CN106777985A - A kind of Hydrologic Series nonuniformity diagnostic method based on classification - Google Patents
A kind of Hydrologic Series nonuniformity diagnostic method based on classification Download PDFInfo
- Publication number
- CN106777985A CN106777985A CN201611177270.5A CN201611177270A CN106777985A CN 106777985 A CN106777985 A CN 106777985A CN 201611177270 A CN201611177270 A CN 201611177270A CN 106777985 A CN106777985 A CN 106777985A
- Authority
- CN
- China
- Prior art keywords
- nonuniformity
- time series
- sequence
- variance
- null hypothesis
- 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
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
Landscapes
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The present invention relates to a kind of Hydrologic Series nonuniformity diagnostic method based on classification, including step：Collect statistical information；Carry out the nonuniformity diagnosis of Hydrological Time Series variance；Carry out the nonuniformity diagnosis of Hydrological Time Series average；Judge whether Hydrological Time Series are consistent.Method of the present invention is according to the nonconforming definition of the hydrology, and classified the hydrology is non-uniform according to the different of running parameter, then diagnosed using for running parameter selection different statistical methods, avoid the fuzzy disunity for causing statistical methods to diagnose the repetition of object, result due to checked object and inspection purpose, and it is uncertain, therefore the technical method is comparatively more advanced.The present invention is applied to any Hydrological Time Series.The method of the invention technical method is general, it is easy to popularization and application.
Description
Technical field
It is that a kind of hydrology that is based on surveys number the present invention relates to a kind of Hydrologic Series nonuniformity diagnostic method based on classification
According to Statistical Identifying Method, be it is a kind of based on nonuniformity classification diagnostic method.
Background technology
On the diagnosis of hydrology nonuniformity, current technology mainly has following several.One kind is to Hydrological Time Series trend
With the statistical check of catastrophe point, quantitative assessment is carried out according to assay watershed hydrographic features.Such diagnostic method is a lot, generation
The table method of inspection has Man-Kendall to check, Hurst coefficients, Pettitt inspections, Spearman rank correlation methods.Second
It is then integrated various Statistical Identifying Methods, the multiple statistical inspection of trend and catastrophe point is carried out to single time series, to determine
The real change of the hydrology variable, diagnostic system is made a variation as representative with the hydrology, and diagnosis process is divided into：Tentative diagnosis, examine in detail
Disconnected, three steps of comprehensive diagnos, in detail tentative diagnosis design process collimation method, moving average method and Hurst Y-factor method Ys, diagnosis then divide
It is that trend and jump are diagnosed, is related to 14 kinds of statistical methods, and comprehensive diagnos is then by the result synthesis of trend and jump diagnosis
Get up to be analyzed and evaluated.Finally combine actual hydrologic survey to analyze, variant form and conclusion are confirmed, so as to obtain most
Whole variation diagnostic result.The third is, based on statistical means such as Spectrum Analysis, the subjective judgement of line period to be entered to time series,
Main method has wavelet analysis method, continuous Zymography, period map method etc..More than it is several existing non-uniform to Hydrologic Series
All there are some deficiencies in various degree in property diagnostic method.
Individual event inspection to Hydrologic Series, the basic assumption that defect comes from different statistical methods is different, these methods master
Will by judge sequence whether occurrence tendency or jump change it is whether consistent to judge sequence, its essence is to Hydrologic Series sample
Whether this average there is the hypothesis testing of significant changes.Computational accuracy differs between distinct methods, and assay is inconsistent, verifies
Jump it is not unique.Though non-uniform diagnostic system combines statistical methods, it is intended to by the result of various diagnostic methods
Reliability to assay is lifted, but calculating process is cumbersome, and the reliability of result of calculation is not also in single inspection
Increased significantly on the basis of method.
The deficiency of above method is：The definition of the uniformity according to time series：The average and variance of sequence samples are equal
Do not changed with the time, conventional inspection method is only checked to the average of sample, and have ignored the change of variance.This is right
Judge the whether consistent most important of time series.
The content of the invention
In order to overcome problem of the prior art, the conformance definition according to time series of the invention, to Hydrological Time Series
Nonuniformity be classified, and propose a kind of based on average is non-uniform and the non-uniform two kinds of Hydrologic Series of variance
Nonuniformity diagnostic method.Methods described has the ability applied in any Hydrological Time Series, and with can be diagnosed to be
Which kind of nonconforming ability is key element belong to.
The object of the present invention is achieved like this：A kind of Hydrologic Series nonuniformity diagnostic method based on classification, it is special
Levy and be, comprise the following steps：
The step of collecting statistical information：The time series of the hydrographic features for needing to be analyzed is collected, sample size is at least 30；
The step of carrying out Hydrological Time Series variance nonuniformity and diagnose：Assuming that predictor is constituted by with Regression：
（1）
Wherein；
Make the following assumptions：
:,:。
Construction augmentation Dickey-fowler（Augmented Dickey-Fuller, ADF）Test statistics：, its
In,It is the standard deviation of statistic ρ；
In the case where there is unit root in sequence,Statistic does not meet t- distributions, and with the increase of sample size, τ statistics are received
Hold back in the functional of standard Wiener-Hopf equation, and can be drawn with monte carlo method simulation, delayed item is added on the right of model：, to alleviateThe auto-correlation problem of item.ADF inspections are based on least square（OLS）Regression equation assume predictor by
Constituted with Regression：
（2）
Wherein：T is the time,.Inspection random processWhether there is unit root, as checkWhether
Significantly, less than 0, and null hypothesis be in the presence of a unit root, alternative hypothesis be in the absence of unit root, ifSignificantly, less than 0, then zero
Assuming that being rejected.ADF verifies as single side test, when significance is taken asWhen, noteFor the level of signifiance isWhen tantile,
ThenWhen, refuse null hypothesis, it is believed that sequence does not exist unit root, otherwise receives null hypothesis, it is believed that sequence has unit root,
Variance has significant change.
The step of nonuniformity for carrying out Hydrological Time Series average is diagnosed：To sequence x1, x2 ..., xn, first determine all
Allelomorph (xi, xj), (j>I), the xi in<The appearance number (being set to p) of xj；(i, j) subset of order is：(i=1, j=
2,3,4 ..., n), (i=2, j=3,4,5 ..., n) ..., (i=n-1, j=n)；If the value advanced in order is all greater than preceding
One value, this is a kind of ascendant trend, p be (n-1)+(n-2)+...+1, it is arithmetic series to be, then summation is n (n-1)/2.Such as
Fruit order is all turned around, then p=0, as downward trend.It follows that to neutral sequence, the mathematic expectaion of p is；
This inspection statistic be：
（3）
Wherein：
；
；
Work as n>When 10, U converges on standardized normal distribution；
Null hypothesis is trendless, after given level of signifiance α, critical value U α/2 is found in gaussian distribution table；
WhenWhen, receive null hypothesis, i.e. average and significant changes do not occur；WhenWhen, refuse null hypothesis, i.e.,
There are significant changes in value；
Judge the whether consistent step of Hydrological Time Series：When average and variance do not exist significant changes, the hydrology time
Sequence is consistent；Otherwise it is non-uniform.
The beneficial effect comprise that：Method of the present invention according to the nonconforming definition of the hydrology, and according to
The different of running parameter are classified the hydrology is non-uniform, are then examined using for running parameter selection different statistical methods
It is disconnected, it is to avoid due to checked object and inspection purpose the fuzzy repetition, the result that cause statistical methods to diagnose object not
It is unified and uncertain, therefore the technical method is comparatively more advanced.The present invention is applied to any hydrology time sequence
Row.The method of the invention technical method is general, it is easy to popularization and application.
Brief description of the drawings
The invention will be further described with reference to the accompanying drawings and examples.
Fig. 1 is the flow chart of embodiments of the invention methods described.
Specific embodiment
Embodiment：
The present embodiment is a kind of Hydrologic Series nonuniformity diagnostic method based on classification.Consistency concept is to hydrology simple sequence
Nonuniformity is classified, and provides reliable diagnosis according to the nonuniformity research that nonuniformity is categorized as hydrology simple sequence
Method.
The general principle of the present embodiment is：According to the nonconforming definition of the hydrology：Hydrological Time Series are no longer complies with independence
Same distributional assumption, average or variance or both change with the time, it is possible thereby to pass through Check-Out Time sequence average and
Whether variance there are significant changes to determine whether time series occurs nonuniformity.Change in Mean show time series with
There is a Long-term change trend for determination time, and variance change shows that the dispersion of time series changes.Conversed analysis, with going
Stable time series necessarily contains trend components after trend, and stable time series its original after first-order difference
Variance necessarily change over time, but the change of its average with otherwise do not know.But processed by both, can be according to average
Or nonuniformity is divided into two classes by the change of variance, a class is the nonuniformity of average, and another kind of is the nonuniformity of variance.
According to classification, the non-uniform of average is diagnosed using Kendall ranks related check, using unit root test
The non-uniform of variance is diagnosed, exclude two kinds it is non-uniform can confirm that the sequence is consistent, otherwise for non-uniform, and examine
Break and the sequence and belong to any non-uniform type.
It is computer program that method described in the present embodiment can be worked out, in operating in PC or other all-purpose computers.
The step of the present embodiment methods described, is as follows：
The step of collecting statistical information：The time series of the hydrographic features for needing to be analyzed is collected, sample size is at least 30；
The step of nonuniformity for carrying out Hydrological Time Series variance is diagnosed：The non-uniform of variance is carried out from unit root test
Inspection, is used to diagnose whether variance occurs significant changes.Checked using the augmentation Dickey in unit root test-fowler
(Augmented Dickey-Fuller, ADF）Carry out the non-uniform diagnosis of variance：
Assuming that predictor is constituted by with Regression：
（4）
Wherein；
Make the following assumptions：
:,:。
Construction augmentation Dickey-fowler（Augmented Dickey-Fuller, ADF）Test statistics：, its
In,It is the standard deviation of statistic ρ；
In the case where there is unit root in sequence,Statistic does not meet t- distributions, and with the increase of sample size, τ statistics are received
Hold back in the functional of standard Wiener-Hopf equation, and can be drawn with monte carlo method simulation, delayed item is added on the right of model：, to alleviateThe auto-correlation problem of item.ADF inspections are based on least square（OLS）Regression equation assume predictor by
Constituted with Regression：
（5）
Wherein：T is the time,.Inspection random processWhether there is unit root, as checkWhether
Significantly, less than 0, and null hypothesis be in the presence of a unit root, alternative hypothesis be in the absence of unit root, ifSignificantly, less than 0, then zero
Assuming that being rejected.ADF verifies as single side test, when significance is taken asWhen, noteFor the level of signifiance isWhen tantile,
ThenWhen, refuse null hypothesis, it is believed that sequence does not exist unit root, otherwise receives null hypothesis, it is believed that sequence has unit root,
Variance has significant change.
The step of nonuniformity for carrying out Hydrological Time Series average is diagnosed：From Kendall rank related checks, with true
Determine whether average occurs significant changes：
To sequence x1, x2 ..., xn, all allelomorphs (xi, xj) (j is first determined>I) xi in<The appearance number of xj (is set to
p).(i, j) subset of order is：(i=1, j=2,3,4 ..., n), (i=2, j=3,4,5 ..., n) ..., (i=n-1, j=n).
If the value advanced in order is all greater than previous value, this is a kind of ascendant trend, p be (n-1)+(n-2)+...+1, be for
Arithmetic series, then summation is n (n-1)/2.If order is all turned around, p=0, as downward trend.It follows that to nothing
The sequence of trend, the mathematic expectaion of p is。
This inspection statistic be：
（6）
Wherein：
；
；
Work as n>When 10, U converges on standardized normal distribution.
Null hypothesis is trendless, after given level of signifiance α, critical value U α/2 is found in gaussian distribution table.WhenWhen, receive null hypothesis, i.e. average and significant changes do not occur；WhenWhen, refuse null hypothesis, i.e. average and occur
Significant changes.
Judge the whether consistent step of Hydrological Time Series：When there are no significant changes in average and variance, the sequence
It is consistent, otherwise for non-uniform.
Two inspections by showing that former sequence is concensus sequence, any one not by being non-uniform sequence, if
The test fails for Kendall, and as average is non-uniform, if unit root test does not pass through, as variance is non-uniform, if both
Do not pass through, show that former sequence belongs to the non-uniform of average and variance.
The present embodiment application example：
7 Inflow Sequences of the 9 extreme rainfall sequences and Miyun Reservoir of choosing Beijing carry out non-uniform diagnosis, as a result as follows
Shown in table.
The extreme interception rainfall index sequence nonuniformity diagnostic result in table 1- Beijing 9
| Continuous Drought day Number | Continuous rainfall day Number | Moistening day accumulation drop Rainfall | Rainfall ＞ 10mm Number of days | Rainfall ＞ 25mm Number of days | Rainfall ＞ 50mm Number of days | >95% tantile rainfall is tired out Metering | >99% tantile rainfall is tired out Metering | Drop within maximum one day Water | Drop within maximum five days Water | |
| Difference | ○ | ○ | ○ | ● | ● | ○ | ● | ● | ● | ● |
| Trend | ○ | ○ | ○ | ○ | ○ | ○ | ● | ○ | ● | ○ |
| Diagnosis As a result | ○ | ○ | ○ | ● | ● | ○ | ● | ● | ● | ● |
7 runoff index series nonuniformity diagnostic results of table 2- Miyun Reservoirs
| Spring runoff | Summer runoff | Autumn runoff | Winter runoff | Non-flood period runoff | Flood season runoff | Annual flow | |
| Difference | ● | ○ | ○ | ● | ● | ○ | ○ |
| Trend | ● | ● | ● | ● | ● | ● | ● |
| Diagnostic result | ● | ● | ● | ● | ● | ● | ● |
In table：● represent such non-uniform change, zero represents such non-uniform change
Diagnostic result shows have 6 indexs that non-uniform changes, 6 sides of index occur in 9 Extreme Precipitation indexs of Beijing
There is non-uniform change in difference, wherein 2 averages of index occur non-uniform change；7 runoff indexs of Miyun remittance are all sent out
There is non-uniform change in the non-uniform change of life, 7 averages of index, wherein 3 variances of index also there occurs non-uniform change.
The application effect for reaching is as follows：
1st, ability of the present invention suitable for various time serieses has been embodied.9 Extreme Precipitation indexs and 7 runoff indexs are entered
Nonuniformity diagnosis is gone, and has determined which kind of nonuniformity it belongs to；
2nd, calculate easy, it is only necessary to carry out two steps and just can determine whether whether time series is nonuniformity, if so, can simultaneously judge that it belongs to
In which kind of time series；
Finally it should be noted that being merely illustrative of the technical solution of the present invention and unrestricted above, although with reference to preferred arrangement side
Case has been described in detail to the present invention, it will be understood by those within the art that, can be to technical scheme
（The application of such as statistical method, the use of formula, ordinal relation of each step etc.）Modify or equivalent, without
Depart from the spirit and scope of technical solution of the present invention.
Claims (1)
1. it is a kind of based on the Hydrologic Series nonuniformity diagnostic method classified, it is characterised in that to comprise the following steps：
The step of collecting statistical information：The time series of the hydrographic features for needing to be analyzed is collected, sample size is at least 30；
The step of nonuniformity for carrying out Hydrological Time Series variance is diagnosed：Assuming that predictor is constituted by with Regression：
（1）
Wherein；
Make the following assumptions：
:,:；
Construction augmentation Dickey-fowler test statistics：, wherein,It is the standard deviation of statistic ρ；
In the case where there is unit root in sequence,Statistic does not meet t- distributions, and with the increase of sample size, τ statistics are received
Hold back in the functional of standard Wiener-Hopf equation, and can be drawn with monte carlo method simulation, delayed item is added on the right of model：, to alleviateThe auto-correlation problem of item；
ADF inspections are based on least square（OLS）Regression equation assumes that predictor is constituted by with Regression：
（2）
Wherein：T is the time,；Inspection random processWhether there is unit root, as checkWhether
Significantly, less than 0, and null hypothesis be in the presence of a unit root, alternative hypothesis be in the absence of unit root, ifSignificantly, less than 0, then zero
Assuming that being rejected；
ADF verifies as single side test, when significance is taken asWhen, noteFor the level of signifiance isWhen tantile, then
When, refuse null hypothesis, it is believed that sequence does not exist unit root, otherwise receives null hypothesis, it is believed that sequence has unit root, and variance has aobvious
Write change；
The step of nonuniformity for carrying out Hydrological Time Series average is diagnosed：To sequence x1, x2 ..., xn, all antithesis are first determined
Value (xi, xj) (j>I) xi in<The appearance number (being set to p) of xj；(i, j) subset of order is：(i=1, j=2,3,
4 ..., n), (i=2, j=3,4,5 ..., n) ..., (i=n-1, j=n)；If the value advanced in order is all greater than previous value,
This is a kind of ascendant trend, p be (n-1)+(n-2)+...+1, it is arithmetic series to be, then summation is n (n-1)/2；
If order is all turned around, p=0, as downward trend；
It follows that to neutral sequence, the mathematic expectaion of p is；
This inspection statistic be：
（3）
Wherein：
；
；
Work as n>When 10, U converges on standardized normal distribution；
Null hypothesis is trendless, after given level of signifiance α, critical value U α/2 is found in gaussian distribution table；
WhenWhen, receive null hypothesis, i.e. average and significant changes do not occur；WhenWhen, refuse null hypothesis, i.e.,
There are significant changes in value；
Judge the whether consistent step of Hydrological Time Series：When average and variance do not exist significant changes, the hydrology time
Sequence is consistent；Otherwise it is non-uniform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611177270.5A CN106777985B (en) | 2016-12-19 | 2016-12-19 | A kind of Hydrologic Series nonuniformity diagnostic method based on classification |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611177270.5A CN106777985B (en) | 2016-12-19 | 2016-12-19 | A kind of Hydrologic Series nonuniformity diagnostic method based on classification |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106777985A true CN106777985A (en) | 2017-05-31 |
| CN106777985B CN106777985B (en) | 2018-07-06 |
Family
ID=58890325
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611177270.5A Active CN106777985B (en) | 2016-12-19 | 2016-12-19 | A kind of Hydrologic Series nonuniformity diagnostic method based on classification |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106777985B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107330252A (en) * | 2017-06-12 | 2017-11-07 | 武汉大学 | Hydrological Time Series jump variation error comprehensive diagnosis method |
| CN109271664A (en) * | 2018-08-03 | 2019-01-25 | 华南理工大学 | Design storm Safety Analysis Method under the conditions of being assumed based on consistency and nonuniformity |
| CN110260774A (en) * | 2019-07-22 | 2019-09-20 | 安徽理工大学 | A kind of inspection of GNSS deformation information and method for early warning based on Pettitt algorithm |
| CN111353127A (en) * | 2018-12-24 | 2020-06-30 | 顺丰科技有限公司 | Single variable point detection method, system, equipment and storage medium |
| CN111488679A (en) * | 2020-04-03 | 2020-08-04 | 中国能源建设集团江苏省电力设计院有限公司 | Method for calculating non-uniform wind speed sequence design wind speed |
| CN112149296A (en) * | 2020-09-17 | 2020-12-29 | 中国科学院地理科学与资源研究所 | Method for judging stability type of hydrological time sequence |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105205217A (en) * | 2015-08-25 | 2015-12-30 | 中国科学院地理科学与资源研究所 | Method for judging hydrologic time series non-stationarity |
| CN106202002A (en) * | 2016-06-28 | 2016-12-07 | 河海大学 | A kind of for detecting the method whether series of hydrological parameter makes a variation |
-
2016
- 2016-12-19 CN CN201611177270.5A patent/CN106777985B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105205217A (en) * | 2015-08-25 | 2015-12-30 | 中国科学院地理科学与资源研究所 | Method for judging hydrologic time series non-stationarity |
| CN106202002A (en) * | 2016-06-28 | 2016-12-07 | 河海大学 | A kind of for detecting the method whether series of hydrological parameter makes a variation |
Non-Patent Citations (3)
| Title |
|---|
| YANGYANG XIE 等: "Period analysis of hydrologic series through moving-window correlation analysis method", 《JOURNAL OF HYDROLOGY》 * |
| 张应华 等: "水文气象序列趋势分析与变异诊断的方法及其对比", 《干旱区地理》 * |
| 胡义明 等: "基于趋势分析的非一致性水文频率分析方法研究", 《水力发电学报》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107330252A (en) * | 2017-06-12 | 2017-11-07 | 武汉大学 | Hydrological Time Series jump variation error comprehensive diagnosis method |
| CN109271664A (en) * | 2018-08-03 | 2019-01-25 | 华南理工大学 | Design storm Safety Analysis Method under the conditions of being assumed based on consistency and nonuniformity |
| CN109271664B (en) * | 2018-08-03 | 2022-09-27 | 华南理工大学 | Design rainstorm safety analysis method based on assumed conditions of consistency and non-consistency |
| CN111353127A (en) * | 2018-12-24 | 2020-06-30 | 顺丰科技有限公司 | Single variable point detection method, system, equipment and storage medium |
| CN111353127B (en) * | 2018-12-24 | 2024-05-17 | 顺丰科技有限公司 | Single-change-point detection method, system, equipment and storage medium |
| CN110260774A (en) * | 2019-07-22 | 2019-09-20 | 安徽理工大学 | A kind of inspection of GNSS deformation information and method for early warning based on Pettitt algorithm |
| CN111488679A (en) * | 2020-04-03 | 2020-08-04 | 中国能源建设集团江苏省电力设计院有限公司 | Method for calculating non-uniform wind speed sequence design wind speed |
| CN111488679B (en) * | 2020-04-03 | 2022-06-10 | 中国能源建设集团江苏省电力设计院有限公司 | Method for calculating non-uniform wind speed sequence design wind speed |
| CN112149296A (en) * | 2020-09-17 | 2020-12-29 | 中国科学院地理科学与资源研究所 | Method for judging stability type of hydrological time sequence |
| CN112149296B (en) * | 2020-09-17 | 2023-06-20 | 中国科学院地理科学与资源研究所 | Method for judging stability type of hydrologic time sequence |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106777985B (en) | 2018-07-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106777985B (en) | A kind of Hydrologic Series nonuniformity diagnostic method based on classification | |
| CN106650797B (en) | Power distribution network electricity stealing suspicion user intelligent identification method based on integrated ELM | |
| Wang et al. | Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes | |
| Perelman et al. | Automated sub-zoning of water distribution systems | |
| Prataviera et al. | A new generalized odd log-logistic flexible Weibull regression model with applications in repairable systems | |
| Sadri et al. | Copula-based pooled frequency analysis of droughts in the Canadian Prairies | |
| De Kort et al. | Decision making under uncertainty in a decision support system for the Red River | |
| CN103886217A (en) | Ecological risk determining method for heavy metal pollution in river and lake sediments | |
| Li et al. | A new strategy to benchmark and evaluate building electricity usage using multiple data mining technologies | |
| Fan et al. | Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: Application to the Xiangxi river watershed, China | |
| Omer et al. | Integrated assessment of the impacts of climate variability and anthropogenic activities on river runoff: a case study in the Hutuo River Basin, China | |
| Ridolfi et al. | A new methodology to define homogeneous regions through an entropy based clustering method | |
| Ahmad et al. | On median control charting under double sampling scheme | |
| Adams et al. | An analysis of the impact of spatial variability in rainfall on runoff and sediment predictions from a distributed model | |
| Seiller et al. | Improving the temporal transposability of lumped hydrological models on twenty diversified US watersheds | |
| CN104616211A (en) | Water and electricity consumption ratio clustering based abnormal water and electricity user detection method and system | |
| Haining | Designing a health needs GIS with spatial analysis capability | |
| Zhang et al. | Spatio-temporal heterogeneity analysis of energy use in residential buildings | |
| CN103487644A (en) | Electric energy subentry energy consumption metering method and device | |
| CN111898068A (en) | Anomaly detection method based on PERT algorithm and instrument usage analysis | |
| Mirzaei et al. | Model calibration and uncertainty analysis of runoff in the Zayanderood River basin using generalized likelihood uncertainty estimation (GLUE) method | |
| Guan et al. | Objective Bayesian analysis for competing risks model with Wiener degradation phenomena and catastrophic failures | |
| CN113947504B (en) | Random forest method-based electricity stealing analysis method and system | |
| Wang et al. | Delineation and validation of river network spatial scales for water resources and fisheries management | |
| Knowles III et al. | Describing the dynamics, distributions, and multiscale relationships in the time evolution of residential building energy consumption |
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 |