Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory71.3 B

Variable types

Categorical2
Numeric6

Alerts

관측소명 is highly overall correlated with 당년 누적 강우량(mm) and 3 other fieldsHigh correlation
댐명 is highly overall correlated with 당년 누적 강우량(mm) and 3 other fieldsHigh correlation
당년 누적 강우량(mm) is highly overall correlated with 전년 누적 강우량(mm) and 3 other fieldsHigh correlation
전년 누적 강우량(mm) is highly overall correlated with 당년 누적 강우량(mm) and 3 other fieldsHigh correlation
예년 평균 누적 강우량(mm) is highly overall correlated with 당년 누적 강우량(mm) and 3 other fieldsHigh correlation
강우량(mm) has 68 (68.0%) zerosZeros
전년 강우량(mm) has 74 (74.0%) zerosZeros
전년 누적 강우량(mm) has 17 (17.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:59:15.740038
Analysis finished2023-12-10 13:59:23.196751
Duration7.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
군남
21 
광동
19 
감포
17 
괴산
17 
구천
16 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row감포
2nd row감포
3rd row감포
4th row감포
5th row감포

Common Values

ValueCountFrequency (%)
군남 21
21.0%
광동 19
19.0%
감포 17
17.0%
괴산 17
17.0%
구천 16
16.0%
군위 10
10.0%

Length

2023-12-10T22:59:23.343284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:59:23.553397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 21
21.0%
광동 19
19.0%
감포 17
17.0%
괴산 17
17.0%
구천 16
16.0%
군위 10
10.0%

관측소명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경주시(감포댐)
17 
거제시(구천댐)
16 
연천군(군남댐)
11 
태백시(상사미동)
10 
연천군(횡산리)
10 
Other values (5)
36 

Length

Max length9
Median length8
Mean length7.92
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경주시(감포댐)
2nd row경주시(감포댐)
3rd row경주시(감포댐)
4th row경주시(감포댐)
5th row경주시(감포댐)

Common Values

ValueCountFrequency (%)
경주시(감포댐) 17
17.0%
거제시(구천댐) 16
16.0%
연천군(군남댐) 11
11.0%
태백시(상사미동) 10
10.0%
연천군(횡산리) 10
10.0%
삼척시(광동댐) 9
9.0%
내속리(전) 9
9.0%
괴산댐(FTP) 8
8.0%
군위군(군위댐) 6
 
6.0%
군위군(양지리) 4
 
4.0%

Length

2023-12-10T22:59:23.760028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:59:24.018334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경주시(감포댐 17
17.0%
거제시(구천댐 16
16.0%
연천군(군남댐 11
11.0%
태백시(상사미동 10
10.0%
연천군(횡산리 10
10.0%
삼척시(광동댐 9
9.0%
내속리(전 9
9.0%
괴산댐(ftp 8
8.0%
군위군(군위댐 6
 
6.0%
군위군(양지리 4
 
4.0%

관측일
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190616
Minimum20190602
Maximum20190630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:24.248238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190602
5-th percentile20190603
Q120190609
median20190616
Q320190623
95-th percentile20190628
Maximum20190630
Range28
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.5151321
Coefficient of variation (CV)4.2173712 × 10-7
Kurtosis-1.2584571
Mean20190616
Median Absolute Deviation (MAD)7.5
Skewness0.008375713
Sum2.0190616 × 109
Variance72.507475
MonotonicityNot monotonic
2023-12-10T22:59:24.499439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20190610 6
 
6.0%
20190602 5
 
5.0%
20190628 5
 
5.0%
20190627 5
 
5.0%
20190612 5
 
5.0%
20190622 5
 
5.0%
20190614 4
 
4.0%
20190625 4
 
4.0%
20190621 4
 
4.0%
20190606 4
 
4.0%
Other values (19) 53
53.0%
ValueCountFrequency (%)
20190602 5
5.0%
20190603 4
4.0%
20190604 2
 
2.0%
20190605 3
3.0%
20190606 4
4.0%
20190607 3
3.0%
20190608 4
4.0%
20190609 3
3.0%
20190610 6
6.0%
20190611 3
3.0%
ValueCountFrequency (%)
20190630 2
 
2.0%
20190629 3
3.0%
20190628 5
5.0%
20190627 5
5.0%
20190626 2
 
2.0%
20190625 4
4.0%
20190624 4
4.0%
20190623 1
 
1.0%
20190622 5
5.0%
20190621 4
4.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.63
Minimum0
Maximum86
Zeros68
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:24.751554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5
95-th percentile36.1
Maximum86
Range86
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation15.455805
Coefficient of variation (CV)2.7452585
Kurtosis16.769883
Mean5.63
Median Absolute Deviation (MAD)0
Skewness3.9798052
Sum563
Variance238.88192
MonotonicityNot monotonic
2023-12-10T22:59:24.959043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 68
68.0%
4.0 4
 
4.0%
10.0 3
 
3.0%
1.0 3
 
3.0%
8.0 3
 
3.0%
11.0 2
 
2.0%
2.0 2
 
2.0%
19.0 2
 
2.0%
0.5 2
 
2.0%
86.0 1
 
1.0%
Other values (10) 10
 
10.0%
ValueCountFrequency (%)
0.0 68
68.0%
0.5 2
 
2.0%
1.0 3
 
3.0%
2.0 2
 
2.0%
4.0 4
 
4.0%
7.0 1
 
1.0%
8.0 3
 
3.0%
10.0 3
 
3.0%
11.0 2
 
2.0%
11.5 1
 
1.0%
ValueCountFrequency (%)
86.0 1
1.0%
85.0 1
1.0%
73.0 1
1.0%
40.5 1
1.0%
38.0 1
1.0%
36.0 1
1.0%
19.0 2
2.0%
18.0 1
1.0%
16.0 1
1.0%
14.0 1
1.0%

전년 강우량(mm)
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.44
Minimum0
Maximum138
Zeros74
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:25.124684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.625
95-th percentile21.15
Maximum138
Range138
Interquartile range (IQR)0.625

Descriptive statistics

Standard deviation16.807478
Coefficient of variation (CV)3.785468
Kurtosis43.575747
Mean4.44
Median Absolute Deviation (MAD)0
Skewness6.1436434
Sum444
Variance282.49131
MonotonicityNot monotonic
2023-12-10T22:59:25.302750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 74
74.0%
1.0 9
 
9.0%
2.0 2
 
2.0%
13.0 2
 
2.0%
0.5 1
 
1.0%
19.5 1
 
1.0%
4.0 1
 
1.0%
21.0 1
 
1.0%
39.0 1
 
1.0%
34.5 1
 
1.0%
Other values (7) 7
 
7.0%
ValueCountFrequency (%)
0.0 74
74.0%
0.5 1
 
1.0%
1.0 9
 
9.0%
2.0 2
 
2.0%
4.0 1
 
1.0%
7.0 1
 
1.0%
11.0 1
 
1.0%
13.0 2
 
2.0%
14.0 1
 
1.0%
16.5 1
 
1.0%
ValueCountFrequency (%)
138.0 1
1.0%
76.0 1
1.0%
39.0 1
1.0%
34.5 1
1.0%
24.0 1
1.0%
21.0 1
1.0%
19.5 1
1.0%
16.5 1
1.0%
14.0 1
1.0%
13.0 2
2.0%

당년 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306.235
Minimum86
Maximum957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:25.527450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile111.6
Q1177.25
median272.9
Q3354
95-th percentile710
Maximum957
Range871
Interquartile range (IQR)176.75

Descriptive statistics

Standard deviation195.50061
Coefficient of variation (CV)0.6384006
Kurtosis1.2958543
Mean306.235
Median Absolute Deviation (MAD)95.75
Skewness1.4023819
Sum30623.5
Variance38220.487
MonotonicityNot monotonic
2023-12-10T22:59:25.855563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
710.0 6
 
6.0%
116.0 5
 
5.0%
374.5 4
 
4.0%
275.5 4
 
4.0%
255.5 4
 
4.0%
564.0 3
 
3.0%
157.0 3
 
3.0%
179.0 3
 
3.0%
126.0 3
 
3.0%
277.0 3
 
3.0%
Other values (44) 62
62.0%
ValueCountFrequency (%)
86.0 2
 
2.0%
90.0 2
 
2.0%
104.0 1
 
1.0%
112.0 1
 
1.0%
114.0 1
 
1.0%
116.0 5
5.0%
126.0 3
3.0%
132.0 1
 
1.0%
140.0 3
3.0%
157.0 3
3.0%
ValueCountFrequency (%)
957.0 1
 
1.0%
871.0 1
 
1.0%
710.0 6
6.0%
709.0 1
 
1.0%
699.0 3
3.0%
649.0 1
 
1.0%
564.0 3
3.0%
424.0 1
 
1.0%
395.5 1
 
1.0%
384.5 1
 
1.0%

전년 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean428.16
Minimum0
Maximum1104
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:26.078037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1369
median442
Q3490
95-th percentile870
Maximum1104
Range1104
Interquartile range (IQR)121

Descriptive statistics

Standard deviation253.82147
Coefficient of variation (CV)0.5928192
Kurtosis0.086675593
Mean428.16
Median Absolute Deviation (MAD)60
Skewness0.023959459
Sum42816
Variance64425.338
MonotonicityNot monotonic
2023-12-10T22:59:26.283532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 17
 
17.0%
473.0 6
 
6.0%
460.0 5
 
5.0%
490.0 5
 
5.0%
396.0 5
 
5.0%
442.0 4
 
4.0%
382.0 4
 
4.0%
861.0 4
 
4.0%
484.5 4
 
4.0%
419.0 3
 
3.0%
Other values (33) 43
43.0%
ValueCountFrequency (%)
0.0 17
17.0%
311.0 2
 
2.0%
316.0 2
 
2.0%
326.0 2
 
2.0%
329.0 1
 
1.0%
330.0 1
 
1.0%
382.0 4
 
4.0%
383.0 1
 
1.0%
384.0 1
 
1.0%
389.0 2
 
2.0%
ValueCountFrequency (%)
1104.0 1
 
1.0%
885.0 1
 
1.0%
871.0 2
2.0%
870.0 2
2.0%
861.0 4
4.0%
859.0 1
 
1.0%
783.0 1
 
1.0%
782.0 2
2.0%
774.0 2
2.0%
561.0 1
 
1.0%

예년 평균 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.237
Minimum118
Maximum540.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:26.463381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum118
5-th percentile144
Q1218.725
median301.2
Q3396.075
95-th percentile482.745
Maximum540.3
Range422.3
Interquartile range (IQR)177.35

Descriptive statistics

Standard deviation108.64687
Coefficient of variation (CV)0.35362561
Kurtosis-1.1061187
Mean307.237
Median Absolute Deviation (MAD)86.75
Skewness0.18985356
Sum30723.7
Variance11804.143
MonotonicityNot monotonic
2023-12-10T22:59:26.669066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187.0 2
 
2.0%
144.0 2
 
2.0%
340.6 1
 
1.0%
245.8 1
 
1.0%
200.9 1
 
1.0%
222.4 1
 
1.0%
213.5 1
 
1.0%
230.3 1
 
1.0%
267.1 1
 
1.0%
422.3 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
118.0 1
1.0%
118.5 1
1.0%
140.5 1
1.0%
141.5 1
1.0%
144.0 2
2.0%
145.6 1
1.0%
147.2 1
1.0%
153.5 1
1.0%
159.0 1
1.0%
187.0 2
2.0%
ValueCountFrequency (%)
540.3 1
1.0%
513.3 1
1.0%
495.6 1
1.0%
489.3 1
1.0%
485.5 1
1.0%
482.6 1
1.0%
472.2 1
1.0%
471.4 1
1.0%
462.8 1
1.0%
455.0 1
1.0%

Interactions

2023-12-10T22:59:21.939712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:16.591580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.618949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:18.971901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.295661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.211480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:22.070092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:16.776781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.749473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:19.272465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.477370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.354796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:22.228422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:16.939708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.873693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:19.499582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.624355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.472283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:22.439987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.159495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:18.008360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:19.718575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.766982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.606791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:22.575818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.340338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:18.169540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:19.901394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.931804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.704141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:22.704573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:17.497806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:18.771905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:20.069494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.063671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:21.815192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:59:26.810130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
댐명1.0001.0000.0000.2590.0810.7950.8970.793
관측소명1.0001.0000.0000.0000.0000.7910.8740.914
관측일0.0000.0001.0000.2500.3840.2250.0000.000
강우량(mm)0.2590.0000.2501.0000.4520.6120.5620.371
전년 강우량(mm)0.0810.0000.3840.4521.0000.1800.3920.304
당년 누적 강우량(mm)0.7950.7910.2250.6120.1801.0000.9280.830
전년 누적 강우량(mm)0.8970.8740.0000.5620.3920.9281.0000.829
예년 평균 누적 강우량(mm)0.7930.9140.0000.3710.3040.8300.8291.000
2023-12-10T22:59:26.999985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소명댐명
관측소명1.0000.978
댐명0.9781.000
2023-12-10T22:59:27.128344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)댐명관측소명
관측일1.0000.1720.2970.3630.2830.3070.0000.000
강우량(mm)0.1721.0000.2510.1920.1580.1470.0940.000
전년 강우량(mm)0.2970.2511.0000.2210.3650.2170.0500.000
당년 누적 강우량(mm)0.3630.1920.2211.0000.5620.8490.5950.531
전년 누적 강우량(mm)0.2830.1580.3650.5621.0000.6750.7620.663
예년 평균 누적 강우량(mm)0.3070.1470.2170.8490.6751.0000.5640.526
댐명0.0000.0940.0500.5950.7620.5641.0000.978
관측소명0.0000.0000.0000.5310.6630.5260.9781.000

Missing values

2023-12-10T22:59:22.893168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:59:23.116889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
0감포경주시(감포댐)201906020.00.0202.5460.0340.6
1감포경주시(감포댐)201906160.00.0275.0484.5363.9
2감포경주시(감포댐)201906180.50.0275.5484.5367.4
3감포경주시(감포댐)201906090.00.0255.5460.5354.1
4감포경주시(감포댐)201906050.00.0202.5460.0346.6
5감포경주시(감포댐)2019062811.034.5395.5541.0429.5
6감포경주시(감포댐)2019062736.016.5384.5506.5422.4
7감포경주시(감포댐)2019060740.50.5255.5460.5351.4
8감포경주시(감포댐)201906240.00.0275.5490.0389.3
9감포경주시(감포댐)2019062673.00.0348.5490.0417.7
댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
90군위군위군(군위댐)2019062714.0138.0270.8469.0301.3
91군위군위군(양지리)2019060710.00.0195.0311.0145.6
92군위군위군(양지리)201906170.00.0205.0326.0153.5
93군위군위군(군위댐)201906080.00.0213.0316.0246.3
94군위군위군(양지리)201906190.00.0205.0326.0159.0
95군위군위군(군위댐)201906110.013.0213.8329.0249.3
96군위군위군(군위댐)201906210.00.0214.8330.0260.0
97군위군위군(군위댐)201906100.00.0213.8316.0246.8
98군위군위군(군위댐)201906288.07.0278.8476.0303.1
99군위군위군(양지리)201906090.00.0195.0311.0147.2