CN107403004A - A kind of suspicious numerical examination method of remote gauged rainfall website based on terrain data - Google Patents
A kind of suspicious numerical examination method of remote gauged rainfall website based on terrain data Download PDFInfo
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- CN107403004A CN107403004A CN201710605510.5A CN201710605510A CN107403004A CN 107403004 A CN107403004 A CN 107403004A CN 201710605510 A CN201710605510 A CN 201710605510A CN 107403004 A CN107403004 A CN 107403004A
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Abstract
The invention discloses a kind of suspicious numerical examination method of the remote gauged rainfall website based on terrain data.Real-time rainfall station data in region is established Spatial weight matrix by the present invention according to geospatial coordinates distance first；Then spatial autocorrelation modeling analysis is carried out to each website according to the rainfall property value of website, and judges whether the property value clusters according to class statistic parameter；Secondly on the basis of cluster, by calculating cluster condition discrimination index ScdiFilter out suspicious numerical value；The reclassification of website is finally carried out according to the digital elevation attribute of remote gauged rainfall website, and spatial simlanty analysis is carried out to the property value of suspicious website and the website in the range of the distance threshold of setting, confirm whether the data that remote gauged rainfall website transmits are exceptional value with this.The present invention improves the degree of accuracy and the precision of traditional single threshold value verification.
Description
Technical field
The present invention relates to a kind of numerical examination algorithm, and in particular to is based on continuous essential factors space relation and slope aspect to one kind
The precipitation station telemetry method of calibration of checking.
Background technology
Precipitation has served as key player in fields such as hydrology, meteorology, ecology and agricultural researches, especially big number
According to the arrival in epoch, rainfall data has important basic meaning in fields such as hazard prediction, Flood Pre-warning Systems.Precipitation conduct simultaneously
One important environmental key-element, and the study hotspot in natural science field；Therefore the degree of accuracy of rainfall and precision, it is necessary to
Effectively verified.Ground Remote detection of precipitation station is a kind of widely used Rainfall estimation means, and the hydrology should at present
The major way gathered with department's rainfall data.But due to remote-measuring equipment failure, damage, and some other non-natural reason,
Wrong report or the missing of rainfall data are often caused, to improve the simple data verification method by threshold range verification in the past,
The present invention proposes a kind of suspicious numerical examination algorithm of remote gauged rainfall website based on terrain data.
The content of the invention
It is an object of the invention to solve problem present in existing rainfall verification, improvement relied on threshold range merely in the past
The data verification method of verification, and propose a kind of suspicious numerical examination method of the remote gauged rainfall website based on terrain data.
Data verification method in the present invention combines continuous essential factors space Correlation model, on the basis of cluster,
By calculating cluster condition discrimination index ScdiValue filter out suspicious numerical value.It is finally high according to the numeral of remote gauged rainfall website
Journey attribute carries out the reclassification of website, and carries out sky to the property value of suspicious website and the website in the range of the distance threshold of setting
Between similarity analysis, confirm whether the data that remote gauged rainfall website transmits are exceptional value, improve traditional single threshold value school with this
The degree of accuracy tested and precision.
The concrete technical scheme of the present invention is as follows：
The suspicious numerical examination method of remote gauged rainfall website based on terrain data, it comprises the following steps：
S1：Obtain the remote gauged rainfall station rainfall monitoring data sometime put target area and remote gauged rainfall website space is sat
Mark data and DEM terrain datas；
S2：Selected threshold distance, described spatial data is subjected to spatial relationship generalities processing, generation space away from
From weight matrix, aspect factor is extracted from DEM terrain datas and carries out binaryzation assignment processing；
S3：According to described space length weight matrix, the rainfall property value of remote gauged rainfall website is subjected to space from phase
Modeling analysis is closed, and the cluster state of remote gauged rainfall website is differentiated according to modeling result；On the basis of cluster, by each
The rainfall property value of remote gauged rainfall website carries out difference analysis to differentiate suspicious data；
S4：In the range of the distance threshold of setting, to having the website of suspicious data with surrounding adjacent to remote gauged rainfall in S3
Website carries out the comparison analysis of slope aspect property value, and finally differentiates whether it is abnormal data.
Preferably, rainfall station data temporal resolution is 1 hour in the S1；The space of the DEM terrain datas
Resolution ratio is 30 meters.
Preferably, in the S2, when the spatial data is carried out into spatial relationship generalities processing, threshold distance
Selection need to be calculated according to rainfall site density and model and require, it is ensured that each key element has several adjacent key elements, with generation
Space length weight matrix.
Preferably, in the S2, dem data is subjected to slope aspect processing, its processing procedure is：Using 3 × 3 mobile windows
Mouth accesses each pixel in input raster, and the slope aspect value positioned at the pixel of window center will be entered by fitting surface method every time
Row calculates.
Preferably, in the S3, the specific method of the spatial autocorrelation modeling analysis carried out to each station data is：
The spatial autocorrelation indicators C of i-th of remote gauged rainfall websiteiCalculation formula：
Wherein XiIt is the rainfall property value of i-th of remote gauged rainfall website,It is the average value of all website rainfall, SiFor institute
There are the root-mean-square value of website rainfall value, wijIt is adjacent sites j space length weights, n is equal to website sum；Wherein：
ZiIt is statistical significance measurement, for judging whether that null hypothesis can be refused, its calculation formula is：
Wherein EiFor desired value, σ is standard deviation；
Suspicious data discriminant index ScdiAsk calculate formula be：
Wherein m is the adjacent sites number in the range of the distance threshold of setting with website i, and N is rainfall website total number,
WijFor website j distance weighting,For the average value of all website rainfall；
Then the C of each remote gauged rainfall website is calculated respectivelyi、Scdi、Zi, under 0.95 confidence level, if key element
Ci>0 and Scdi<0, then judge the abnormity point that the website includes high level for a low value；If the C of key elementi<0 and Scdi<0, then sentence
The disconnected website is the abnormity point that a high level includes low value；Both the above situation is identified as suspicious data point.
Preferably, in the S4, the attribute data of slope aspect attribute data and remote gauged rainfall website in S2 is folded
Add analyzing and processing, the space coordinates based on remote gauged rainfall website extracts aspect factor the attribute of corresponding remote gauged rainfall website
In；Remote gauged rainfall station data is classified according to slope aspect attribute data, by referring to comparison, if existing in threshold distance
Exist together the adjacent remote gauged rainfall website of a slope aspect with the website with suspicious data, then it is abnormity point to prove this point, is completed
Abnormal point numerical verifies.
The present invention verified based on continuous essential factors space relation and slope aspect, to confirm that the data that remote gauged rainfall website transmits are
No is exceptional value, improves the degree of accuracy and the precision of traditional single threshold value verification.
Brief description of the drawings
Fig. 1 is remote gauged rainfall website distributing position schematic diagram in embodiment；
Fig. 2 is cluster and exceptional value distribution situation in embodiment；
Fig. 3 is slope aspect classification and buffering area schematic diagram in embodiment；
Fig. 4 is position of failure point schematic diagram in embodiment.
Embodiment
The present invention is further described with specific implementation profit below in conjunction with the accompanying drawings.
Real-time rainfall station data in region is established space weight square by the present invention according to geospatial coordinates distance first
Battle array；Then spatial autocorrelation modeling analysis is carried out to each website according to the rainfall property value of website, and according to class statistic
Parameter judges whether the property value clusters；Secondly on the basis of cluster, by calculating cluster condition discrimination index ScdiFilter out
Suspicious numerical value；The reclassification of website is finally carried out according to the digital elevation attribute of remote gauged rainfall website, and to suspicious website with setting
The property value of website in the range of fixed distance threshold carries out spatial simlanty analysis, confirms that remote gauged rainfall website transmits with this
Data whether be exceptional value.Wenzhou City, Zhejiang Province Wild Goose and Reed Marsh Mountains area is chosen below as target area, illustrates the present invention's
Implementation process.
Wenzhou City Wild Goose and Reed Marsh Mountains belongs to Southeastern Zhejiang Province mountains low-to-middle in height, hills area, general 500 to 600 meters of height above sea level, the hilllock Jian Hai of top hundred
1056.5 meters are pulled out, belongs to subtropical oceanic climate, abundant rainfall, seasonal rainfall is obvious, early summer plum rain season in 5~June, overcast and rainy
Unbroken, rainfall accounts for the 26~28% of whole year, and 7~September is by the more thunder showers of typhoon influence or torrential rain, and telemetric stations are placed in more
Mountain area, and be evenly distributed (distribution situation is as shown in Figure 1), station data fault rate is higher, chooses this region as research area
With typicalness.Several times are captured at random from the telemetry storehouse in Wild Goose and Reed Marsh Mountains area rainy season in 2015 (annual May-October)
The real-time rainfall data of node, after pretreatment, the model for carrying out inventive algorithm calculates, and is analyzed by relevant parameter, leads to
The intervention of terrain factor is crossed, finally from can be to filter out fault data in data, to improve the degree of accuracy of remote rain amount data.
The first step：The remote gauged rainfall station rainfall monitoring data and remote gauged rainfall website space coordinates number in the region are obtained first
According to DEM terrain datas, rainfall station data is Zhejiang Province Yandangshan Ares flood season real-time telemetry data in the present embodiment, the time
Resolution ratio is 1 hour；And it is 30 meters with region dem data spatial resolution.
Second step：Spatial data in step 1 is subjected to spatial relationship generalities processing, it is necessary to according to precipitation station
Dot density, threshold distance is chosen to be 5km, to ensure that each key element has several adjacent key elements, generates Spatial weight matrix.From
Aspect factor is extracted in DEM terrain datas and carries out binaryzation assignment processing.Wherein DEM slope aspects processing procedure is：Using movement
The window access input rasters of 3x 3 in each pixel, and every time positioned at the pixel of window center slope aspect value pass through be fitted it is bent
Eight adjacent picture elements values are included algorithm and calculated by face method, obtain the slope aspect value of center pel.
3rd step：The space length weight matrix obtained according to previous step, the rainfall property value of remote gauged rainfall website is entered
Row spatial autocorrelation modeling analysis, analysis method are：
The spatial autocorrelation indicators C of i-th of remote gauged rainfall websiteiCalculation formula：
Wherein XiIt is the rainfall property value of i-th of remote gauged rainfall website,It is the average value of all website rainfall, SiFor institute
There are the root-mean-square value of website rainfall value, wijIt is adjacent sites j space length weights, n is equal to website sum；Wherein：
ZiIt is statistical significance measurement, for judging whether that null hypothesis can be refused, its calculation formula is：
Wherein EiFor desired value, σ is standard deviation；
Suspicious data discriminant index ScdiAsk calculate formula be：
Wherein m is the adjacent sites number in the range of the distance threshold of setting with website i, and N is rainfall website total number,
WijFor website j distance weighting,For the average value of all website rainfall；
Then the cluster state of remote gauged rainfall website is differentiated according to above-mentioned modeling result, on the basis of cluster, by right
The rainfall property value of each remote gauged rainfall website carries out difference analysis to differentiate suspicious data.Its specific differentiation process is：
The C for calculating each remote gauged rainfall website respectively carried out to each station datai、Scdi、Zi, in 0.95 confidence water
Under flat (now | Zi|>1.96), if the C of key elementi>0 and Scdi<0, then judge the exception that the website includes high level for a low value
Point；If the C of key elementi<0 and Scdi<0, then judge the abnormity point that the website includes low value for a high level；Both the above situation
It is identified as suspicious data point.As shown in Fig. 2 filter out some suspicious data points.
4th step：It is neighbouring to having the website of suspicious data and surrounding in previous step in the range of the distance threshold of setting
Remote gauged rainfall website carries out the comparison analysis of slope aspect property value, and finally differentiates whether it is abnormal data.Specific practice is：
The attribute data of slope aspect data in step 2 and remote gauged rainfall website is overlapped analyzing and processing, based on remote measurement
The space coordinates of rainfall website is extracted aspect factor in the attribute of corresponding remote gauged rainfall website；To remote gauged rainfall station data
Slope aspect classification is carried out according to slope aspect data.As shown in figure 3, white portion is windward slope, brown areas is leeward slope, to that can count
Strong point carries out threshold distance 5km buffering area processing respectively.
Differ because alpine terrain rises and falls, and be located in seashore, can be by factor shadows such as High aititude temperature change, wind field deformations
Ring, therefore in order to exclude the influence of such factor, by referring to comparison, buffering area apart from it is interior exist with suspicious data
Website exists together the adjacent sites of a slope aspect, then proves that this point is abnormity point, that is, complete abnormal point numerical verification.Such as Fig. 4
Shown data point is fault data.
The above method improves the simple data verification method by threshold range verification in the past, improves the single threshold of tradition
It is worth the degree of accuracy and the precision of verification.
Embodiment described above is a kind of preferable scheme of the present invention, and so it is not intended to limiting the invention.Have
The those of ordinary skill of technical field is closed, without departing from the spirit and scope of the present invention, various changes can also be made
Change and modification.Therefore the technical scheme that all modes for taking equivalent substitution or equivalent transformation are obtained, the guarantor of the present invention is all fallen within
In the range of shield.
Claims (6)
1. a kind of suspicious numerical examination method of remote gauged rainfall website based on terrain data, it is characterised in that comprise the following steps：
S1：Obtain remote gauged rainfall station rainfall monitoring data and the remote gauged rainfall website space coordinates number that target area is sometime put
According to DEM terrain datas；
S2：Selected threshold distance, described spatial data is subjected to spatial relationship generalities processing, generation space length power
Weight matrix, aspect factor is extracted from DEM terrain datas and carries out binaryzation assignment processing；
S3：According to described space length weight matrix, the rainfall property value of remote gauged rainfall website is subjected to spatial autocorrelation and built
Mould is analyzed, and the cluster state of remote gauged rainfall website is differentiated according to modeling result；On the basis of cluster, by each remote measurement
The rainfall property value of rainfall website carries out difference analysis to differentiate suspicious data；
S4：In the range of the distance threshold of setting, to having the website of suspicious data with surrounding adjacent to remote gauged rainfall website in S3
The comparison analysis of slope aspect property value is carried out, and finally differentiates whether it is abnormal data.
2. the suspicious numerical examination method of the remote gauged rainfall website of terrain data as claimed in claim 1, it is characterised in that described
Rainfall station data temporal resolution is 1 hour in S1；The spatial resolution of the DEM terrain datas is 30 meters.
3. the suspicious numerical examination method of the remote gauged rainfall website of terrain data as claimed in claim 1, it is characterised in that described
In S2, when the spatial data is carried out into spatial relationship generalities processing, the selection of threshold distance is needed according to precipitation station
Dot density and model, which calculate, to be required, it is ensured that each key element has several adjacent key elements, to generate space length weight matrix.
4. the suspicious numerical examination method of the remote gauged rainfall website of terrain data as claimed in claim 1, it is characterised in that described
In S2, dem data is subjected to slope aspect processing, its processing procedure is：Using every in 3 × 3 mobile window access input rasters
Individual pixel, and the slope aspect value positioned at the pixel of window center will be calculated by fitting surface method every time.
5. the suspicious numerical examination method of the remote gauged rainfall website of terrain data as claimed in claim 1, it is characterised in that described
In S3, the specific method of the spatial autocorrelation modeling analysis carried out to each station data is：
The spatial autocorrelation indicators C of i-th of remote gauged rainfall websiteiCalculation formula：
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The root-mean-square value of rainfall value, wijIt is adjacent sites j space length weights, n is equal to website sum；Wherein：
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Wherein m is adjacent sites number in the range of the distance threshold of setting with website i, and N is rainfall website total number, WijFor
Website j distance weighting,For the average value of all website rainfall；
Then the C of each remote gauged rainfall website is calculated respectivelyi、Scdi、Zi, under 0.95 confidence level, if the C of key elementi>0
And Scdi<0, then judge the abnormity point that the website includes high level for a low value；If the C of key elementi<0 and Scdi<0, then judging should
Website is the abnormity point that a high level includes low value；Both the above situation is identified as suspicious data point.
6. the suspicious numerical examination method of the remote gauged rainfall website of terrain data as claimed in claim 1, it is characterised in that described
In S4, the attribute data of the slope aspect attribute data in S2 and remote gauged rainfall website is overlapped analyzing and processing, based on remote measurement rain
The space coordinates of amount website is extracted aspect factor in the attribute of corresponding remote gauged rainfall website；To remote gauged rainfall station data root
Classified according to slope aspect attribute data, by referring to comparison, if existing in threshold distance same with the website with suspicious data
Locate the adjacent remote gauged rainfall website of a slope aspect, then prove that this point is abnormity point, complete abnormal point numerical verification.
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| CN112016588A (en) * | 2020-07-16 | 2020-12-01 | 中山大学 | Spatial autocorrelation clustering method oriented to remote correlation mode |
| WO2022011728A1 (en) * | 2020-07-16 | 2022-01-20 | 中山大学 | Spatial auto-correlation clustering method for remote correlation mode |
| CN112016588B (en) * | 2020-07-16 | 2024-04-12 | 中山大学 | Space autocorrelation clustering method facing remote correlation mode |
| US12013900B2 (en) | 2020-07-16 | 2024-06-18 | Sun Yat-Sen University | Teleconnection pattern-oriented spatial association clustering method |
| CN113128590A (en) * | 2021-04-19 | 2021-07-16 | 浙江省水文管理中心 | Equipment data optimization and fusion method |
| CN114236645A (en) * | 2021-11-26 | 2022-03-25 | 中国水利水电科学研究院 | Large-scale rainfall monitoring abnormal site screening method |
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