Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory72.3 B

Variable types

Categorical1
Numeric7

Alerts

저수위(m) is highly overall correlated with 댐이름High correlation
유입량(ms) is highly overall correlated with 방류량(ms) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
강우량(mm) has 90 (90.0%) zerosZeros
유입량(ms) has 14 (14.0%) zerosZeros
방류량(ms) has 16 (16.0%) zerosZeros
저수율 has 16 (16.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:22:26.397127
Analysis finished2023-12-10 13:22:34.748995
Duration8.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
군남
28 
한탄강
28 
평화의댐
28 
여주저류지
16 

Length

Max length5
Median length4
Mean length3.32
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군남 28
28.0%
한탄강 28
28.0%
평화의댐 28
28.0%
여주저류지 16
16.0%

Length

2023-12-10T22:22:34.864074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:35.022317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 28
28.0%
한탄강 28
28.0%
평화의댐 28
28.0%
여주저류지 16
16.0%

일자/시간(t)
Real number (ℝ)

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190215
Minimum20190201
Maximum20190228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:35.544829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190201
5-th percentile20190202
Q120190208
median20190215
Q320190221
95-th percentile20190227
Maximum20190228
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.035678
Coefficient of variation (CV)3.9799864 × 10-7
Kurtosis-1.2111062
Mean20190215
Median Absolute Deviation (MAD)7
Skewness-0.052981739
Sum2.0190215 × 109
Variance64.572121
MonotonicityNot monotonic
2023-12-10T22:22:35.732858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20190221 4
 
4.0%
20190214 4
 
4.0%
20190225 4
 
4.0%
20190209 4
 
4.0%
20190215 4
 
4.0%
20190223 4
 
4.0%
20190202 4
 
4.0%
20190217 4
 
4.0%
20190206 4
 
4.0%
20190205 4
 
4.0%
Other values (18) 60
60.0%
ValueCountFrequency (%)
20190201 3
3.0%
20190202 4
4.0%
20190203 4
4.0%
20190204 3
3.0%
20190205 4
4.0%
20190206 4
4.0%
20190207 3
3.0%
20190208 3
3.0%
20190209 4
4.0%
20190210 3
3.0%
ValueCountFrequency (%)
20190228 3
3.0%
20190227 3
3.0%
20190226 3
3.0%
20190225 4
4.0%
20190224 4
4.0%
20190223 4
4.0%
20190222 4
4.0%
20190221 4
4.0%
20190220 4
4.0%
20190219 4
4.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.2974
Minimum25.58
Maximum170.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:35.936537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.58
5-th percentile25.6395
Q125.72
median46.99
Q3167.605
95-th percentile170.311
Maximum170.43
Range144.85
Interquartile range (IQR)141.885

Descriptive statistics

Standard deviation61.292261
Coefficient of variation (CV)0.84777961
Kurtosis-1.0655408
Mean72.2974
Median Absolute Deviation (MAD)21.27
Skewness0.9312189
Sum7229.74
Variance3756.7413
MonotonicityNot monotonic
2023-12-10T22:22:36.272372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.72 5
 
5.0%
28.55 5
 
5.0%
28.54 4
 
4.0%
25.68 3
 
3.0%
25.64 3
 
3.0%
25.71 2
 
2.0%
47.22 2
 
2.0%
46.87 2
 
2.0%
47.28 2
 
2.0%
28.37 2
 
2.0%
Other values (62) 70
70.0%
ValueCountFrequency (%)
25.58 2
2.0%
25.6 1
 
1.0%
25.63 2
2.0%
25.64 3
3.0%
25.65 1
 
1.0%
25.66 2
2.0%
25.67 1
 
1.0%
25.68 3
3.0%
25.69 2
2.0%
25.7 2
2.0%
ValueCountFrequency (%)
170.43 1
1.0%
170.41 1
1.0%
170.39 1
1.0%
170.37 1
1.0%
170.33 1
1.0%
170.31 1
1.0%
170.24 1
1.0%
170.06 1
1.0%
170.04 1
1.0%
170.03 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.560894
Minimum0
Maximum13.8587
Zeros90
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:36.629140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.01578
Maximum13.8587
Range13.8587
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3112572
Coefficient of variation (CV)4.1206666
Kurtosis24.301032
Mean0.560894
Median Absolute Deviation (MAD)0
Skewness4.9155394
Sum56.0894
Variance5.3419098
MonotonicityNot monotonic
2023-12-10T22:22:36.942343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 90
90.0%
1.0 2
 
2.0%
13.0 1
 
1.0%
3.0 1
 
1.0%
2.5 1
 
1.0%
3.3156 1
 
1.0%
1.4151 1
 
1.0%
13.8587 1
 
1.0%
5.0 1
 
1.0%
12.0 1
 
1.0%
ValueCountFrequency (%)
0.0 90
90.0%
1.0 2
 
2.0%
1.4151 1
 
1.0%
2.5 1
 
1.0%
3.0 1
 
1.0%
3.3156 1
 
1.0%
5.0 1
 
1.0%
12.0 1
 
1.0%
13.0 1
 
1.0%
13.8587 1
 
1.0%
ValueCountFrequency (%)
13.8587 1
 
1.0%
13.0 1
 
1.0%
12.0 1
 
1.0%
5.0 1
 
1.0%
3.3156 1
 
1.0%
3.0 1
 
1.0%
2.5 1
 
1.0%
1.4151 1
 
1.0%
1.0 2
 
2.0%
0.0 90
90.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0369
Minimum0
Maximum24.14
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:37.223333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.23175
median10.3835
Q313.3115
95-th percentile19.81505
Maximum24.14
Range24.14
Interquartile range (IQR)9.07975

Descriptive statistics

Standard deviation6.3721797
Coefficient of variation (CV)0.70512894
Kurtosis-0.83387719
Mean9.0369
Median Absolute Deviation (MAD)5.656
Skewness0.22832039
Sum903.69
Variance40.604674
MonotonicityNot monotonic
2023-12-10T22:22:37.625484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
13.208 1
 
1.0%
5.006 1
 
1.0%
8.923 1
 
1.0%
12.907 1
 
1.0%
24.14 1
 
1.0%
12.555 1
 
1.0%
10.917 1
 
1.0%
21.023 1
 
1.0%
15.217 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.023 1
 
1.0%
0.116 1
 
1.0%
3.454 1
 
1.0%
3.544 1
 
1.0%
3.661 1
 
1.0%
3.72 1
 
1.0%
3.794 1
 
1.0%
3.959 1
 
1.0%
4.004 1
 
1.0%
ValueCountFrequency (%)
24.14 1
1.0%
22.671 1
1.0%
22.426 1
1.0%
21.023 1
1.0%
19.968 1
1.0%
19.807 1
1.0%
19.277 1
1.0%
18.738 1
1.0%
18.39 1
1.0%
17.486 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.80575
Minimum0
Maximum23.735
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:37.851841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.18725
median10.547
Q314.76925
95-th percentile21.88535
Maximum23.735
Range23.735
Interquartile range (IQR)10.582

Descriptive statistics

Standard deviation7.2124224
Coefficient of variation (CV)0.73552991
Kurtosis-1.1123011
Mean9.80575
Median Absolute Deviation (MAD)5.999
Skewness0.25615862
Sum980.575
Variance52.019036
MonotonicityNot monotonic
2023-12-10T22:22:38.074740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
4.592 2
 
2.0%
13.358 1
 
1.0%
4.659 1
 
1.0%
17.594 1
 
1.0%
23.735 1
 
1.0%
12.011 1
 
1.0%
15.79 1
 
1.0%
20.78 1
 
1.0%
14.661 1
 
1.0%
Other values (74) 74
74.0%
ValueCountFrequency (%)
0.0 16
16.0%
3.597 1
 
1.0%
3.639 1
 
1.0%
3.73 1
 
1.0%
3.739 1
 
1.0%
3.744 1
 
1.0%
3.752 1
 
1.0%
3.755 1
 
1.0%
3.969 1
 
1.0%
4.164 1
 
1.0%
ValueCountFrequency (%)
23.735 1
1.0%
23.09 1
1.0%
22.923 1
1.0%
22.314 1
1.0%
22.139 1
1.0%
21.872 1
1.0%
21.741 1
1.0%
21.326 1
1.0%
21.312 1
1.0%
21.003 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.81786
Minimum0.009
Maximum12.336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:38.344973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.01
Q10.47025
median3.505
Q36.595
95-th percentile12.05435
Maximum12.336
Range12.327
Interquartile range (IQR)6.12475

Descriptive statistics

Standard deviation4.0287031
Coefficient of variation (CV)1.0552255
Kurtosis-0.5189947
Mean3.81786
Median Absolute Deviation (MAD)3.0355
Skewness0.88665045
Sum381.786
Variance16.230449
MonotonicityNot monotonic
2023-12-10T22:22:38.592148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.608 5
 
5.0%
0.019 5
 
5.0%
0.017 4
 
4.0%
0.009 4
 
4.0%
3.557 3
 
3.0%
3.505 3
 
3.0%
3.595 2
 
2.0%
0.01 2
 
2.0%
0.442 2
 
2.0%
0.503 2
 
2.0%
Other values (59) 68
68.0%
ValueCountFrequency (%)
0.009 4
4.0%
0.01 2
 
2.0%
0.016 1
 
1.0%
0.017 4
4.0%
0.019 5
5.0%
0.442 2
 
2.0%
0.449 1
 
1.0%
0.45 1
 
1.0%
0.453 1
 
1.0%
0.456 1
 
1.0%
ValueCountFrequency (%)
12.336 1
1.0%
12.288 1
1.0%
12.241 1
1.0%
12.194 1
1.0%
12.099 1
1.0%
12.052 1
1.0%
11.889 1
1.0%
11.473 1
1.0%
11.427 1
1.0%
11.404 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.552
Minimum0
Maximum5.2
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:38.821232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median0.3
Q34.825
95-th percentile5
Maximum5.2
Range5.2
Interquartile range (IQR)4.625

Descriptive statistics

Standard deviation2.1432761
Coefficient of variation (CV)1.3809769
Kurtosis-1.0308737
Mean1.552
Median Absolute Deviation (MAD)0.2
Skewness0.9840254
Sum155.2
Variance4.5936323
MonotonicityNot monotonic
2023-12-10T22:22:38.999177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.2 30
30.0%
0.0 16
16.0%
5.0 15
15.0%
0.4 10
 
10.0%
0.3 9
 
9.0%
4.9 8
 
8.0%
0.5 7
 
7.0%
4.8 3
 
3.0%
5.2 2
 
2.0%
ValueCountFrequency (%)
0.0 16
16.0%
0.2 30
30.0%
0.3 9
 
9.0%
0.4 10
 
10.0%
0.5 7
 
7.0%
4.8 3
 
3.0%
4.9 8
 
8.0%
5.0 15
15.0%
5.2 2
 
2.0%
ValueCountFrequency (%)
5.2 2
 
2.0%
5.0 15
15.0%
4.9 8
 
8.0%
4.8 3
 
3.0%
0.5 7
 
7.0%
0.4 10
 
10.0%
0.3 9
 
9.0%
0.2 30
30.0%
0.0 16
16.0%

Interactions

2023-12-10T22:22:33.451385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:26.805071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:28.010851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.136197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:30.307240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:31.369961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:32.370420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:33.593285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:26.991682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:28.150887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.290816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:30.547186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:31.494873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:32.517162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:33.724939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.144085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:28.298476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.460617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:30.724285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:31.635857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:32.733005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:33.857648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.295369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:28.443496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.636345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:30.862813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:31.759247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:32.894686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:33.984747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.419939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:28.625251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.783978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:31.013315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:31.911200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:33.050973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:34.120788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.562195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:28.818383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.924645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:31.127007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:32.066428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:33.184105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:34.288090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.847861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:28.983135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:30.095266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:31.249147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:32.215893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:33.317281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:22:39.194019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.9510.9870.9781.000
일자/시간(t)0.0001.0000.0000.5380.0000.0000.3680.000
저수위(m)1.0000.0001.0000.0000.8970.9430.8640.449
강우량(mm)0.0000.5380.0001.0000.2860.0960.2520.000
유입량(ms)0.9510.0000.8970.2861.0000.9390.7690.887
방류량(ms)0.9870.0000.9430.0960.9391.0000.7840.980
저수량(백만m3)0.9780.3680.8640.2520.7690.7841.0001.000
저수율1.0000.0000.4490.0000.8870.9801.0001.000
2023-12-10T22:22:39.479232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.017-0.1210.0600.082-0.024-0.0930.000
저수위(m)-0.0171.0000.0160.3170.3800.467-0.2510.995
강우량(mm)-0.1210.0161.0000.1320.1080.1180.1500.000
유입량(ms)0.0600.3170.1321.0000.9650.9250.7230.850
방류량(ms)0.0820.3800.1080.9651.0000.9340.6670.913
저수량(백만m3)-0.0240.4670.1180.9250.9341.0000.7030.784
저수율-0.093-0.2510.1500.7230.6670.7031.0000.990
댐이름0.0000.9950.0000.8500.9130.7840.9901.000

Missing values

2023-12-10T22:22:34.479842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:22:34.669027image/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

댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
0군남2019021825.710.013.20813.3583.5955.0
1군남2019020225.680.011.00110.73.5575.0
2군남2019022025.720.014.8914.893.6085.0
3군남2019022225.680.011.10311.2533.5575.0
4군남2019021325.630.010.25910.3983.4934.9
5군남2019021025.630.010.49210.0523.4934.9
6군남2019020325.7113.011.72411.2843.5955.0
7군남2019021925.723.014.32214.1723.6085.0
8군남2019020825.780.013.12211.1783.6865.2
9군남2019021425.580.010.53111.2723.4294.8
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90여주저류지2019022428.540.00.00.00.0170.0
91여주저류지2019020328.360.00.00.00.0090.0
92여주저류지2019022328.540.00.00.00.0170.0
93여주저류지2019020528.370.00.00.00.010.0
94여주저류지2019021928.550.00.0230.00.0190.0
95여주저류지2019020928.350.00.00.00.0090.0
96여주저류지2019021528.540.00.00.00.0170.0
97여주저류지2019021428.550.00.1160.00.0190.0
98여주저류지2019022228.550.00.00.00.0190.0
99여주저류지2019022128.550.00.00.00.0190.0