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 4 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 방류량(ms) and 1 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
강우량(mm) has 86 (86.0%) zerosZeros
유입량(ms) has 10 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:11:59.399536
Analysis finished2023-12-10 14:12:09.627334
Duration10.23 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
31 
광동
31 
구천
31 
달방
7 

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 31
31.0%
광동 31
31.0%
구천 31
31.0%
달방 7
 
7.0%

Length

2023-12-10T23:12:09.750342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:09.919657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
감포 31
31.0%
광동 31
31.0%
구천 31
31.0%
달방 7
 
7.0%

일자/시간(t)
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190515
Minimum20190501
Maximum20190531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:10.131175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190501
5-th percentile20190502
Q120190507
median20190515
Q320190523
95-th percentile20190530
Maximum20190531
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5636805 × 10-7
Kurtosis-1.2602055
Mean20190515
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0190515 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T23:12:10.354021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190501 4
 
4.0%
20190503 4
 
4.0%
20190504 4
 
4.0%
20190505 4
 
4.0%
20190506 4
 
4.0%
20190507 4
 
4.0%
20190502 4
 
4.0%
20190526 3
 
3.0%
20190522 3
 
3.0%
20190523 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20190501 4
4.0%
20190502 4
4.0%
20190503 4
4.0%
20190504 4
4.0%
20190505 4
4.0%
20190506 4
4.0%
20190507 4
4.0%
20190508 3
3.0%
20190509 3
3.0%
20190510 3
3.0%
ValueCountFrequency (%)
20190531 3
3.0%
20190530 3
3.0%
20190529 3
3.0%
20190528 3
3.0%
20190527 3
3.0%
20190526 3
3.0%
20190525 3
3.0%
20190524 3
3.0%
20190523 3
3.0%
20190522 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.7932
Minimum37.86
Maximum672.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:10.659366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.86
5-th percentile37.9295
Q138.05
median87.06
Q3671.7675
95-th percentile672.1005
Maximum672.23
Range634.37
Interquartile range (IQR)633.7175

Descriptive statistics

Standard deviation281.94729
Coefficient of variation (CV)1.1065731
Kurtosis-1.335665
Mean254.7932
Median Absolute Deviation (MAD)49.03
Skewness0.81256983
Sum25479.32
Variance79494.276
MonotonicityNot monotonic
2023-12-10T23:12:10.984485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.05 4
 
4.0%
38.06 4
 
4.0%
38.01 4
 
4.0%
38.03 3
 
3.0%
38.04 3
 
3.0%
87.27 3
 
3.0%
38.02 2
 
2.0%
86.77 2
 
2.0%
87.29 2
 
2.0%
672.05 2
 
2.0%
Other values (69) 71
71.0%
ValueCountFrequency (%)
37.86 1
1.0%
37.87 1
1.0%
37.89 1
1.0%
37.91 1
1.0%
37.92 1
1.0%
37.93 1
1.0%
37.95 1
1.0%
37.97 1
1.0%
37.98 1
1.0%
37.99 1
1.0%
ValueCountFrequency (%)
672.23 1
1.0%
672.19 1
1.0%
672.16 1
1.0%
672.14 1
1.0%
672.11 1
1.0%
672.1 1
1.0%
672.08 1
1.0%
672.07 1
1.0%
672.06 1
1.0%
672.05 2
2.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.875963
Minimum0
Maximum77
Zeros86
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:11.218977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.075
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.156293
Coefficient of variation (CV)5.4139092
Kurtosis46.099916
Mean1.875963
Median Absolute Deviation (MAD)0
Skewness6.7716652
Sum187.5963
Variance103.15029
MonotonicityNot monotonic
2023-12-10T23:12:11.435487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 86
86.0%
4.0 2
 
2.0%
6.5 1
 
1.0%
5.5 1
 
1.0%
3.0642 1
 
1.0%
0.4679 1
 
1.0%
0.5321 1
 
1.0%
2.5321 1
 
1.0%
66.0 1
 
1.0%
12.0 1
 
1.0%
Other values (4) 4
 
4.0%
ValueCountFrequency (%)
0.0 86
86.0%
0.4679 1
 
1.0%
0.5321 1
 
1.0%
1.0 1
 
1.0%
2.0 1
 
1.0%
2.5321 1
 
1.0%
3.0 1
 
1.0%
3.0642 1
 
1.0%
4.0 2
 
2.0%
5.5 1
 
1.0%
ValueCountFrequency (%)
77.0 1
1.0%
66.0 1
1.0%
12.0 1
1.0%
6.5 1
1.0%
5.5 1
1.0%
4.0 2
2.0%
3.0642 1
1.0%
3.0 1
1.0%
2.5321 1
1.0%
2.0 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26037
Minimum0
Maximum2.021
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:11.656830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median0.1085
Q30.32275
95-th percentile1.09345
Maximum2.021
Range2.021
Interquartile range (IQR)0.30275

Descriptive statistics

Standard deviation0.39657834
Coefficient of variation (CV)1.5231338
Kurtosis7.3955292
Mean0.26037
Median Absolute Deviation (MAD)0.099
Skewness2.6077749
Sum26.037
Variance0.15727438
MonotonicityNot monotonic
2023-12-10T23:12:11.919490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
0.01 3
 
3.0%
0.02 3
 
3.0%
0.102 2
 
2.0%
0.447 2
 
2.0%
0.066 2
 
2.0%
0.022 2
 
2.0%
0.144 2
 
2.0%
0.003 2
 
2.0%
0.009 2
 
2.0%
Other values (69) 70
70.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.001 1
 
1.0%
0.002 1
 
1.0%
0.003 2
 
2.0%
0.009 2
 
2.0%
0.01 3
 
3.0%
0.012 1
 
1.0%
0.014 1
 
1.0%
0.015 1
 
1.0%
0.016 1
 
1.0%
ValueCountFrequency (%)
2.021 1
1.0%
1.869 1
1.0%
1.655 1
1.0%
1.471 1
1.0%
1.216 1
1.0%
1.087 1
1.0%
0.97 1
1.0%
0.787 1
1.0%
0.772 1
1.0%
0.652 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31455
Minimum0.009
Maximum2.452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:12.150157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.0159
Q10.03
median0.321
Q30.34125
95-th percentile0.90395
Maximum2.452
Range2.443
Interquartile range (IQR)0.31125

Descriptive statistics

Standard deviation0.37689728
Coefficient of variation (CV)1.198211
Kurtosis13.498507
Mean0.31455
Median Absolute Deviation (MAD)0.1535
Skewness3.2060231
Sum31.455
Variance0.14205156
MonotonicityNot monotonic
2023-12-10T23:12:12.376648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.024 4
 
4.0%
0.029 3
 
3.0%
0.328 3
 
3.0%
0.327 3
 
3.0%
0.03 3
 
3.0%
0.336 2
 
2.0%
0.343 2
 
2.0%
0.332 2
 
2.0%
0.325 2
 
2.0%
0.334 2
 
2.0%
Other values (65) 74
74.0%
ValueCountFrequency (%)
0.009 1
1.0%
0.01 2
2.0%
0.012 1
1.0%
0.014 1
1.0%
0.016 1
1.0%
0.017 1
1.0%
0.018 1
1.0%
0.019 2
2.0%
0.02 2
2.0%
0.022 2
2.0%
ValueCountFrequency (%)
2.452 1
1.0%
1.901 1
1.0%
1.538 1
1.0%
1.301 1
1.0%
1.093 1
1.0%
0.894 1
1.0%
0.759 1
1.0%
0.699 1
1.0%
0.647 1
1.0%
0.634 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.99299
Minimum1.984
Maximum9.405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:12.647214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.984
5-th percentile1.9969
Q12.018
median6.664
Q38.98075
95-th percentile9.28545
Maximum9.405
Range7.421
Interquartile range (IQR)6.96275

Descriptive statistics

Standard deviation2.8754604
Coefficient of variation (CV)0.47980397
Kurtosis-1.3805674
Mean5.99299
Median Absolute Deviation (MAD)2.4415
Skewness-0.42102672
Sum599.299
Variance8.2682726
MonotonicityNot monotonic
2023-12-10T23:12:12.935856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.018 4
 
4.0%
2.02 4
 
4.0%
2.011 4
 
4.0%
2.015 3
 
3.0%
2.017 3
 
3.0%
6.859 3
 
3.0%
2.013 2
 
2.0%
6.664 2
 
2.0%
6.867 2
 
2.0%
9.239 2
 
2.0%
Other values (69) 71
71.0%
ValueCountFrequency (%)
1.984 1
1.0%
1.986 1
1.0%
1.99 1
1.0%
1.993 1
1.0%
1.995 1
1.0%
1.997 1
1.0%
2.0 1
1.0%
2.004 1
1.0%
2.006 1
1.0%
2.008 1
1.0%
ValueCountFrequency (%)
9.405 1
1.0%
9.368 1
1.0%
9.34 1
1.0%
9.322 1
1.0%
9.294 1
1.0%
9.285 1
1.0%
9.266 1
1.0%
9.257 1
1.0%
9.248 1
1.0%
9.239 2
2.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.194
Minimum65.6
Maximum76.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:13.204372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65.6
5-th percentile66.19
Q168.075
median70.2
Q375.825
95-th percentile76.6
Maximum76.6
Range11
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation3.8655385
Coefficient of variation (CV)0.054295847
Kurtosis-1.5062997
Mean71.194
Median Absolute Deviation (MAD)3.3
Skewness0.25376941
Sum7119.4
Variance14.942388
MonotonicityNot monotonic
2023-12-10T23:12:13.444041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.6 8
 
8.0%
76.4 5
 
5.0%
76.3 4
 
4.0%
66.2 3
 
3.0%
68.6 3
 
3.0%
76.5 3
 
3.0%
68.5 3
 
3.0%
70.4 3
 
3.0%
66.4 3
 
3.0%
68.4 3
 
3.0%
Other values (52) 62
62.0%
ValueCountFrequency (%)
65.6 1
 
1.0%
65.7 1
 
1.0%
65.9 2
2.0%
66.0 1
 
1.0%
66.2 3
3.0%
66.4 3
3.0%
66.5 2
2.0%
66.7 1
 
1.0%
66.9 1
 
1.0%
67.0 1
 
1.0%
ValueCountFrequency (%)
76.6 8
8.0%
76.5 3
 
3.0%
76.4 5
5.0%
76.3 4
4.0%
76.2 2
 
2.0%
76.1 1
 
1.0%
76.0 1
 
1.0%
75.9 1
 
1.0%
75.8 1
 
1.0%
75.7 1
 
1.0%

Interactions

2023-12-10T23:12:07.520518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:00.043195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:01.318182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:02.527231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:03.986023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:05.263532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:06.448574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:07.644335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:00.221975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:01.494418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:02.683259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:04.209103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:05.417981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:06.604707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:07.773441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:00.389753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:01.681495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:02.854033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:04.395686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:05.585828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:06.751645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:07.939235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:00.655061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:01.858531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:03.097701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:04.598703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:05.755328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:06.921489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:08.118942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:00.845186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:02.025282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:03.287193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:04.779441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:05.946467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:07.103012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:08.407000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:00.977751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:02.179363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:03.453251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:04.952466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:06.157210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:07.250841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:08.585547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:01.148654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:02.361139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:03.718017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:05.118194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:06.320671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:07.389961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:12:13.615700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.4300.7560.9930.982
일자/시간(t)0.0001.0000.1390.1620.0000.2560.0000.589
저수위(m)1.0000.1391.0000.0000.4450.7390.7840.940
강우량(mm)0.0000.1620.0001.0000.8450.0000.0000.000
유입량(ms)0.4300.0000.4450.8451.0000.9010.3640.777
방류량(ms)0.7560.2560.7390.0000.9011.0000.7140.804
저수량(백만m3)0.9930.0000.7840.0000.3640.7141.0000.930
저수율0.9820.5890.9400.0000.7770.8040.9301.000
2023-12-10T23:12:13.824161image/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)1.000-0.2630.246-0.354-0.051-0.162-0.3000.000
저수위(m)-0.2631.000-0.0630.7380.8600.951-0.3450.995
강우량(mm)0.246-0.0631.0000.070-0.161-0.055-0.0820.000
유입량(ms)-0.3540.7380.0701.0000.6290.704-0.3640.260
방류량(ms)-0.0510.860-0.1610.6291.0000.914-0.5590.586
저수량(백만m3)-0.1620.951-0.0550.7040.9141.000-0.4700.887
저수율-0.300-0.345-0.082-0.364-0.559-0.4701.0000.904
댐이름0.0000.9950.0000.2600.5860.8870.9041.000

Missing values

2023-12-10T23:12:08.792564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:12:09.053315image/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
0감포2019050138.020.00.1290.0262.01376.4
1감포2019050238.040.00.0710.032.01776.5
2감포2019050338.050.00.050.0292.01876.6
3감포2019050438.060.00.0450.0242.0276.6
4감포2019050538.060.00.0420.0422.0276.6
5감포2019050638.060.00.0220.0222.0276.6
6감포2019050738.060.00.0270.0272.0276.6
7감포2019050838.050.00.00.0172.01876.6
8감포2019050938.050.00.0190.0192.01876.6
9감포2019051038.050.00.0240.0242.01876.6
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구천2019052987.250.00.6520.3356.85168.4
91구천2019053087.270.00.420.3296.85968.5
92구천2019053187.273.00.3150.3156.85968.5
93달방20190501110.770.00.4570.1526.38673.0
94달방20190502110.830.00.4470.1416.41373.3
95달방20190503110.860.00.30.1476.42673.5
96달방20190504110.890.00.2970.1436.43973.6
97달방20190505110.920.00.280.1266.45273.8
98달방20190506110.931.00.1810.136.45773.8
99달방20190507110.970.00.3340.1286.47574.0