk, a pipe and a node between 0.5 and 1 are defined as medium risk, a pipe and a node between 0 and 0.5 are defined as low risk, and a pipe and a node with a failure probability of 0 are defined as no risk.
CN202010742654.7A 2020-07-24 2020-07-24 Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model Active CN112084608B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010742654.7A CN112084608B (en) 2020-07-24 2020-07-24 Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010742654.7A CN112084608B (en) 2020-07-24 2020-07-24 Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model

Publications (2)

Publication Number Publication Date
CN112084608A true CN112084608A (en) 2020-12-15
CN112084608B CN112084608B (en) 2023-12-19

Family

ID=73735999

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010742654.7A Active CN112084608B (en) 2020-07-24 2020-07-24 Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model

Country Status (1)

Country Link
CN (1) CN112084608B (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112613750A (en) * 2020-12-25 2021-04-06 中建三局绿色产业投资有限公司 Sedimentation risk assessment method for deep sewage conveying tunnel
CN113505449A (en) * 2021-06-18 2021-10-15 中国石油大学(华东) Random analysis method for predicting failure load of composite material flexible pipe
CN113836710A (en) * 2021-09-15 2021-12-24 西安理工大学 Method for quickly tracking external water invasion of drainage pipe network
WO2023284060A1 (en) * 2021-07-12 2023-01-19 浙江大学 Geographic three-dimensional information-based method for analyzing uncertainty of flow rate of sewage pipe network

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102096740A (en) * 2011-02-15 2011-06-15 上海海事大学 Bayesian network-based urban storm water pipe network reliability evolution modeling method
CN102968529A (en) * 2012-11-14 2013-03-13 上海城市水资源开发利用国家工程中心有限公司 Method for quantifying computed result non-determinacy interval of water supply pipe network model
US20140163916A1 (en) * 2012-12-10 2014-06-12 International Business Machines Corporation Techniques for Iterative Reduction of Uncertainty in Water Distribution Networks
CN107038278A (en) * 2017-02-27 2017-08-11 同济大学 Parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102096740A (en) * 2011-02-15 2011-06-15 上海海事大学 Bayesian network-based urban storm water pipe network reliability evolution modeling method
CN102968529A (en) * 2012-11-14 2013-03-13 上海城市水资源开发利用国家工程中心有限公司 Method for quantifying computed result non-determinacy interval of water supply pipe network model
US20140163916A1 (en) * 2012-12-10 2014-06-12 International Business Machines Corporation Techniques for Iterative Reduction of Uncertainty in Water Distribution Networks
CN107038278A (en) * 2017-02-27 2017-08-11 同济大学 Parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
李芊;张明媛;袁永博;: "SWMM和蒙特卡罗法的雨水管网节点可靠性分析", 辽宁工程技术大学学报(自然科学版), no. 11 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112613750A (en) * 2020-12-25 2021-04-06 中建三局绿色产业投资有限公司 Sedimentation risk assessment method for deep sewage conveying tunnel
CN112613750B (en) * 2020-12-25 2022-08-09 中建三局绿色产业投资有限公司 Sedimentation risk assessment method for deep sewage conveying tunnel
CN113505449A (en) * 2021-06-18 2021-10-15 中国石油大学(华东) Random analysis method for predicting failure load of composite material flexible pipe
WO2023284060A1 (en) * 2021-07-12 2023-01-19 浙江大学 Geographic three-dimensional information-based method for analyzing uncertainty of flow rate of sewage pipe network
CN113836710A (en) * 2021-09-15 2021-12-24 西安理工大学 Method for quickly tracking external water invasion of drainage pipe network

Also Published As

Publication number Publication date
CN112084608B (en) 2023-12-19

Similar Documents

Publication Publication Date Title
CN112084608B (en) Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model
Chen et al. Copulas and its application in hydrology and water resources
Brunner et al. Bivariate return periods and their importance for flood peak and volume estimation
Dupuis Exceedances over high thresholds: A guide to threshold selection
Guven et al. New approach for stage–discharge relationship: gene-expression programming
Abdi et al. Regional bivariate modeling of droughts using L-comoments and copulas
Vojinovic et al. Multi-objective rehabilitation of urban drainage systems under uncertainties
Song et al. Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach
Bagarello et al. Establishing a soil loss threshold for limiting rilling
Aydoğan et al. Regional flood frequency analysis for Ç oruh B asin of T urkey with L‐moments approach
Abdollahi et al. Probabilistic event based rainfall-runoff modeling using copula functions
Zamani et al. Extreme streamflow drought in the Karkheh river basin (Iran): probabilistic and regional analyses
Ghimire et al. Development of stage-discharge rating curve in river using genetic algorithms and model tree
Soroush et al. Optimal selection of number and location of pressure sensors in water distribution systems using geostatistical tools coupled with genetic algorithm
Lund et al. Assimilating flow and level data into an urban drainage surrogate model for forecasting flows and overflows
Tu et al. Bivariate design of hydrological droughts and their alterations under a changing environment
Yazdi et al. A stochastic optimization algorithm for optimizing flood risk management measures including rainfall uncertainties and nonphysical flood damages
Gumiere et al. Evaluating the impact of the spatial distribution of land management practices on water erosion: case study of a Mediterranean catchment
Tung Uncertainty and reliability analysis in water resources engineering
Mirzaei et al. Model calibration and uncertainty analysis of runoff in the Zayanderood River basin using generalized likelihood uncertainty estimation (GLUE) method
Mohtashami et al. Numerical simulation of groundwater in an unconfined aquifer with a novel hybrid model (case study: Birjand Aquifer, Iran)
Othman et al. Development of stage-distance-discharge relationship and rating curve using least square method
Zhou et al. Conceptual planning approach of low impact development s for combined water quality‐quantity control at an urban scale: A case study in Southern China
Chilkoti et al. Diagnostic evaluation of hydrologic models employing flow duration curve
Wu et al. Local and regional flood frequency analysis based on hierarchical Bayesian model: application to annual maximum streamflow for the Huaihe River basin

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