CN107038278A - Parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions - Google Patents
Parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions Download PDFInfo
- Publication number
- CN107038278A CN107038278A CN201710107361.XA CN201710107361A CN107038278A CN 107038278 A CN107038278 A CN 107038278A CN 201710107361 A CN201710107361 A CN 201710107361A CN 107038278 A CN107038278 A CN 107038278A
- Authority
- CN
- China
- Prior art keywords
- parameter
- swmm
- matlab
- data
- formatted files
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
Abstract
The present invention relates to a kind of parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions, including step：S1：EFAST analysis methods are simulated by Matlab, input sample of data is passed to the inp formatted files needed for SWMM；S2：Called using SWMM computation engine and extract the information in inp formatted files and carry out computing, and operation result is respectively stored in rpt formatted files and out formatted files；S3：Called by Matlab and extract in rpt formatted files corresponding output information and generate corresponding output data sample；S4：Input sample of data and output data sample are calculated to the one order factor of relevant parameter respectively by eFAST sensitivity analysis algorithms respectively；S5：The big corresponding condition of parameter of the one order factor is optimized according to the one order factor optimizing specific aim of acquisition.Compared with prior art, the present invention realizes eFAST algorithms using Matlab, can accelerate model optimization progress.
Description
Technical field
The present invention relates to a kind of city surface source pollution control field, more particularly, to one kind based on SWMM and MATLAB data
Interactive parametric sensitivity optimization method.
Background technology
In order to improve the parameter calibration efficiency of SWMM models, lot of domestic and foreign scientific research and engineering staff propose that various methods are entered
Row SWMM analysis of model parameters sensitivity, including：Pair of object function is used as using height of run-off, crest discharge and discharge curve
Production in SWMM models is confluxed and pipe network is migrated, and module carries out the Local sensitivity analysis method of Parameter Sensitivity Analysis；With footpath
Fluid is accumulated, as object function the time required to peak flow and peak value, using Local sensitivity analysis method to impervious surface
Product ratio, peak width, ground low-lying area store depth (permeable and waterproof region), pipeline Manning coefficient and permeable and impervious zone
Manning roughness carry out sensitivity analysis；Method based on the Monte-Carlo method of samplings and sensitivity analysis (RSA)
Determine the sensitivity of SWMM model parameters；Using Morris methods production flow module respectively to SWMM models and hydrology hydraulics ginseng
Number sensitivity is analyzed, and the sensitivity of the parameter of influence height of run-off and peak flow is considered respectively；With height of run-off, flood
Peak flow and flood peak time are as the Stepwise Regression Method based on the Monte Carlo method of samplings of output parameter to model
Hydrology Hydrodynamic Parameters carry out sensitivity analysis.
But these above-mentioned methods do not consider that influence of the coupling to analog result between parameter adds model parameter
Selected and input parameter sample number etc. limitation, reduce the representativeness of sensitivity analysis result.Therefore we select more
Suitable method --- eFAST carries out more comprehensive system to the model sensitivity of the input parameter of SWMM models and scientifically divided
Analysis.This method is a kind of parameter quantitative Sensitivity Analysis Method based on variance analysis, is combined by Sailtelli etc.
A kind of Global sensitivity analysis method that the advantage of Sobol methods and Fourier modulus sensitivity test method is proposed, with sane, meter
Calculate the advantages of efficient and required sample number is relatively low.But, although eFAST methods are less compared to sample needed for other method, root
65*K are found when it carries out parameter sample size required during sensitivity analysis at least according to the study, and K is input parameter number, but
SWMM is allowed for as the complex model of multi-parameter, its calculating is not still can be by artificial light tractable.Need to seek
A kind of alternative is looked for improve efficiency, the reduction analytical error of sensitivity analysis.
The content of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind based on SWMM with
The parametric sensitivity optimization method of MATLAB data interactions.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions, including step：
S1：EFAST analysis methods are simulated by Matlab, input sample of data is passed to the inp forms needed for SWMM
File；
S2：Called using SWMM computation engine and extract the information in inp formatted files and carry out computing, and by computing knot
Fruit is respectively stored in rpt formatted files and out formatted files；
S3：Called by Matlab and extract corresponding output information and the corresponding output number of generation in rpt formatted files
According to sample；
S4：Input sample of data and output data sample are calculated into phase respectively by eFAST sensitivity analysis algorithms respectively
Answer the one order factor of parameter；
S5：The big parameter of the one order factor is optimized according to the one order factor optimizing specific aim of acquisition corresponding
Condition.
The step S1 specifically includes step：
S11：Run Matlab, the input sample of data of the parameter according to needed for eFAST analysis methods；
S12：The input sample of data of generation is passed to the inp formatted files needed for SWMM.
The step S4 is specifically included：
S41：Build multidimensional function model Y=f (X), wherein X (x1,x2,…,xn) it is input parameter；
S42：Function G is searched in settingi, and use search function by multidimensional function model conversion for one-dimensional functions model Y=f
(s)：
Gi=(sin ωiS), i=1,2 ..., n
Wherein：I is parameter xiParameter sequence number, ωiFor parameter xiInteger frequency, s is real number；
S43：Fourier transformation is carried out to one-dimensional functions model：
Wherein：P is Fourier's factor, p ∈ Z, Ap,BpFor Fourier's amplitude, and：
S44：Calculate the variance as caused by each parameter and model population variance：
Wherein：ViFor parameter xiCaused variance, V is model population variance, ΛjFor the frequency spectrum of Fourier space, j ∈ Z；
S45：According to acquisition variance as caused by each parameter and model population variance, the one order of each parameter is obtained
The factor：
Wherein：SiFor parameter xiThe one order factor.
Fourier space is specially：
Wherein：ΛpFor the frequency spectrum of Fourier space.
The input parameter at least includes peak width, regional slope, region Impervious surface coverage, the water storage depth in water penetration region
Degree, the water storage depth in waterproof region, waterproof bent Manning coefficient, permeable area's Manning coefficient and impervious zone Wu Wa store ground skill
Ratio.
Compared with prior art, the present invention has advantages below：
1) eFAST methods are a kind of parameter quantitative Sensitivity Analysis Methods based on variance analysis, are by Sailtelli etc.
Combine a kind of Global sensitivity analysis method of the advantage proposition of Sobol methods and Fourier modulus sensitivity test method, the party
Method is sane, it is relatively low to calculate efficient and required sample number.
2) eFAST Sensitivity Analysis Methods belong to Global sensitivity analysis method, sensitive different from currently conventional part
Analysis method and Morris methods are spent, this method considers the complexity of SWMM models and the coupling of different parameters to defeated
Go out the influence of result, that is, consider sensitivity of the interaction between parameter for model output result.
3) using data processing function powerful Matlab, a large amount of calculating that eFAST algorithms are related to are realized, it is quick, accurate
The clear and definite SWMM model parameters sensitivity situation in ground, is searched out on modeling influence some parameters the most significant, so as to model
Efficiency is improved during calibration, increases the modeling degree of accuracy.
Brief description of the drawings
Fig. 1 is main method schematic flow sheet of the invention.
Embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, give detailed embodiment and specific operating process, but protection scope of the present invention is not limited to
Following embodiments.
It can improve and SWMM model parameters are carried out present invention aims at offer one kind with eFAST Sensitivity Analysis Methods
The operating efficiency of sensitivity sequence, the method for error during reduction processing mass data.Method has efficiently, accurately, time saving province
The advantage of power.
By mathematical analysis software Matlab programming softwares, programming realization Matlab solve eFAST algorithms computational problem with
And the data interaction between Matlab and SWMM softwares, the rapid sensitive degree analysis of SWMM model parameters is completed, is finally relied on
The result arrived carries out the rapid Optimum of model, targetedly controls particular variables to reach optimization aim.
A kind of parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions, including step：
S1：EFAST analysis methods are simulated by Matlab, input sample of data is passed to the inp forms needed for SWMM
File, specifically includes step：
S11：Run Matlab, the input sample of data of the parameter according to needed for eFAST analysis methods；
S12：The input sample of data of generation is passed to the inp formatted files needed for SWMM.
S2：Called using SWMM computation engine and extract the information in inp formatted files and carry out computing, and by computing knot
Fruit is respectively stored in rpt formatted files and out formatted files；
S3：Called by Matlab and extract corresponding output information and the corresponding output number of generation in rpt formatted files
According to sample；
S4：Input sample of data and output data sample are calculated into phase respectively by eFAST sensitivity analysis algorithms respectively
The one order factor of parameter is answered, step is specifically included：
S41：Build multidimensional function model Y=f (X), wherein X (x1,x2,…,xn) it is input parameter, input parameter is at least
Including peak width, regional slope, region Impervious surface coverage, the water storage depth in water penetration region, the water storage depth in waterproof region,
Waterproof bent Manning coefficient, permeable area's Manning coefficient and impervious zone Wu Wa store ground skill ratio, and each parameter has specific change
Scope and distribution form, it is generally parameter space R to constitute onen；
S42：Function G is searched in settingi, and use search function by multidimensional function model conversion for one-dimensional functions model Y=f
(s)：
Gi=(sin ωiS), i=1,2 ..., n
Wherein：I is parameter xiParameter sequence number, ωiFor parameter xiInteger frequency, s is real number；
S43：Fourier transformation is carried out to one-dimensional functions model：
Wherein：P is Fourier's factor, p ∈ Z, Ap,BpFor Fourier's amplitude, and：
S44：Calculate the variance as caused by each parameter and model population variance：
Wherein：ViFor parameter xiCaused variance, V is model population variance, ΛjFor the frequency spectrum of Fourier space, j ∈ Z；
The spectrum curve of Fourier space is specially：
Wherein：ΛpFor Fourier space.
According to the characteristic of Fourier transformation, Ap=A-p, Bp=B-p, Λp=Λ-p, then by parameter xiInput change drawn
The variance V of the model result riseniIt can be expressed as：
Wherein：Z0For Z deleted neighbourhood.Then the population variance V computational methods of model are as follows：
S is equidistantly sampled between interval [- π ,+π], sampling value is converted into each parameter by searching function
Value, after model is run multiple times, A can be obtained respectively input modelp,BpApproximation：
Wherein：
NsFor sampling number.By ApAnd Bp, and parameter xiCorresponding frequencies omegaiPass through variance and mould caused by each parameter
The calculating formula of type population variance can obtain the variance V of the model output caused by parametersiAnd the population variance of model output
V.Because the population variance that model is exported is obtained jointly by parameters and interaction among parameters, population variance V can be decomposed into
Following form：
V=∑siVi+∑i≠jVij+∑i≠j≠mVijm+…+V1,2,…,n
Wherein：VijFor parameter xiBy with parameter xjInteraction contribution variance, VijmFor parameter xiWith xjAnd xm's
The total variance with contribution of coupling, V11...kFor xiWith x1, x2..., xkThe variance of collective effect contribution.It therefore, it can obtain respectively
Parameter xiSingle order sensitive factor SiFor：
Wherein：VijFor parameter xiBy with parameter xiInteraction contribution variance, VijmFor parameter xiWith xjAnd xm's
The total variance with contribution of coupling, V1,2,…,nFor xiWith except xiThe variance that all parameter collective effects are contributed in addition.It therefore, it can point
Huo get not parameter xiSingle order sensitive factor.
S45：According to acquisition variance as caused by each parameter and model population variance, the one order of each parameter is obtained
The factor：
Wherein：SiFor parameter xiThe one order factor.
The one order factor reflects the individual contributions rate that parameter exports population variance for model.Similarly, it can obtain
The second order of parameter and each rank sensitivity factor, are calculated as follows：
The total sensitivity factor of parameter is each rank sensitivity factor sum of parameter, and calculation is as follows：
STi=Si+Sij+Sijm+…+S1,2,…,n
The total sensitivity factor of parameter represents parameter with other all phase interactions having between interactive parameter
With the contribution sum for exporting population variance.
S5：The big parameter of the one order factor is optimized according to the one order factor optimizing specific aim of acquisition corresponding
Condition, improves model optimization efficiency, so as to accelerate model optimization speed.
The main difficult technical of application scheme has two：One is the Matlab problems of implementation of eFAST algorithms, and two be Matlab
The realization of data interaction between SWMM softwares.
Achievement according to previous studies, on the basis of eFAST algorithms, by being modified to its some algorithm or again
It is written as being adapted to Matlab algorithm；In Matlab and SWMM hybrid programming, SWMM dynamic number is called by Matlab
Calling for SWMM is realized according to linked database file, needs to provide the document format data needed for operation when SWMM is called.
Urban Storm Flood simulation softward SWMM has its independent input file form * .inp, wherein containing model running institute
The parameters value and the spatial data of modeling needed, produces the output file * of other two kinds of forms automatically during model running
.inp with * .out, SWMM analysis report file and operation result file is represented respectively.By realizing eFAST in Matlab
The calculating of sensitivity, parameter needed for input SWMM models, Matlab generates the input for transmitting SWMM according to the data sample of input
File * .inp, model calculation generation * .rpt and * .out destination file, the output called, extracted in * .rpt by Matlab
Information simultaneously generate be available for its handle the output data sample corresponding with input sample of data, then by input sample with it is corresponding
Export the sensitivity factor that sample calculates relevant parameter by matlab eFAST sensitivity analysis algorithms.
SWMM has contain all SWMM model runnings in oneself independent input file form * .inp, file needed for
The value of parameters and the spatial data of modeling, when SWMM is called, while needing to provide the input of other two kinds of forms
File * .rpt and * .out, both are SWMM analysis report file and SWMM operation result file, both of these documents respectively
Content can be automatically generated in SWMM operation, and * .inp files are then needed before SWMM computings in strict accordance with SWMM institutes
Parameter and elevation information is needed to generate.Therefore, first by running Matlab, the specific data according to needed for eFAST analysis methods
Sample generation side X (according to specific search curve, Searching Curve), then the data sample X of generation is delivered separately to
SWMM input file * .inp, every time transmission all replaces legacy data sample with one group of new data sample respectively.Afterwards, SWMM
Computation engine SWMM Engine information in extraction input file will be called to carry out computing, and operation result is stored respectively
In * .rpt and * .out files, then called by Matlab and extract in * .rpt files corresponding output information and generate
Corresponding output data sample Y, it is finally, respectively that input sample X and output sample Y is specific by eFAST sensitivity analysis
Algorithm calculates the single order and the total sensitivity factor of relevant parameter respectively.Said process will according to default number of parameters k and
Search curve sampling number Ns determine, once complete sensitivity analysis will need carry out k*Ns SWMM and Matlab between
Data interaction, and Matlab is completed with the data interaction between SWMM * .inp files by regular expression
An experiment case study explanation everywhere below：
Based on CHJ drainage systems, it is considered to (different rainfall intensities, different rainfall durations), SWMM moulds under different condition of raining
The different input parameters of type are to system output (height of run-off, crest discharge reach time and the system total displacement of flood peak).
Case is considered under Infiltration Model Horton, Green-Ampt and CN curve method respectively, under different condition of raining, model
Under the analysis knot Horton Infiltration Models of parametric sensitivity, impervious surface ratio is influence height of run-off, peak flow, accumulative row
The primary factor of influence of water；Under Green-Ampt Infiltration Models, impervious surface percentage is to influence the primary shadow of height of run-off
It is influence peak flow to ring the factor, impervious surface Manning coefficient.Larger ginseng is influenceed on model result for our clear and definite parameters
Several classes of types, the time required to reducing the work of model calibration, improve the degree of accuracy of analog result.
Claims (5)
1. a kind of parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions, it is characterised in that including step：
S1：EFAST analysis methods are simulated by Matlab, input sample of data is passed to the inp formatted files needed for SWMM；
S2：Called using SWMM computation engine and extract the information in inp formatted files and carry out computing, and operation result is divided
It is not stored in rpt formatted files and out formatted files；
S3：Called by Matlab and extract in rpt formatted files corresponding output information and generate corresponding output data sample
This；
S4：Input sample of data and output data sample are calculated into corresponding ginseng respectively by eFAST sensitivity analysis algorithms respectively
Several one order factors；
S5：The big corresponding bar of parameter of the one order factor is optimized according to the one order factor optimizing specific aim of acquisition
Part.
2. a kind of parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions according to claim 1, its
It is characterised by, the step S1 specifically includes step：
S11：Run Matlab, the input sample of data of the parameter according to needed for eFAST analysis methods；
S12：The input sample of data of generation is passed to the inp formatted files needed for SWMM.
3. a kind of parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions according to claim 1, its
It is characterised by, the step S4 is specifically included：
S41：Build multidimensional function model Y=f (X), wherein X (x1,x2,…,xn) it is input parameter；
S42：Function G is searched in settingi, and use search function by multidimensional function model conversion for one-dimensional functions model Y=f (s)：
Gi=(sin ωiS), i=1,2 ..., n
Wherein：I is parameter xiParameter sequence number, ωiFor parameter xiInteger frequency, s is real number；
S43：Fourier transformation is carried out to one-dimensional functions model：
Wherein：P is Fourier's factor, p ∈ Z, Ap,BpFor Fourier's amplitude, and：
S44：Calculate the variance as caused by each parameter and model population variance：
Wherein：ViFor parameter xiCaused variance, V is model population variance, ΛjFor the frequency spectrum of Fourier space, j ∈ Z；
S45：According to acquisition variance as caused by each parameter and model population variance, obtain the one order of each parameter because
Son：
Wherein：SiFor parameter xiThe one order factor.
4. a kind of Parameter Sensitivity Analysis method based on SWMM Yu MATLAB data interactions according to claim 3, its
It is characterised by, Fourier space is specially：
Wherein：ΛpFor the frequency spectrum of Fourier space.
5. a kind of parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions according to claim 3, its
It is characterised by, the input parameter at least includes peak width, regional slope, region Impervious surface coverage, the water storage in water penetration region
Depth, the water storage depth in waterproof region, waterproof bent Manning coefficient, permeable area's Manning coefficient and impervious zone Wu Wa store ground
Skill ratio.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710107361.XA CN107038278A (en) | 2017-02-27 | 2017-02-27 | Parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710107361.XA CN107038278A (en) | 2017-02-27 | 2017-02-27 | Parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107038278A true CN107038278A (en) | 2017-08-11 |
Family
ID=59533621
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710107361.XA Pending CN107038278A (en) | 2017-02-27 | 2017-02-27 | Parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107038278A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107742170A (en) * | 2017-10-25 | 2018-02-27 | 天津大学 | A kind of storm sewer system the Hydraulic Design parameter optimization method |
| CN108319788A (en) * | 2018-02-06 | 2018-07-24 | 重庆大学 | A method of the identification in line pollution sources of Storm Sewer Network sewage |
| CN108897964A (en) * | 2018-07-09 | 2018-11-27 | 重庆大学 | A kind of Bayesian statistics source tracing method of sewage network discharge beyond standards industrial wastewater |
| CN112084608A (en) * | 2020-07-24 | 2020-12-15 | 北京工业大学 | Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model |
| CN112597670A (en) * | 2021-03-05 | 2021-04-02 | 武汉理工大学 | Data input method and device of rainstorm flood management model software |
| CN113190944A (en) * | 2021-04-30 | 2021-07-30 | 西安理工大学 | Urban rainwater drainage system automatic optimization method based on SWMM and MATLAB |
| CN113468834A (en) * | 2021-06-17 | 2021-10-01 | 深圳市一博科技股份有限公司 | Method for simplifying generation of rpt file |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105389469A (en) * | 2015-11-09 | 2016-03-09 | 中山大学 | Automatic calibration method of storm water management model parameters |
| CN106056247A (en) * | 2016-06-02 | 2016-10-26 | 广东工业大学 | Method for selecting optimal traffic path in urban waterlogging situation |
-
2017
- 2017-02-27 CN CN201710107361.XA patent/CN107038278A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105389469A (en) * | 2015-11-09 | 2016-03-09 | 中山大学 | Automatic calibration method of storm water management model parameters |
| CN106056247A (en) * | 2016-06-02 | 2016-10-26 | 广东工业大学 | Method for selecting optimal traffic path in urban waterlogging situation |
Non-Patent Citations (3)
| Title |
|---|
| 何维 等: "模型参数全局敏感性分析的EFAST方法", 《遥感技术与应用》 * |
| 张一龙 等: "城市地表产流计算方法和径流模型研究进展", 《四川环境》 * |
| 熊剑智: "城市雨洪模型参数敏感性分析与率定", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107742170A (en) * | 2017-10-25 | 2018-02-27 | 天津大学 | A kind of storm sewer system the Hydraulic Design parameter optimization method |
| CN108319788A (en) * | 2018-02-06 | 2018-07-24 | 重庆大学 | A method of the identification in line pollution sources of Storm Sewer Network sewage |
| CN108319788B (en) * | 2018-02-06 | 2021-12-24 | 重庆大学 | Method for identifying direct sewage discharge pollution source of rainwater pipe network |
| CN108897964A (en) * | 2018-07-09 | 2018-11-27 | 重庆大学 | A kind of Bayesian statistics source tracing method of sewage network discharge beyond standards industrial wastewater |
| CN112084608A (en) * | 2020-07-24 | 2020-12-15 | 北京工业大学 | Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model |
| CN112084608B (en) * | 2020-07-24 | 2023-12-19 | 北京工业大学 | Method for identifying risk pipelines and nodes by adopting parameter uncertainty analysis model |
| CN112597670A (en) * | 2021-03-05 | 2021-04-02 | 武汉理工大学 | Data input method and device of rainstorm flood management model software |
| CN113190944A (en) * | 2021-04-30 | 2021-07-30 | 西安理工大学 | Urban rainwater drainage system automatic optimization method based on SWMM and MATLAB |
| CN113468834A (en) * | 2021-06-17 | 2021-10-01 | 深圳市一博科技股份有限公司 | Method for simplifying generation of rpt file |
| CN113468834B (en) * | 2021-06-17 | 2024-01-05 | 深圳市一博科技股份有限公司 | Method for simplifying rpt file generation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107038278A (en) | Parametric sensitivity optimization method based on SWMM Yu MATLAB data interactions | |
| Malekzadeh et al. | A novel approach for prediction of monthly ground water level using a hybrid wavelet and non-tuned self-adaptive machine learning model | |
| Hewlett et al. | In defense of experimental watersheds | |
| Demirci et al. | Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches | |
| Momm et al. | AGNPS GIS-based tool for watershed-scale identification and mapping of cropland potential ephemeral gullies | |
| CN105912770A (en) | Real-time hydrologic forecasting system | |
| CN103473463B (en) | A kind of method quantitatively determining background concentration of nitrogen and phosphorus of water body of lake basins | |
| CN107463730A (en) | A kind of streamflow change attribution recognition methods for considering Spatio-temporal Evolution of Land Use | |
| Leake et al. | A new capture fraction method to map how pumpage affects surface water flow | |
| Wu et al. | Reuse of return flows and its scale effect in irrigation systems based on modified SWAT model | |
| CN108733952B (en) | Three-dimensional characterization method for spatial variability of soil water content based on sequential simulation | |
| CN113673765A (en) | Small watershed flood forecasting and early warning method and terminal | |
| Li et al. | Development in improved surface irrigation in China | |
| Fang et al. | Application of long short-term memory (LSTM) on the prediction of rainfall-runoff in karst area | |
| CN105893590B (en) | One kind being used for digital Terrain Analysis modeling knowledge case automatic processing method | |
| CN116401327A (en) | Storm flood calculation auxiliary system for small and medium-sized watershed design in non-data area | |
| CN113312736B (en) | River network hydrodynamic simulation implementation method and system based on cloud platform | |
| CN110111538B (en) | Dynamic monitoring, early warning and analyzing system for mountain torrent disasters | |
| Rifai et al. | Data mining applied for earthworks optimisation of a toll road construction project | |
| CN114492233A (en) | Basin water simulation method based on webGIS platform and considering comprehensive utilization requirements | |
| Changjun et al. | A point clouds filtering algorithm based on grid partition and moving least squares | |
| CN114462254A (en) | Distributed hydrological model parallel computing method based on flow direction | |
| CN114841402A (en) | Underground water level height prediction method and system based on multi-feature map network | |
| Flipo et al. | Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data | |
| Guo et al. | Application of GIS and remote sensing techniques for water resources management |
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 |
Application publication date: 20170811 |
|
| RJ01 | Rejection of invention patent application after publication |