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 유입량(ms) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 댐이름High correlation
댐이름 is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
강우량(mm) has 68 (68.0%) zerosZeros
유입량(ms) has 19 (19.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:11:42.087435
Analysis finished2023-12-10 14:11:51.878050
Duration9.79 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
감포
30 
광동
30 
구천
30 
달방
10 

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 (%)
감포 30
30.0%
광동 30
30.0%
구천 30
30.0%
달방 10
 
10.0%

Length

2023-12-10T23:11:52.050916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:11:52.380425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
감포 30
30.0%
광동 30
30.0%
구천 30
30.0%
달방 10
 
10.0%

일자/시간(t)
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190614
Minimum20190601
Maximum20190630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:11:52.544570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190601
5-th percentile20190602
Q120190607
median20190614
Q320190622
95-th percentile20190629
Maximum20190630
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8334763
Coefficient of variation (CV)4.3750408 × 10-7
Kurtosis-1.2329291
Mean20190614
Median Absolute Deviation (MAD)8
Skewness0.16149815
Sum2.0190614 × 109
Variance78.030303
MonotonicityNot monotonic
2023-12-10T23:11:52.767868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190601 4
 
4.0%
20190603 4
 
4.0%
20190604 4
 
4.0%
20190605 4
 
4.0%
20190606 4
 
4.0%
20190607 4
 
4.0%
20190608 4
 
4.0%
20190609 4
 
4.0%
20190610 4
 
4.0%
20190602 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20190601 4
4.0%
20190602 4
4.0%
20190603 4
4.0%
20190604 4
4.0%
20190605 4
4.0%
20190606 4
4.0%
20190607 4
4.0%
20190608 4
4.0%
20190609 4
4.0%
20190610 4
4.0%
ValueCountFrequency (%)
20190630 3
3.0%
20190629 3
3.0%
20190628 3
3.0%
20190627 3
3.0%
20190626 3
3.0%
20190625 3
3.0%
20190624 3
3.0%
20190623 3
3.0%
20190622 3
3.0%
20190621 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.6548
Minimum37.51
Maximum672.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:11:53.007485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.51
5-th percentile37.5895
Q137.815
median89.13
Q3671.5675
95-th percentile672.1405
Maximum672.36
Range634.85
Interquartile range (IQR)633.7525

Descriptive statistics

Standard deviation278.21197
Coefficient of variation (CV)1.1099407
Kurtosis-1.2472464
Mean250.6548
Median Absolute Deviation (MAD)51.365
Skewness0.86073659
Sum25065.48
Variance77401.898
MonotonicityNot monotonic
2023-12-10T23:11:53.255136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.16 3
 
3.0%
37.74 3
 
3.0%
37.83 2
 
2.0%
672.11 2
 
2.0%
89.13 2
 
2.0%
88.79 2
 
2.0%
672.12 2
 
2.0%
671.59 2
 
2.0%
672.09 2
 
2.0%
672.1 2
 
2.0%
Other values (75) 78
78.0%
ValueCountFrequency (%)
37.51 1
1.0%
37.53 1
1.0%
37.55 1
1.0%
37.57 1
1.0%
37.58 1
1.0%
37.59 1
1.0%
37.62 1
1.0%
37.63 1
1.0%
37.65 1
1.0%
37.66 1
1.0%
ValueCountFrequency (%)
672.36 1
1.0%
672.28 1
1.0%
672.22 1
1.0%
672.18 1
1.0%
672.15 1
1.0%
672.14 1
1.0%
672.12 2
2.0%
672.11 2
2.0%
672.1 2
2.0%
672.09 2
2.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.885
Minimum0
Maximum123
Zeros68
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:11:53.486704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.915125
95-th percentile73.207345
Maximum123
Range123
Interquartile range (IQR)1.915125

Descriptive statistics

Standard deviation23.262633
Coefficient of variation (CV)2.6181917
Kurtosis10.390715
Mean8.885
Median Absolute Deviation (MAD)0
Skewness3.2372415
Sum888.5
Variance541.15009
MonotonicityNot monotonic
2023-12-10T23:11:53.821857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 68
68.0%
1.0 3
 
3.0%
0.5 2
 
2.0%
3.0642 2
 
2.0%
40.5 1
 
1.0%
12.0642 1
 
1.0%
4.0 1
 
1.0%
105.0 1
 
1.0%
17.0 1
 
1.0%
86.0 1
 
1.0%
Other values (19) 19
 
19.0%
ValueCountFrequency (%)
0.0 68
68.0%
0.5 2
 
2.0%
0.5321 1
 
1.0%
1.0 3
 
3.0%
1.5321 1
 
1.0%
3.0642 2
 
2.0%
4.0 1
 
1.0%
4.1926 1
 
1.0%
7.4037 1
 
1.0%
8.0 1
 
1.0%
ValueCountFrequency (%)
123.0 1
1.0%
105.0 1
1.0%
86.0 1
1.0%
85.0 1
1.0%
77.1469 1
1.0%
73.0 1
1.0%
49.0 1
1.0%
40.5 1
1.0%
38.0 1
1.0%
36.0 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7735
Minimum0
Maximum8.409
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:11:54.204380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01425
median0.12
Q30.592
95-th percentile4.93915
Maximum8.409
Range8.409
Interquartile range (IQR)0.57775

Descriptive statistics

Standard deviation1.7195398
Coefficient of variation (CV)2.2230637
Kurtosis9.8311095
Mean0.7735
Median Absolute Deviation (MAD)0.12
Skewness3.167271
Sum77.35
Variance2.9568171
MonotonicityNot monotonic
2023-12-10T23:11:54.504799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
0.01 3
 
3.0%
0.11 2
 
2.0%
0.114 2
 
2.0%
0.026 2
 
2.0%
0.224 2
 
2.0%
0.124 2
 
2.0%
0.109 1
 
1.0%
0.573 1
 
1.0%
8.179 1
 
1.0%
Other values (65) 65
65.0%
ValueCountFrequency (%)
0.0 19
19.0%
0.002 1
 
1.0%
0.007 1
 
1.0%
0.01 3
 
3.0%
0.012 1
 
1.0%
0.015 1
 
1.0%
0.016 1
 
1.0%
0.017 1
 
1.0%
0.02 1
 
1.0%
0.023 1
 
1.0%
ValueCountFrequency (%)
8.409 1
1.0%
8.179 1
1.0%
7.742 1
1.0%
5.829 1
1.0%
5.341 1
1.0%
4.918 1
1.0%
4.901 1
1.0%
3.516 1
1.0%
2.587 1
1.0%
2.041 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42568
Minimum0.009
Maximum3.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:11:54.861766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.0268
Q10.03875
median0.3295
Q30.34225
95-th percentile1.47535
Maximum3.46
Range3.451
Interquartile range (IQR)0.3035

Descriptive statistics

Standard deviation0.65011278
Coefficient of variation (CV)1.5272336
Kurtosis13.031367
Mean0.42568
Median Absolute Deviation (MAD)0.0455
Skewness3.4841034
Sum42.568
Variance0.42264662
MonotonicityNot monotonic
2023-12-10T23:11:55.213983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 8
 
8.0%
0.337 5
 
5.0%
0.324 3
 
3.0%
0.331 3
 
3.0%
0.342 3
 
3.0%
0.338 3
 
3.0%
0.34 3
 
3.0%
0.344 2
 
2.0%
0.314 2
 
2.0%
0.339 2
 
2.0%
Other values (59) 66
66.0%
ValueCountFrequency (%)
0.009 1
 
1.0%
0.012 1
 
1.0%
0.019 1
 
1.0%
0.022 1
 
1.0%
0.023 1
 
1.0%
0.027 2
 
2.0%
0.028 1
 
1.0%
0.029 1
 
1.0%
0.03 8
8.0%
0.031 1
 
1.0%
ValueCountFrequency (%)
3.46 1
1.0%
3.452 1
1.0%
3.39 1
1.0%
2.426 1
1.0%
1.824 1
1.0%
1.457 1
1.0%
1.18 1
1.0%
1.031 1
1.0%
1.024 1
1.0%
0.949 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.22291
Minimum1.923
Maximum9.526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:11:55.511512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.923
5-th percentile1.9369
Q11.97625
median7.472
Q38.829
95-th percentile9.33195
Maximum9.526
Range7.603
Interquartile range (IQR)6.85275

Descriptive statistics

Standard deviation2.9441924
Coefficient of variation (CV)0.47312149
Kurtosis-1.3390752
Mean6.22291
Median Absolute Deviation (MAD)1.4705
Skewness-0.60028575
Sum622.291
Variance8.6682691
MonotonicityNot monotonic
2023-12-10T23:11:55.753655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.626 3
 
3.0%
1.963 3
 
3.0%
1.979 2
 
2.0%
9.294 2
 
2.0%
7.614 2
 
2.0%
7.472 2
 
2.0%
6.796 2
 
2.0%
9.303 2
 
2.0%
8.82 2
 
2.0%
9.276 2
 
2.0%
Other values (74) 78
78.0%
ValueCountFrequency (%)
1.923 1
1.0%
1.927 1
1.0%
1.93 1
1.0%
1.934 1
1.0%
1.935 1
1.0%
1.937 1
1.0%
1.942 1
1.0%
1.944 1
1.0%
1.948 1
1.0%
1.949 1
1.0%
ValueCountFrequency (%)
9.526 1
1.0%
9.452 1
1.0%
9.396 1
1.0%
9.359 1
1.0%
9.35 1
1.0%
9.331 1
1.0%
9.322 1
1.0%
9.303 2
2.0%
9.294 2
2.0%
9.285 2
2.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.043
Minimum66.2
Maximum93.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:11:56.065705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66.2
5-th percentile67.19
Q170.6
median73.85
Q375.025
95-th percentile76.815
Maximum93.4
Range27.2
Interquartile range (IQR)4.425

Descriptive statistics

Standard deviation4.4037921
Coefficient of variation (CV)0.060290406
Kurtosis5.4767252
Mean73.043
Median Absolute Deviation (MAD)2.25
Skewness1.5101887
Sum7304.3
Variance19.393385
MonotonicityNot monotonic
2023-12-10T23:11:56.376591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.1 4
 
4.0%
74.8 4
 
4.0%
74.6 4
 
4.0%
74.2 4
 
4.0%
75.1 3
 
3.0%
70.8 3
 
3.0%
74.5 3
 
3.0%
76.0 3
 
3.0%
71.3 2
 
2.0%
70.9 2
 
2.0%
Other values (54) 68
68.0%
ValueCountFrequency (%)
66.2 1
1.0%
66.3 1
1.0%
66.4 1
1.0%
66.8 1
1.0%
67.0 1
1.0%
67.2 2
2.0%
67.5 2
2.0%
67.7 2
2.0%
67.8 1
1.0%
67.9 2
2.0%
ValueCountFrequency (%)
93.4 1
 
1.0%
89.4 1
 
1.0%
85.1 1
 
1.0%
83.6 1
 
1.0%
77.1 1
 
1.0%
76.8 1
 
1.0%
76.7 1
 
1.0%
76.4 1
 
1.0%
76.3 1
 
1.0%
76.1 4
4.0%

Interactions

2023-12-10T23:11:50.106135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:42.594385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:43.952723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:45.050556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:46.133292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:47.501643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:49.091415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:50.275565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:42.808773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:44.115222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:45.207043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:46.293302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:47.792938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:49.255845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:50.417355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:42.984143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:44.250208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:45.349292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:46.431960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:48.205740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:49.363016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:50.574348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:43.233612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:44.399556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:45.507527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:46.554522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:48.520692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:49.514543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:50.715415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:43.437606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:44.553128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:45.644331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:46.672302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:48.655238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:49.640990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:50.865570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:43.634774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:44.698633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:45.818650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:46.810731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:48.800370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:49.808181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:51.026155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:43.792431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:44.844987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:45.983695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:46.981205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:48.948217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:49.953768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:11:56.622525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.2980.4070.5800.9790.871
일자/시간(t)0.0001.0000.0000.2490.1430.3160.3570.395
저수위(m)1.0000.0001.0000.1770.3400.5290.9900.720
강우량(mm)0.2980.2490.1771.0000.8950.0000.1620.457
유입량(ms)0.4070.1430.3400.8951.0000.8510.3010.843
방류량(ms)0.5800.3160.5290.0000.8511.0000.3470.634
저수량(백만m3)0.9790.3570.9900.1620.3010.3471.0000.756
저수율0.8710.3950.7200.4570.8430.6340.7561.000
2023-12-10T23:11:57.243127image/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)1.000-0.0390.1790.079-0.1180.1000.1840.000
저수위(m)-0.0391.0000.0620.5750.7240.918-0.3630.995
강우량(mm)0.1790.0621.0000.414-0.0100.0580.0920.132
유입량(ms)0.0790.5750.4141.0000.5850.6230.2060.185
방류량(ms)-0.1180.724-0.0100.5851.0000.797-0.2980.283
저수량(백만m3)0.1000.9180.0580.6230.7971.000-0.2730.894
저수율0.184-0.3630.0920.206-0.298-0.2731.0000.542
댐이름0.0000.9950.1320.1850.2830.8940.5421.000

Missing values

2023-12-10T23:11:51.245444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:11:51.779384image/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
0감포2019060137.830.00.00.0421.97975.1
1감포2019060237.820.00.010.031.97775.0
2감포2019060337.80.00.00.041.97474.9
3감포2019060437.780.00.00.031.9774.8
4감포2019060537.760.00.00.0381.96774.6
5감포2019060637.7412.50.00.0391.96374.5
6감포2019060737.7940.50.1240.0221.97274.8
7감포2019060837.780.00.0150.0351.9774.8
8감포2019060937.770.00.0070.0271.96974.7
9감포2019061037.740.00.00.0371.96374.5
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90달방20190601110.480.00.0810.2826.2671.6
91달방20190602110.430.00.030.286.23871.3
92달방20190603110.380.00.0410.296.21671.1
93달방20190604110.330.00.0710.326.19570.8
94달방20190605110.260.00.0260.3736.16570.5
95달방20190606110.2117.00.1160.3636.14470.2
96달방20190607111.12105.04.9010.2936.54274.8
97달방20190608111.420.01.7150.1646.67676.3
98달방20190609111.520.00.6490.1326.7276.8
99달방20190610111.584.00.4570.1466.74777.1