CN107403004B - Remote-measuring rainfall site suspicious numerical inspection method based on terrain data - Google Patents

Remote-measuring rainfall site suspicious numerical inspection method based on terrain data Download PDF

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CN107403004B
CN107403004B CN201710605510.5A CN201710605510A CN107403004B CN 107403004 B CN107403004 B CN 107403004B CN 201710605510 A CN201710605510 A CN 201710605510A CN 107403004 B CN107403004 B CN 107403004B
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邱超
吴宏海
王威
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Abstract

The invention discloses a method for remotely detecting suspicious numerical values of rainfall sites based on topographic data. Firstly, establishing a spatial weight matrix for real-time rainfall station data in an area according to a geographical space coordinate distance; then, performing spatial autocorrelation modeling analysis on each site according to the rainfall attribute values of the sites, and judging whether the attribute values are clustered or not according to clustering statistical parameters; secondly, on the basis of clustering, judging an index S by calculating a clustering statecdiScreening out a suspicious value; and finally, reclassifying the stations according to the digital elevation attribute of the telemetering rainfall station, and performing spatial similarity analysis on the attribute values of the suspicious station and the stations within the set distance threshold range to confirm whether the data transmitted by the telemetering rainfall station is an abnormal value or not. The invention improves the accuracy and precision of the traditional single threshold check.

Description

Remote-measuring rainfall site suspicious numerical inspection method based on terrain data
Technical Field
The invention relates to a numerical check algorithm, in particular to a rainfall station telemetering data check method based on continuous element space relation and slope direction check.
Background
Precipitation plays an important role in the fields of hydrology, meteorology, ecology, agricultural research and the like, and particularly, rainfall data has important fundamental significance in the fields of disaster prediction, flood control and early warning and the like when a big data era comes. Meanwhile, precipitation is an important environmental factor and is also a research hotspot in the field of natural science; the accuracy and precision of the rainfall must therefore be effectively verified. The ground rainfall telemetry station is a rainfall measurement means which is widely applied, and is also a main rainfall data acquisition mode of the current hydrological application department. However, due to the failure and damage of the telemetering equipment and other unnatural reasons, the rainfall data is often misinformed or lost, and in order to improve the conventional data verification method which only depends on threshold range verification, the invention provides a telemetering rainfall site suspicious numerical inspection algorithm based on topographic data.
Disclosure of Invention
The invention aims to solve the problems in the existing rainfall verification, improve the conventional data verification method which only depends on the threshold range verification, and provide a method for remotely detecting the suspicious numerical value of a rainfall site based on topographic data.
The data verification method of the invention integrates a continuous element space correlation relation model, and discriminates an index S by calculating a clustering state on the basis of clusteringcdiAnd screening out suspicious values. And finally, reclassifying the stations according to the digital elevation attribute of the telemetering rainfall station, and performing spatial similarity analysis on the attribute values of the suspicious station and the stations within the set distance threshold range to confirm whether the data transmitted by the telemetering rainfall station is an abnormal value or not, so that the accuracy and precision of the traditional single threshold verification are improved.
The specific technical scheme of the invention is as follows:
a remote metering rainfall site suspicious numerical inspection method based on terrain data comprises the following steps:
s1: acquiring rainfall monitoring data of a telemetering rainfall station at a certain time point of a target area, spatial coordinate data of the telemetering rainfall station and DEM topographic data;
s2: selecting a threshold distance, performing space relation conceptualization processing on the space coordinate data to generate a space distance weight matrix, extracting a slope factor from DEM topographic data and performing binarization assignment processing;
s3: according to the spatial distance weight matrix, performing spatial autocorrelation modeling analysis on the rainfall attribute value of the telemetering rainfall station, and judging the clustering state of the telemetering rainfall station according to a modeling result; on the basis of clustering, the difference analysis is carried out on the rainfall attribute value of each telemetering rainfall site to judge suspicious data;
s4: and in the set distance threshold range, performing comparison analysis on the slope attribute values of the station with the suspicious data in the step S3 and the surrounding adjacent telemetering rainfall stations, and finally judging whether the station is abnormal data or not.
Preferably, the time resolution of the rainfall station data in S1 is 1 hour; the spatial resolution of the DEM terrain data is 30 meters.
Preferably, in S2, when the spatial relationship conceptualization processing is performed on the spatial coordinate data, the selection of the threshold distance requires that each element has a plurality of adjacent elements according to the rainfall station density and the model calculation requirement, so as to generate a spatial distance weight matrix.
Preferably, in S2, the DEM data is sloped by accessing each pixel in the input grid using a moving 3 × 3 window, and the slope value of each pixel located at the center of the window is calculated by a surface fitting method.
Preferably, in S3, the specific method of performing the spatial autocorrelation modeling analysis on the data of each station is as follows:
spatial autocorrelation index C of ith telemetering rainfall stationiThe calculation formula of (2):
Figure GDA0002479967230000021
wherein XiIs the rainfall attribute value for the ith telemetry rainfall station,
Figure GDA0002479967230000022
is the average of all station rainfall, SiRoot mean square, w, of rain values for all stationsijIs the space distance weight of the adjacent station j, n is equal to the total number of stations; wherein:
Figure GDA0002479967230000023
Ziis a statistical significance measure used to determine whether a null hypothesis can be rejected, and has the formula:
Figure GDA0002479967230000024
wherein Eiσ is the standard deviation for the expected value;
suspicious data discrimination index ScdiThe calculation formula is as follows:
Figure GDA0002479967230000031
wherein m is the number of adjacent stations with station i within a set distance threshold range, N is the total number of rainfall stations, WijIs the distance weight for the station j,
Figure GDA0002479967230000032
the average value of the rainfall of all stations is obtained;
then respectively calculating C of each telemetering rainfall stationi、Scdi、ZiAt 0.95 confidence level, if C of an elementi>0 and Scdi<0, judging that the station is an abnormal point with a low value and a high value; if C of the elementi<0 and Scdi<0, judging that the station is an abnormal point with a high value and a low value; the above two cases are identified as suspicious data points.
Preferably, in S4, the attribute data of the remote rainfall station and the attribute data of the slope in S2 are subjected to superposition analysis, and the slope factor is extracted into the attribute of the corresponding remote rainfall station based on the spatial coordinates of the remote rainfall station; and classifying the telemetering rainfall site data according to the slope attribute data, and by reference comparison, if a slope adjacent telemetering rainfall site which is located at the same position as the site with the suspicious data exists within a threshold distance, proving that the point is an abnormal point, and finishing the verification of abnormal point data.
The method is based on the continuous element space relationship and slope verification to confirm whether the data transmitted from the remote rainfall station is an abnormal value or not, and improves the accuracy and precision of the traditional single threshold verification.
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FIG. 1 is a schematic diagram of distribution positions of telemetering rainfall stations in the embodiment;
FIG. 2 is a diagram of clustering and outlier distribution in an example embodiment;
FIG. 3 is a schematic diagram of a slope classification and buffer in an embodiment;
fig. 4 is a schematic diagram of a fault point position in the embodiment.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
Firstly, establishing a spatial weight matrix for real-time rainfall station data in an area according to a geographical space coordinate distance; then, performing spatial autocorrelation modeling analysis on each site according to the rainfall attribute values of the sites, and judging whether the attribute values are clustered or not according to clustering statistical parameters; secondly, on the basis of clustering, judging an index S by calculating a clustering statecdiScreening out a suspicious value; and finally, reclassifying the stations according to the digital elevation attribute of the telemetering rainfall station, and performing spatial similarity analysis on the attribute values of the suspicious station and the stations within the set distance threshold range to confirm whether the data transmitted by the telemetering rainfall station is an abnormal value or not. The following specifically describes the implementation process of the present invention by selecting the Yandang mountain area in Wenzhou, Zhejiang province as the target area.
The Yandangshan mountain in the Wenzhou city belongs to the middle and low mountains and hilly areas in the southeast of Zhejiang, the altitude is generally 500-600 meters, the altitude of the peak of the Baigang tip is 1056.5 meters, the Yandangshan mountain belongs to subtropical marine climate, the rainfall is abundant, the seasonal rainfall is obvious, the plum rain season in the early summer is 5-6 months, the overcast rain is continuous, the rainfall accounts for 26-28% of the whole year, the typhoon rain or heavy rainstorm is influenced in 7-9 months, the remote measurement stations are mostly arranged in the mountainous areas and are uniformly distributed (the distribution condition is shown in figure 1), the failure rate of station data is high, and the area is selected as a research area and has typicality. Real-time rainfall data of a plurality of time nodes are randomly captured from a telemetering database in 2015-year wet season (5-10 months per year) of the Yandang mountain area, model calculation of the algorithm is carried out after preprocessing, and finally fault data are screened out from suspicious data through relevant parameter analysis and intervention of terrain factors, so that the accuracy of the rainfall telemetering data is improved.
The first step is as follows: firstly, telemetering rainfall station rainfall monitoring data, telemetering rainfall station space coordinate data and DEM topographic data of the area are obtained, the rainfall station data in the embodiment is real-time telemetering data in the Yandang region in Zhejiang province, and the time resolution is 1 hour; and the same-region DEM data spatial resolution is 30 meters.
The second step is that: and (3) performing space relation conceptualization processing on the space coordinate data in the step one, wherein a threshold distance is selected to be 5km according to the rainfall site density so as to ensure that each element has a plurality of adjacent elements, and generating a space weight matrix. And extracting slope factors from DEM topographic data and carrying out binarization assignment processing. The DEM slope direction treatment process comprises the following steps: and accessing each pixel in the input grid by adopting a moving 3x 3 window, and calculating the slope value of the pixel positioned in the center of the window every time by using a fitted surface method to incorporate the values of eight adjacent pixels into an algorithm to obtain the slope value of the center pixel.
The third step: according to the spatial distance weight matrix obtained in the last step, carrying out spatial autocorrelation modeling analysis on the rainfall attribute value of the telemetering rainfall site, wherein the analysis method comprises the following steps:
spatial autocorrelation index C of ith telemetering rainfall stationiThe calculation formula of (2):
Figure GDA0002479967230000041
wherein XiIs the rainfall attribute value for the ith telemetry rainfall station,
Figure GDA0002479967230000042
is the average of all station rainfall, SiRoot mean square, w, of rain values for all stationsijIs the space distance weight of the adjacent station j, n is equal to the total number of stations; wherein:
Figure GDA0002479967230000051
Ziis a statistical significance measure used to determine whether a null hypothesis can be rejected, and has the formula:
Figure GDA0002479967230000052
wherein Eiσ is the standard deviation for the expected value;
suspicious data discrimination index ScdiThe calculation formula is as follows:
Figure GDA0002479967230000053
wherein m is the number of adjacent stations with station i within a set distance threshold range, N is the total number of rainfall stations, WijIs the distance weight for the station j,
Figure GDA0002479967230000054
the average value of the rainfall of all stations is obtained;
then, the clustering state of the telemetering rainfall sites is judged according to the modeling result, and on the basis of clustering, the suspicious data is judged by performing difference analysis on the rainfall attribute value of each telemetering rainfall site. The specific distinguishing process is as follows:
c for respectively calculating each telemetering rainfall station by carrying out data calculation on each stationi、Scdi、ZiAt 0.95 confidence level (at this time | Z |)i|>1.96) if C of an elementi>0 and Scdi<0, judging that the station is an abnormal point with a low value and a high value; if C of the elementi<0 and Scdi<0, judging that the station is an abnormal point with a high value and a low value; the above two cases are identified as suspicious data points. As shown in fig. 2, several suspect data points are screened.
The fourth step: and in the range of the set distance threshold value, carrying out comparison analysis on the slope attribute value of the station with the suspicious data in the previous step and the surrounding adjacent telemetering rainfall stations, and finally judging whether the station is abnormal data.
The specific method comprises the following steps:
performing superposition analysis processing on the slope data in the step 2 and attribute data of the telemetering rainfall station, and extracting a slope factor into the attribute of the corresponding telemetering rainfall station based on the spatial coordinate of the telemetering rainfall station; and carrying out slope classification on the telemetering rainfall site data according to the slope data. As shown in fig. 3, the white area is a windward slope, the brown area is a leeward slope, and the suspicious data points are respectively processed by buffer areas with a threshold distance of 5 km.
Since the mountainous area has uneven terrain and is located at the seaside and affected by factors such as high-altitude temperature change, wind field deformation and the like, in order to eliminate the influence of the factors, a slope-direction adjacent station which is located at the same position as the station with suspicious data exists in the buffer area distance through reference comparison, and the abnormal point data check is completed. The data points are fault data as shown in fig. 4.
The method improves the conventional data verification method which only depends on the threshold range verification, and improves the accuracy and precision of the traditional single threshold verification.
The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention. Various changes and modifications may be made by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present invention. Therefore, the technical scheme obtained by adopting the mode of equivalent replacement or equivalent transformation is within the protection scope of the invention.

Claims (5)

1. A telemetering rainfall site suspicious numerical inspection method based on topographic data is characterized by comprising the following steps:
s1: acquiring rainfall monitoring data of a telemetering rainfall station at a certain time point of a target area, spatial coordinate data of the telemetering rainfall station and DEM topographic data;
s2: selecting a threshold distance, performing space relation conceptualization processing on the space coordinate data to generate a space distance weight matrix, extracting a slope factor from DEM topographic data and performing binarization assignment processing;
s3: according to the spatial distance weight matrix, performing spatial autocorrelation modeling analysis on the rainfall attribute value of the telemetering rainfall station, and judging the clustering state of the telemetering rainfall station according to a modeling result; on the basis of clustering, the difference analysis is carried out on the rainfall attribute value of each telemetering rainfall site to judge suspicious data;
the specific method for carrying out the spatial autocorrelation modeling analysis on the data of each station comprises the following steps:
spatial autocorrelation index C of ith telemetering rainfall stationiThe calculation formula of (2):
Figure FDA0002479967220000011
wherein XiIs the rainfall attribute value for the ith telemetry rainfall station,
Figure FDA0002479967220000012
is the average of all station rainfall, SiRoot mean square, w, of rain values for all stationsijIs the space distance weight of the adjacent station j, n is equal to the total number of stations; wherein:
Figure FDA0002479967220000013
Ziis a statistical significance measure used to determine whether a null hypothesis can be rejected, and has the formula:
Figure FDA0002479967220000014
wherein Eiσ is the standard deviation for the expected value;
suspicious data discrimination index ScdiThe calculation formula is as follows:
Figure FDA0002479967220000015
wherein m is the number of adjacent stations with station i within a set distance threshold range, N is the total number of rainfall stations, wijIs the spatial distance weight of the neighboring station j,
Figure FDA0002479967220000016
the average value of the rainfall of all stations is obtained;
then respectively calculating C of each telemetering rainfall stationi、Scdi、ZiAt 0.95 confidence level, if C of an elementi>0 and Scdi<0, judging that the station is an abnormal point with a low value and a high value; if C of the elementi<0 and Scdi<0, judging that the station is an abnormal point with a high value and a low value; the two situations are judged as suspicious data points;
s4: and in the set distance threshold range, performing comparison analysis on the slope attribute values of the station with the suspicious data in the step S3 and the surrounding adjacent telemetering rainfall stations, and finally judging whether the station is abnormal data or not.
2. A method for remotely measuring a suspicious numerical test of a rainfall site of the topographic data as set forth in claim 1, wherein the time resolution of the rainfall site data in S1 is 1 hour; the spatial resolution of the DEM terrain data is 30 meters.
3. The method for remotely testing suspicious numerical verification of a rainfall site of terrain data according to claim 1, wherein in step S2, when the spatial coordinate data is conceptualized in spatial relationship, the threshold distance is selected according to the rainfall site density and the model calculation requirement to ensure that each element has a plurality of adjacent elements so as to generate the spatial distance weight matrix.
4. The method of claim 1, wherein in step S2, the DEM data is processed in a sloping direction by accessing each pixel in the input grid using a moving 3 × 3 window, and the slope value of each pixel in the center of the window is calculated by fitting a surface method.
5. The method for remotely testing suspicious numerical control of rainfall site of the topographic data as set forth in claim 1, wherein in S4, the slope attribute data in S2 and the attribute data of the telemetric rainfall site are subjected to superposition analysis, and the slope factor is extracted into the attribute of the corresponding telemetric rainfall site based on the spatial coordinates of the telemetric rainfall site; and classifying the telemetering rainfall site data according to the slope attribute data, and by reference comparison, if a slope adjacent telemetering rainfall site which is located at the same position as the site with the suspicious data exists within a threshold distance, proving that the point is an abnormal point, and finishing the verification of abnormal point data.
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