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 4 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 4 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 4 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
강우량(mm) has 94 (94.0%) zerosZeros
유입량(ms) has 16 (16.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:13:01.730131
Analysis finished2023-12-10 14:13:11.604548
Duration9.87 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

2023-12-10T23:13:11.712621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:11.861654image/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%
Mean20190115
Minimum20190101
Maximum20190131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:13:12.050945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190101
5-th percentile20190102
Q120190107
median20190115
Q320190123
95-th percentile20190130
Maximum20190131
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5637709 × 10-7
Kurtosis-1.2602055
Mean20190115
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0190115 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T23:13:12.284266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190101 4
 
4.0%
20190103 4
 
4.0%
20190104 4
 
4.0%
20190105 4
 
4.0%
20190106 4
 
4.0%
20190107 4
 
4.0%
20190102 4
 
4.0%
20190126 3
 
3.0%
20190122 3
 
3.0%
20190123 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20190101 4
4.0%
20190102 4
4.0%
20190103 4
4.0%
20190104 4
4.0%
20190105 4
4.0%
20190106 4
4.0%
20190107 4
4.0%
20190108 3
3.0%
20190109 3
3.0%
20190110 3
3.0%
ValueCountFrequency (%)
20190131 3
3.0%
20190130 3
3.0%
20190129 3
3.0%
20190128 3
3.0%
20190127 3
3.0%
20190126 3
3.0%
20190125 3
3.0%
20190124 3
3.0%
20190123 3
3.0%
20190122 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255.8359
Minimum38.91
Maximum671.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:13:12.491292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38.91
5-th percentile38.979
Q139.265
median89.635
Q3671.245
95-th percentile671.691
Maximum671.79
Range632.88
Interquartile range (IQR)631.98

Descriptive statistics

Standard deviation280.94888
Coefficient of variation (CV)1.0981605
Kurtosis-1.3357719
Mean255.8359
Median Absolute Deviation (MAD)50.45
Skewness0.81172278
Sum25583.59
Variance78932.273
MonotonicityNot monotonic
2023-12-10T23:13:12.710110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.91 2
 
2.0%
671.71 2
 
2.0%
39.2 2
 
2.0%
39.34 1
 
1.0%
90.0 1
 
1.0%
89.61 1
 
1.0%
89.66 1
 
1.0%
89.7 1
 
1.0%
89.75 1
 
1.0%
89.8 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
38.91 2
2.0%
38.93 1
1.0%
38.94 1
1.0%
38.96 1
1.0%
38.98 1
1.0%
39.0 1
1.0%
39.02 1
1.0%
39.03 1
1.0%
39.05 1
1.0%
39.06 1
1.0%
ValueCountFrequency (%)
671.79 1
1.0%
671.77 1
1.0%
671.74 1
1.0%
671.71 2
2.0%
671.69 1
1.0%
671.66 1
1.0%
671.64 1
1.0%
671.61 1
1.0%
671.59 1
1.0%
671.57 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.369679
Minimum0
Maximum15
Zeros94
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:13:12.887314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.598395
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9139949
Coefficient of variation (CV)5.177451
Kurtosis41.282884
Mean0.369679
Median Absolute Deviation (MAD)0
Skewness6.2120368
Sum36.9679
Variance3.6633765
MonotonicityNot monotonic
2023-12-10T23:13:13.068974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 94
94.0%
10.0 1
 
1.0%
0.5 1
 
1.0%
6.0 1
 
1.0%
2.4679 1
 
1.0%
3.0 1
 
1.0%
15.0 1
 
1.0%
ValueCountFrequency (%)
0.0 94
94.0%
0.5 1
 
1.0%
2.4679 1
 
1.0%
3.0 1
 
1.0%
6.0 1
 
1.0%
10.0 1
 
1.0%
15.0 1
 
1.0%
ValueCountFrequency (%)
15.0 1
 
1.0%
10.0 1
 
1.0%
6.0 1
 
1.0%
3.0 1
 
1.0%
2.4679 1
 
1.0%
0.5 1
 
1.0%
0.0 94
94.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07842
Minimum0
Maximum0.371
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:13:13.331781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01775
median0.065
Q30.13725
95-th percentile0.1964
Maximum0.371
Range0.371
Interquartile range (IQR)0.1195

Descriptive statistics

Standard deviation0.07436413
Coefficient of variation (CV)0.94828016
Kurtosis1.4606081
Mean0.07842
Median Absolute Deviation (MAD)0.0485
Skewness1.1670725
Sum7.842
Variance0.0055300238
MonotonicityNot monotonic
2023-12-10T23:13:13.592771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
0.018 5
 
5.0%
0.068 4
 
4.0%
0.015 3
 
3.0%
0.08 3
 
3.0%
0.066 3
 
3.0%
0.02 3
 
3.0%
0.065 3
 
3.0%
0.161 2
 
2.0%
0.075 2
 
2.0%
Other values (50) 56
56.0%
ValueCountFrequency (%)
0.0 16
16.0%
0.007 1
 
1.0%
0.012 2
 
2.0%
0.014 1
 
1.0%
0.015 3
 
3.0%
0.016 1
 
1.0%
0.017 1
 
1.0%
0.018 5
 
5.0%
0.019 1
 
1.0%
0.02 3
 
3.0%
ValueCountFrequency (%)
0.371 1
1.0%
0.272 1
1.0%
0.267 1
1.0%
0.206 1
1.0%
0.204 1
1.0%
0.196 1
1.0%
0.195 1
1.0%
0.193 1
1.0%
0.179 2
2.0%
0.178 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2444
Minimum0.034
Maximum0.416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:13:13.868416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.034
5-th percentile0.037
Q10.042
median0.31
Q30.371
95-th percentile0.39915
Maximum0.416
Range0.382
Interquartile range (IQR)0.329

Descriptive statistics

Standard deviation0.14400463
Coefficient of variation (CV)0.58921698
Kurtosis-1.4162489
Mean0.2444
Median Absolute Deviation (MAD)0.069
Skewness-0.5861612
Sum24.44
Variance0.020737333
MonotonicityNot monotonic
2023-12-10T23:13:14.095514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 11
 
11.0%
0.039 5
 
5.0%
0.371 4
 
4.0%
0.037 4
 
4.0%
0.304 4
 
4.0%
0.042 4
 
4.0%
0.379 3
 
3.0%
0.313 3
 
3.0%
0.319 3
 
3.0%
0.387 2
 
2.0%
Other values (51) 57
57.0%
ValueCountFrequency (%)
0.034 1
 
1.0%
0.036 1
 
1.0%
0.037 4
 
4.0%
0.038 1
 
1.0%
0.039 5
5.0%
0.04 11
11.0%
0.041 1
 
1.0%
0.042 4
 
4.0%
0.043 2
 
2.0%
0.052 1
 
1.0%
ValueCountFrequency (%)
0.416 1
1.0%
0.409 1
1.0%
0.405 1
1.0%
0.404 1
1.0%
0.402 1
1.0%
0.399 1
1.0%
0.396 1
1.0%
0.388 1
1.0%
0.387 2
2.0%
0.386 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.266742
Minimum2.176
Maximum9.001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:13:14.336103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.176
5-th percentile2.18885
Q12.244
median7.6745
Q38.5135
95-th percentile8.9119
Maximum9.001
Range6.825
Interquartile range (IQR)6.2695

Descriptive statistics

Standard deviation2.7798309
Coefficient of variation (CV)0.4435847
Kurtosis-1.361951
Mean6.266742
Median Absolute Deviation (MAD)0.963
Skewness-0.71776627
Sum626.6742
Variance7.7274597
MonotonicityNot monotonic
2023-12-10T23:13:14.564473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.176 2
 
2.0%
8.929 2
 
2.0%
2.231 2
 
2.0%
2.258 1
 
1.0%
7.9843 1
 
1.0%
7.817 1
 
1.0%
7.838 1
 
1.0%
7.855 1
 
1.0%
7.877 1
 
1.0%
7.898 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
2.176 2
2.0%
2.18 1
1.0%
2.182 1
1.0%
2.186 1
1.0%
2.189 1
1.0%
2.193 1
1.0%
2.197 1
1.0%
2.199 1
1.0%
2.203 1
1.0%
2.204 1
1.0%
ValueCountFrequency (%)
9.001 1
1.0%
8.983 1
1.0%
8.956 1
1.0%
8.929 2
2.0%
8.911 1
1.0%
8.883 1
1.0%
8.865 1
1.0%
8.838 1
1.0%
8.82 1
1.0%
8.803 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.087
Minimum63.9
Maximum85.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:13:14.774067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63.9
5-th percentile64.785
Q167.85
median77.95
Q383.125
95-th percentile85.2
Maximum85.7
Range21.8
Interquartile range (IQR)15.275

Descriptive statistics

Standard deviation7.2912104
Coefficient of variation (CV)0.095827281
Kurtosis-1.338086
Mean76.087
Median Absolute Deviation (MAD)5.85
Skewness-0.36467218
Sum7608.7
Variance53.161748
MonotonicityNot monotonic
2023-12-10T23:13:15.290953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.6 3
 
3.0%
65.3 2
 
2.0%
78.4 2
 
2.0%
68.0 2
 
2.0%
83.6 2
 
2.0%
84.1 2
 
2.0%
82.6 2
 
2.0%
78.5 2
 
2.0%
78.2 2
 
2.0%
85.2 2
 
2.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
63.9 1
1.0%
64.0 1
1.0%
64.3 1
1.0%
64.4 1
1.0%
64.5 1
1.0%
64.8 1
1.0%
64.9 1
1.0%
65.1 1
1.0%
65.3 2
2.0%
65.5 1
1.0%
ValueCountFrequency (%)
85.7 1
 
1.0%
85.5 1
 
1.0%
85.4 1
 
1.0%
85.3 1
 
1.0%
85.2 2
2.0%
85.0 1
 
1.0%
84.9 1
 
1.0%
84.8 1
 
1.0%
84.6 3
3.0%
84.5 1
 
1.0%

Interactions

2023-12-10T23:13:10.333915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:02.080279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:03.140018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:04.448587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:05.549940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:07.806572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:09.107411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:10.475660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:02.206221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:03.324934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:04.616753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:05.789705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:07.965423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:09.326037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:10.627975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:02.336366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:03.560906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:04.757302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:06.001956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:08.120360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:09.503102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:10.767929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:02.473805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:03.766142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:04.914851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:06.911432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:08.396071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:09.681706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:10.925448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:02.630564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:03.964260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:05.079435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:07.137059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:08.570055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:09.898875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:11.061031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:02.796562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:04.160510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:05.263833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:07.392553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:08.738815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:10.050120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:11.198925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:02.975097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:04.322320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:05.409410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:07.645265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:08.934670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:10.203449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:13:15.427241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.8981.0001.0000.919
일자/시간(t)0.0001.0000.1390.1930.0000.0000.4050.575
저수위(m)1.0000.1391.0000.0000.6811.0001.0000.973
강우량(mm)0.0000.1930.0001.0000.0000.2830.0000.267
유입량(ms)0.8980.0000.6810.0001.0000.6920.6880.613
방류량(ms)1.0000.0001.0000.2830.6921.0000.9240.798
저수량(백만m3)1.0000.4051.0000.0000.6880.9241.0000.919
저수율0.9190.5750.9730.2670.6130.7980.9191.000
2023-12-10T23:13:15.608701image/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)1.000-0.3310.130-0.0900.068-0.230-0.3240.000
저수위(m)-0.3311.000-0.1440.7650.8550.951-0.7800.995
강우량(mm)0.130-0.1441.0000.010-0.130-0.1240.0660.000
유입량(ms)-0.0900.7650.0101.0000.6960.709-0.7170.582
방류량(ms)0.0680.855-0.1300.6961.0000.905-0.8980.984
저수량(백만m3)-0.2300.951-0.1240.7090.9051.000-0.7930.995
저수율-0.324-0.7800.066-0.717-0.898-0.7931.0000.837
댐이름0.0000.9950.0000.5820.9840.9950.8371.000

Missing values

2023-12-10T23:13:11.361829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:13:11.517696image/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
0감포2019010139.340.00.0070.0522.25885.7
1감포2019010239.320.00.00.0422.25485.5
2감포2019010339.310.00.0140.0362.25285.4
3감포2019010439.290.00.00.042.24885.3
4감포2019010539.280.00.0150.0372.24685.2
5감포2019010639.270.00.0150.0372.24585.2
6감포2019010739.250.00.00.0392.24185.0
7감포2019010839.230.00.00.0412.23784.9
8감포2019010939.220.00.0120.0342.23584.8
9감포2019011039.20.00.00.0372.23184.6
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구천2019012988.780.00.0680.3087.46874.6
91구천2019013088.730.00.080.327.44774.4
92구천2019013188.715.00.1490.2937.43574.2
93달방20190101111.850.00.0830.1886.86978.5
94달방20190102111.840.00.1410.1936.86478.5
95달방20190103111.830.00.1690.2216.8678.4
96달방20190104111.790.00.0510.266.84278.2
97달방20190105111.780.00.2060.2586.83778.2
98달방20190106111.760.00.1480.2526.82878.1
99달방20190107111.730.00.1040.266.81577.9